The deployment of large language models' (LLMs) inference at the edge can facilitate prompt service responsiveness while protecting user privacy. However, it is critically challenged by the resource constraints of a single edge node. Distributed inference has emerged to aggregate and leverage computational resources across multiple devices. Yet, existing methods typically require strict synchronization, which is often infeasible due to the unreliable network conditions. In this paper, we propose HALO , a novel framework that can boost the distributed LLM inference in lossy edge network. The core idea is to enable a relaxed yet effective synchronization by strategically allocating less critical neuron groups to unstable devices, thus avoiding the excessive waiting time incurred by delayed packets. HALO introduces three key mechanisms: (1) a semantic-aware predictor to assess the significance of neuron groups prior to activation. (2) a parallel execution scheme of neuron group loading during the model inference. (3) a load-balancing scheduler that efficiently orchestrates multiple devices with heterogeneous resources. Experimental results from a Raspberry Pi cluster demonstrate that HALO achieves a 3.41x end-to-end speedup for LLaMA-series LLMs under unreliable network conditions. It maintains performance comparable to optimal conditions and significantly outperforms the state-of-the-art in various scenarios.

In this paper, we propose a novel mobility-aware resource provisioning scheme for edge-assisted extended reality (XR) services. The goal is to minimize resource consumption while satisfying user quality of experience (QoE) requirement, which is measured by the weighted sum of visual quality, quality variation, and round-trip interaction latency. Specifically, we present a mobility model to capture both user spatial movements and XR content interaction features. Since user viewing distance and interaction time are key model parameters that affect the spatiotemporal service demand for XR content rendering and delivery at the edge, we estimate user-specific model parameters and adopt a sample average approximation method to model the relationship between user QoE and the consumption of both communication and edge computing resources. We design a coordinate descent algorithm to make resource provisioning decisions, where a deep neural network provides a valuable initial point to accelerate convergence. Simulation results demonstrate that our proposed scheme is more efficient to utilize network resources in comparison with benchmark schemes while satisfying user QoE requirements.

As semantic communication (SemCom) attracts growing attention as a novel communication paradigm, ensuring the security of transmitted semantic information over open wireless channels has become a critical issue. However, traditional encryption methods often introduce significant additional communication overhead to maintain stability, and conventional learning-based secure SemCom methods typically rely on a channel capacity advantage for the legitimate receiver, which is challenging to guarantee in real-world scenarios. In this paper, we propose a coding-enhanced jamming method that eliminates the need to transmit a secret key by utilizing shared knowledge–potentially part of the training set of the SemCom system–between the legitimate receiver and the transmitter. Specifically, we leverage the shared private knowledge base to generate a set of private digital codebooks in advance using neural network (NN)-based encoders. For each transmission, we encode the transmitted data into digital sequence Y1and associate Y1with a sequence randomly picked from the private codebook, denoted as Y2, through superposition coding. Here, Y1serves as the outer code and Y2as the inner code. By optimizing the power allocation between the inner and outer codes, the legitimate receiver can reconstruct the transmitted data using successive decoding with the index of Y2shared, while the eavesdropper’s decoding performance is severely degraded, potentially to the point of random guessing. Experimental results demonstrate that our method achieves security comparable to state-of-the-art approaches while significantly improving the reconstruction performance of the legitimate receiver by more than 1 dB across varying channel signal-to-noise ratios (SNRs) and compression ratios.

In this paper, we propose a novelpolarforming antenna (PA)to achieve cost-effective wireless sensing and communication. Specifically, the PA can enable polarforming to adaptively control the antenna’s polarization electrically as well as tune its position/rotation mechanically, so as to effectively exploit polarization and spatial diversity to reconfigure wireless channels for improving sensing and communication performance. To analyze the performance gain of PA, we study a PA-enhanced integrated sensing and communication (ISAC) system that utilizes user location sensing to facilitate communication between a PA-equipped base station (BS) and PA-equipped users, by focusing on a new practical channel setup where the locations of users are nearly time-invariant but their orientations may change frequently (e.g., mobile phones rotated by spectators seated in a stadium while taking live photos). First, we model the PA channel in terms of transceiver antenna polarforming vectors and antenna positions/rotations. We then propose a two-timescale ISAC protocol, where in the slow timescale, user localization is first performed, followed by the optimization of the BS antennas’ positions and rotations based on the sensed user locations; subsequently, in the fast timescale, transceiver polarforming is adapted to cater to the instantaneous orientation of user devices in three-dimensional (3D) space, with the optimized BS antennas’ positions and rotations. We propose a new polarforming-based user localization method that uses a structured time-domain pattern of pilot-polarforming vectors to extract the common stable components in the PA channel across different polarizations based on the parallel factor (PARAFAC) tensor model. Moreover, we maximize the achievable average sum-rate of users by jointly optimizing the fast-timescale transceiver polarforming, including phase shifts and amplitude variations, along with the slow-timescale antenna rotations and positions at the BS. Simulation results validate the effectiveness of polarforming-based localization algorithm and demonstrate the performance advantages of polarforming, antenna placement, and their joint design in comparison with various benchmarks without polarforming or antenna position/rotation adaptation.

In this paper, we propose a novel physics-informed generative learning approach, named RadioDiff-k2, for accurate and efficient multipath-aware radio map (RM) construction. As future wireless communication evolves towards environment-aware paradigms, the accurate construction of RMs becomes crucial yet highly challenging. Conventional electromagnetic (EM)-based methods, such as full-wave solvers and ray-tracing approaches, exhibit substantial computational overhead and limited adaptability to dynamic scenarios. Although existing neural network (NN) approaches have efficient inferencing speed, they lack sufficient consideration of the underlying physics of EM wave propagation, limiting their effectiveness in accurately modeling critical EM singularities induced by complex multipath environments. To address these fundamental limitations, we propose a novel physics-inspired RM construction method guided explicitly by the Helmholtz equation, which inherently governs EM wave propagation. Specifically, based on the analysis of partial differential equations (PDEs), we theoretically establish a direct correspondence between EM singularities, which correspond to the critical spatial features influencing wireless propagation, and regions defined by negative wave numbers in the Helmholtz equation. We then design an innovative dual diffusion model (DM)-based large artificial intelligence framework comprising one DM dedicated to accurately inferring EM singularities and another DM responsible for reconstructing the complete RM using these singularities along with environmental contextual information. Experimental results demonstrate that the proposed RadioDiff-k2framework achieves state-of-the-art (SOTA) performance in both image-level RM construction and localization tasks, while maintaining inference latency within a few hundred milliseconds. Code is available at https://github.com/UNIC-Lab/RadioDiff-k.

This paper proposes a novel flexible coupler antenna array that incorporates additional degrees of freedom (DoF) in radiation pattern reconfiguration to achieve strong mechanical beamforming gains and enhanced communication coverage with low hardware cost. Particularly, passive couplers move around a fixed active antenna so that the induced currents on the passive elements can be reshaped to achieve radiation pattern reconfiguration. A new form of mechanical beamforming can be obtained by moving only the passive couplers while keeping the active antenna stationary. In addition, the flexible coupler antenna can slide along a rail toward users, thereby enhancing communication coverage. To fully exploit the potential of the flexible coupler array, we formulate a two-timescale sum-rate maximization problem with statistical channel state information (CSI). The active antenna position is optimized based on scattering cluster-core statistics in the slow timescale, while mechanical beamforming is optimized based on multipath channel statistics in the fast timescale, subject to movement and energy constraints. To address the coupling between timescales and the high cost of extensive channel sampling, we develop a digital agent framework that leverages an electromagnetic (EM) map to generate statistical channel information for different user and antenna positions. Then, a deep neural network is trained to learn a slow-fast performance (SFP) surrogate, which is fine-tuned with a small number of real measurements and then applied for position optimization at the slow timescale using projected gradient ascent. Mechanical beamforming at the fast timescale is obtained by selecting per-antenna radiation patterns from a predefined dictionary via a convex relaxation. Simulation results demonstrate that the proposed flexible coupler array significantly improves system throughput, and the digital agent-assisted algorithm achieves satisfactory performance with greatly reduced online computational complexity.

In this paper, we propose PIRS, a two-server offline/online private information retrieval scheme with small client storage. In PIRS, a client first engages in an offline phase to preprocess a database replicated on two servers to generate query-independent hints. Utilizing the pre-computed hints, the client then securely retrieves any record from the database without exposing its index during an online phase, with the server-side computational complexity being sublinear for high efficiency. Compared to state-of-the-art schemes, PIRS distinguishes itself by enabling the client to outsource the hints to the servers instead of storing them, dramatically reducing the local storage requirement from GB/MB to MB/KB, given that the size of the hints is directly proportional to the database volume. Specifically, the client employs secret sharing to achieve secure hint outsourcing and only fetches the relevant hint for each online PIR query. In such an outsourcing environment, we introduce a new technique named oblivious switching to obfuscate repeated hint/record accesses, and carefully tailor online PIR queries to guarantee sublinear computational complexity. Furthermore, we propose a secure and efficient method that delegates the task of locating appropriate hints for particular PIR queries to the servers, thus avoiding costly computations or extra data storage on the client side. Finally, we conduct a comprehensive security analysis to demonstrate PIRS's security, and develop a proof-of-concept prototype to show the practicality of PIRS in terms of computational, communication, and storage overheads.

Uncrewed aerial vehicle (UAV) has emerged as a promising solution for automating railway inspections due to its high mobility, flexible deployment, and reduced labor cost. In this paper, we investigate UAV-assisted railway inspections, which include object recognition, humidity monitoring, and critical infrastructure modeling, each with distinct data volumes and computational requirements. Particularly, we introduce a UAV-assisted railway inspection framework. Different types of sensors are divided into several clusters. The UAV departs from the hive, flies over each cluster to collect their computational requirements, and performs task offloading before returning to the hive. This process is formulated as a joint optimization problem of trajectory planning and task offloading to minimize the weighted sum of latency and energy consumption. Considering the constrained computing and storage capabilities of UAVs, it is crucial but challenging to develop a lightweight yet high-performing solution for the multi-objective optimization problems. As such, a novelArtificial General Intelligence (AGI)-orientedTransformer (AoT) algorithm is proposed to solve the optimization problem. It uses an encoder-only architecture to process either sensor location or task features, and then directs the encoded outputs to different output heads to make decisions on UAV trajectory and task offloading. Simulation results demonstrate that the proposed AoT algorithm outperforms benchmark algorithms in terms of trajectory length and average offloading cost.

Edge-assisted Hierarchical Federated Learning (EHFL) accelerates global model training across mobile devices by hierarchically aggregating models. However, EHFL encounters critical challenges such as privacy risks for local and edge-level models, vulnerability to collusive Byzantine attacks, and issues with model diversity and heterogeneity due to Non-Independent and Identically Distributed (Non-IID) data. In this paper, we propose PPBR, a novel hybrid scheme that subtly integrates Condensed Local Differential Privacy (CLDP) and Packed Linearly Homomorphic Encryption (PLHE) to achieve strong privacy protection and resilience against various Byzantine attacks in Non-IID data scenarios. Specifically, PPBR clusters the sign statistics of local models and clips the norms of edge-level momenta to filter anomalous models and mitigate Byzantine faults while retaining diverse models coming from Non-IID data. To enhance privacy protection with acceptable accuracy loss, the sign tuples of local models are perturbed with CLDP guarantees, and the momenta of edge-level models are encrypted under PLHE. Meanwhile, PPBR enhances privacy in single-edge-server and single-cloud-server aggregations by using random perturbations, secret sharing, and PLHE. In addition to safeguarding privacy with accommodating abrupt dropouts of mobile devices and edge servers, the aggregations effectively mitigate the adverse effects of Non-IID data under advanced Byzantine attacks. Theoretical analysis and comprehensive experiments validate PPBR's strong privacy guarantees and resilience to various Byzantine attacks under Non-IID data.

In this paper, we propose a digital agent (DA)-assisted resource management scheme for enhanced user quality of experience (QoE) in integrated sensing and communication (ISAC) networks. Particularly, user QoE is a comprehensive metric that integrates quality of service (QoS), user behavioral dynamics, and environmental complexity. The novel DA module includes a user status prediction model, a QoS factor selection model, and a QoE fitting model, which analyzes historical user status data to construct and update user-specific QoE models. Users are clustered into different groups based on their QoE models. A Cramér-Rao bound (CRB) model is utilized to quantify the impact of allocated communication resources on sensing accuracy. A joint optimization problem of communication and computing resource management is formulated to maximize long-term user QoE while satisfying CRB and resource constraints. A two-layer data-model-driven algorithm is developed to solve the formulated problem, where the top layer utilizes an advanced deep reinforcement learning algorithm to make group-level decisions, and the bottom layer uses convex optimization techniques to make user-level decisions. Simulation results based on a real-world dataset demonstrate that the proposed DA-assisted resource management scheme outperforms benchmark schemes in terms of user QoE.

In this paper, we investigate controllable mobile text-to-image generation at scale, considering diverse user preferences. In particular, we observe that, by incorporating visual conditions (e.g.,Canny maps and depth maps) as supplementary inputs alongside text prompts, fine-grained and controllable image generation could be achieved. To this end, we propose a system design for distributed and controllable mobile text-to-image generation by leveraging edge computing. This system can satisfy diverse user-specified quality preferences at reduced transmission cost through effective cooperation of mobile and edge computing. In particular, the proposed system consists of aVisual Condition Engineeringmodule and aDistributed Denoising Controlmodule. Since extensive profiling reveals that different visual conditions affect both generation quality and sensitivity to image encoding parameters, the first module selects the optimal configuration of user-specific visual condition on mobile devices. Key to this module is a Pareto Frontier-based model which subtly balances user-preferred generation quality and transmission efficiency. The second module enables collaborative generation by adaptively distributing denoising tasks between mobile devices and the edge server, according to their available computing resources. At the core of this module is an efficient deep reinforcement learning algorithm designed to optimize the dynamic distribution of denoising tasks. By integrating the deep diffusion model, this algorithm achieves superior action space exploration capabilities while maintaining fast convergence and reliable execution, thereby facilitating enhanced adaptability under variable computing resource scenarios. Extensive experimental results reveal that, the designed system can achieve a reduction in transmission cost by over 90% and enhance user satisfaction by up to 18%, with consistent performance across various diffusion models under diverse resource constraints.

In this paper, we investigate secure and efficient computation offloading in mobile edge computing (MEC) systems. Particularly, computation tasks are dynamically partitioned into multiple sub-tasks for parallel processing on both local devices and edge servers to reduce service latency. In addition, the friendly jamming technique is applied to degrade the interception capabilities of eavesdroppers to protect data secrecy. Our objective is to maximize the number of tasks completed before their respective deadlines and, at the same time, to minimize energy consumption and service latency with security guarantee. To simultaneously handle heterogeneous offloading decisions, we propose an omni-deep deterministic policy gradient (Omni-DDPG) approach that integrates a variational autoencoder for discrete jammer selection, an Ornstein-Uhlenbeck process for continuous computing power allocation, and a Dirichlet distribution for constrained continuous task partitioning. The proposed approach has a low complexity growing linearly with the system size, maintains light memory usage, and enables real-time decisions without using complicated optimization solvers. Extensive simulation results demonstrate our proposed approach can achieve better performance in terms of the number of completed tasks before expiration, energy consumption, and service latency, while satisfying secrecy requirements, compared with the benchmarks such as DDPG, deep Q-network, and greedy algorithms.

Although Federated Learning (FL) can enable advanced autonomous driving via leveraging massive distributed data in vehicular networks, vehicle mobility causes frequent connection interruptions, hindering the FL process. In this paper, we propose a novelMobility-AwareVehicularFL(MAVFL) scheme, which can accelerate the training process in dynamic vehicular networks via adaptive vehicle selection and sparse training. Specifically, the MAVFL dynamically selects participating vehicles based on their locations and training loss. By incorporating adaptive model sparsification, the proposed scheme dynamically proceeds with sparse masks during vehicle local training, thereby reducing communication overhead while preserving model accuracy. We conduct a rigorous convergence analysis to uncover how vehicle mobility and model sparsification affect convergence rate. Furthermore, we formulate an optimization problem to accelerate the training process, which jointly optimizes vehicle selection, sparsification ratio, and bandwidth allocation to minimize training delay. To solve the problem, we employ the Lyapunov optimization method to decouple the long-term problem into a series of instantaneous subproblems. Next, a generalized Benders decomposition method structures the original problem into a master subproblem for vehicle selection and a primal subproblem for bandwidth allocation and sparsification ratio selection. The optimal solutions are derived via alternating iterations between these problems. Extensive simulation results based on the SUMO simulator demonstrate that the MAVFL accelerates model convergence by up to 14% and reduces communication overhead by up to 26% while preserving model accuracy, as compared to the state-of-the-art benchmarks.

This paper investigates backdoor attacks in image oriented semantic communications. The threat of backdoor at tacks on symbol reconstruction in semantic communication (Sem Com) systems has received limited attention. Existing research on backdoor attacks targeting SemCom symbol reconstruction primarily focuses on input-level triggers, which are impractical in scenarios with strict input constraints. In this paper, we propose a novel channel-triggered backdoor attack (CT-BA) framework that exploits inherent wireless channel characteristics as activation triggers. Our key innovation involves utilizing fundamental channel statistics parameters, specifically channel gain with different fading distributions or channel noise with different power, as potential triggers. This approach enhances stealth by eliminating explicit input manipulation, provides flexibility through trigger selection from diverse channel conditions, and enables automatic activation via natural channel variations without adversary intervention. We extensively evaluate CT-BA across four joint source-channel coding (JSCC) communication system architectures and three benchmark datasets. Simulation results demonstrate that our attack achieves near-perfect attack success rate (ASR) while maintaining effective stealth. Finally, we discuss potential defense mechanisms against such attacks.

The Internet of Vehicles (IoV) is undergoing a transformative evolution, enabled by advancements in future 6 G network technologies, to support intelligent, highly reliable, and low-latency vehicular services. However, the enhanced capabilities of loV have heightened the demands for efficient network resource allocation while simultaneously giving rise to diverse vehicular service requirements. For network service providers (NSPs), meeting the customized resource-slicing requirements of vehicle service providers (VSPs) while maximizing social welfare has become a significant challenge. This paper proposes an innovative solution by integrating a mean-field multi-agent reinforcement learning (MFMARL) framework with an enhanced Vickrey-Clarke-Groves (VCG) auction mechanism to address the problem of social welfare maximization under the condition of unknown VSP utility functions. The core of this solution is introducing the “value of information” as a novel monetary metric to estimate the expected benefits of VSPs, thereby ensuring the effective execution of the VCG auction mechanism. MFMARL is employed to optimize resource allocation for social welfare maximization while adapting to the intelligent and dynamic requirements of IoV. The proposed enhanced VCG auction mechanism not only protects the privacy of VSPs but also reduces the likelihood of collusion among VSPs, and it is theoretically proven to be dominant-strategy incentive compatible (DSIC). The simulation results demonstrate that, compared to the VCG mechanism implemented using quantization methods, the proposed mechanism exhibits significant advantages in convergence speed, social welfare maximization, and resistance to collusion, providing new insights into resource allocation in intelligent 6 G networks.

The increase of bandwidth-intensive applications in sixth-generation (6 G) wireless networks, such as real-time volumetric streaming, and multi-sensory extended reality, demands intelligent multicast routing solutions capable of delivering differentiated quality-of-service (QoS) at scale. Traditional shortest-path and multicast routing algorithms are either computationally prohibitive or structurally rigid, and they often fail to support heterogeneous user demands, leading to suboptimal resource utilization. Neural network-based approaches, while offering improved inference speed, typically lack topological generalization and scalability. To address these limitations, this paper presents a graph neural network (GNN)-based multicast routing framework that jointly minimizes total transmission cost and supports user-specific video quality requirements. The routing problem is formulated as a constrained minimum-flow optimization task, and a reinforcement learning algorithm is developed to sequentially construct efficient multicast trees by reusing paths and adapting to network dynamics. A graph attention network (GAT) is employed as the encoder to extract context-aware node embeddings, while a long short-term memory (LSTM) module models the sequential dependencies in routing decisions. Extensive simulations demonstrate that the proposed method closely approximates optimal dynamic programming-based solutions while significantly reducing computational complexity. The results also confirm strong generalization to large-scale and dynamic network topologies, highlighting the method's potential for real-time deployment in 6 G multimedia delivery scenarios. Code is available athttps://github.com/UNIC-Lab/GNN-Routing.

The rapid growth of connected vehicles creates new opportunities to exploit internet of vehicles (IoV) data for traffic state estimation (TSE), which is a key enabler of intelligent transportation systems (ITS). In this paper, we propose a cost-effective TSE framework that leverages sparse IoV data, which significantly reducing the data collection overhead associated with large-scale IoV datasets. We further analyze the impact of data sparsification and show that the induced estimation errors can be well approximated by Gaussian noise, thereby reformulating sparse IoV-based TSE as a denoising problem. To enhance estimation accuracy, we develop a spatial-temporal attention model, termed the convolutional retentive network (CRNet), which integrates convolutional neural networks (CNNs) for spatial correlation learning with a retentive network (RetNet) for temporal dependency modeling. Extensive experiments conducted on a large-scale real-world IoV dataset validate the feasibility of TSE under sparse IoV sensing conditions. Notably, even when only 5% of the data is available, CRNet achieves a mean absolute error (MAE) below 5 km/h, demonstrating both the high accuracy of the proposed approach and its practical applicability in real-world scenarios.

Future 6G networks are envisioned to facilitate edge-assisted mobile augmented reality (MAR) via strengthening the collaboration between MAR devices and edge servers. In order to provide immersive user experiences, MAR devices must timely upload camera frames to an edge server for simultaneous localization and mapping (SLAM)-based device pose tracking. In this paper, to cope with user-specific and non-stationary uplink data traffic, we develop a digital twin (DT)-based approach for user-centric communication service provision for MAR. Specifically, to establish DTs for individual MAR devices, we first construct a data model customized for MAR that captures the intricate impact of the SLAM-based frame uploading mechanism on the user-specific data traffic pattern. We then define two DT operation functions that cooperatively enable adaptive switching between different data-driven models for capturing non-stationary data traffic. Leveraging the user-oriented data management introduced by DTs, we propose an algorithm for network resource management that ensures the timeliness of frame uploading and the robustness against inherent inaccuracies in data traffic modeling for individual MAR devices. Trace-driven simulation results demonstrate that the user-centric communication service provision achieves a 14.2% increase in meeting the camera frame uploading delay requirement in comparison with the slicing-based communication service provision widely used for 5G.

Cooperative uplink multi-base station (BS) reception has emerged as a promising technology to enhance received signal strength and improve wireless spectral efficiency. However, realizing the potential performance gains remains challenging due to substantial communication overhead among cooperative BSs and excessive delays in channel state information (CSI) feedback. This paper investigates a CSI feedback-free mechanism that leverages time-invariant physical layer parameters to facilitate cooperative BS reception within a fully-decoupled radio access network (FD-RAN). First, given the dynamically changing characteristics of the wireless environment, we employ conditional variational autoencoder (CVAE), a state-of-the-art generative artificial intelligence (GAI) approach, to generate location-specific representative channels for calculating CSI feedback-free transmission parameters. Subsequently, to maximize the throughput of user equipment (UE), a diffusion model-based deep reinforcement learning (DRL) framework is proposed for jointly selecting cooperative BS reception sets and precoding schemes, utilizing the representative channels generated by CVAE. Extensive simulations conducted on a link-level simulator demonstrate that the proposed CSI feedback-free mechanism for cooperative multi-BS reception can effectively improve spectrum efficiency by 17.3%, which provides a promising design principle for the development of sixth-generation (6G) wireless networks.

In the rapidly evolving Next-Generation Networking (NGN) era, the adoption of zero-trust architectures has become increasingly crucial to protect security. However, provisioning zero-trust services in NGNs poses significant challenges, primarily due to the environmental complexity and dynamics. Motivated by these challenges, this paper explores efficient zero-trust service provisioning using hierarchical micro-segmentations. Specifically, we model zero-trust networks via hierarchical graphs, thereby jointly considering the resource- and trust-level features to optimize service efficiency. We organize such zero-trust networks through micro-segmentations, which support granular zero-trust policies efficiently. To generate the optimal micro-segmentation, we present the Large Language Model-Enhanced Graph Diffusion (LEGD) algorithm, which leverages the diffusion process to realize a high-quality generation paradigm. Additionally, we utilize gradient ascent and Large Language Models (LLM) to enable LEGD to optimize the generation policy and understand complicated graphical features. Moreover, realizing the unique trustworthiness updates and service upgrades in zero-trust NGN, we further present LEGD-Adaptive Maintenance (LEGD-AM), providing an adaptive way to perform task-oriented fine-tuning on LEGD. Extensive experiments demonstrate that the proposed LEGD achieves 90% higher efficiency in provisioning services compared with other baselines. Moreover, the LEGD-AM can reduce the service outage time by over 50%.

Six-dimensional movable antenna (6DMA) offers a potential solution to enhance wireless transmission performance by physically reconfiguring antenna positions and orientations. However, prevailing snapshot-based reactive methods are ill-suited for continuously tracking mobile user equipments (UEs) due to their neglect of antenna kinematic constraints and system latency. To address these limitations, in this paper, we propose a proactive approach by modeling UE tracking as a single, long-term 6DMA trajectory optimization problem to maximize sum spectral efficiency. Leveraging a channel knowledge map (CKM) for predictive data, our model holistically incorporates the system’s complex kinematics and physical constraints, including velocity limits and safety distances, to ensure a physically feasible trajectory. To solve this high-dimensional, non-convex problem, we develop a novel manifold optimization algorithm. This method maps the antenna’s rotational states onto the SO(3) Lie group and employs an adaptive penalty measure with tangent space backpropagation for an efficient solution. Simulation results demonstrate our approach significantly enhances sum spectral efficiency over benchmarks, while ensuring continuous and physically feasible antenna trajectories.

Low altitude uncrewed aerial vehicles (UAVs) are expected to facilitate the development of aerial-ground integrated intelligent transportation systems and unlocking the potential of the emerging low-altitude economy. However, several critical challenges persist, including the dynamic optimization of network resources and UAV trajectories, limited UAV endurance, and imperfect channel state information (CSI). In this paper, we offer new insights into low-altitude economy networking by exploring intelligent UAV-assisted vehicle-to-everything communication strategies aligned with UAV energy efficiency. Particularly, we formulate an optimization problem of joint channel allocation, power control, and flight altitude adjustment in UAV-assisted vehicular networks. Taking CSI feedback delay into account, our objective is to maximize the vehicle-to-UAV communication sum rate while satisfying the UAV's long-term energy constraint. To this end, we first leverage Lyapunov optimization to decompose the original long-term problem into a series of per-slot deterministic subproblems. We then propose a diffusion-based deep deterministic policy gradient (D3PG) algorithm, which innovatively integrates diffusion models to determine optimal channel allocation, power control, and flight altitude adjustment decisions. Through extensive simulations using real-world vehicle mobility traces, we demonstrate the superior performance of the proposed D3PG algorithm compared to existing benchmark solutions.

Radio maps (RMs) are essential for environment-aware communication and sensing, providing location-specific wireless channel information. Existing RM construction methods often rely on precise environmental data and base station (BS) locations, which are not always available in dynamic or privacy-sensitive environments. While sparse measurement techniques reduce data collection, the impact of noise in sparse data on RM accuracy is not well understood. This paper addresses these challenges by formulating RM construction as a Bayesian inverse problem under coarse environmental knowledge and noisy sparse measurements. Although maximum a posteriori (MAP) filtering offers an optimal solution, it requires a precise prior distribution of the RM, which is typically unavailable. To solve this, we propose RadioDiff-Inverse, a diffusion-enhanced Bayesian inverse estimation framework that uses an unconditional generative diffusion model to learn the RM prior. This approach not only reconstructs the spatial distribution of wireless channel features but also enables environmental building outlines perception, just relying on pathloss, through integrated sensing and communication (ISAC). The proposed method operates on routine communication measurements, without new waveforms, specialized feedback, or protocol changes, thereby enabling a plug-and-play ISAC capability. Remarkably, RadioDiff-Inverse is training-free, leveraging a pre-trained model from Imagenet without task-specific fine-tuning, which significantly reduces the training cost of using a generative large model in wireless networks. Experimental results demonstrate that RadioDiff-Inverse achieves state-of-the-art performance in accuracy of RM construction and environmental reconstruction, and robustness against noisy sparse sampling.

In this paper, we propose a new secure data integrity verification scheme for mobile edge computing (MEC), named IntactEdge, which provides an efficient method for end users in MEC to verify that the edge servers correctly maintain their stored data. Particularly, IntactEdge is designed using lightweight cryptographic mechanisms, such as message authentication codes and hash functions, to ensure efficiency in generating and verifying data integrity proofs. Batch verification is also supported to further enhance computational efficiency for end users during integrity checks. In addition, IntactEdge offers the desirable feature of multi-replica secure storage, allowing user data to be distributed across multiple edge servers to enhance data availability. To prevent edge nodes from exploiting data replicas stored on other nodes to falsely satisfy integrity verification requests, IntactEdge generates distinct data replicas for different edge nodes, while still enabling each replica to support proof generation using the same authentication tag. Finally, we demonstrate that IntactEdge is secure against potential data integrity attacks on edge servers and highly efficient in both tag generation and integrity verification.

Achieving the stringent requirements of ultra reliable low latency communication (URLLC) in vehicular networks, characterized by high mobility, presents significant challenges. Orthogonal time frequency space (OTFS) modulation has surfaced as a promising solution to tackle Doppler shifts and delay spreads in high-mobility wireless channels by mapping symbols into the delay-Doppler (DD) domain. This paper introduces a novel OTFS-enabled transceiver framework for predictive beamforming in vehicular networks. The framework employs frequency division duplex (FDD) to diminish latency and enhance flexibility, with the predictive beamforming technique implemented at the transmitter to improve the received signal strength (RSS). Owing to the latency constraints of URLLC, predictive beamforming necessitates leveraging historical channel state information (CSI) in the DD domain. In this context, a deep learning (DL) approach using the ConvLSTM network is harnessed for predictive beamforming, concentrating on capturing spatial-temporal correlations of high-mobility wireless channels from historical DD domain CSI. Extensive simulations validate the efficacy of our proposed DL-based predictive beamforming for OTFS-enabled URLLC in vehicular networks.

Six-dimensional movable antenna (6DMA) is an innovative and transformative technology to improve wireless network capacity by adjusting the 3D positions and 3D rotations of antennas/surfaces (sub-arrays) based on the channel spatial distribution. For optimization of the antenna positions and rotations, the acquisition of statistical channel state information (CSI) is essential for 6DMA systems. In this paper, we unveil for the first time a new directional sparsity property of the 6DMA channels between the base station (BS) and the distributed users, where each user has significant channel gains only with a (small) subset of 6DMA position-rotation pairs, which can receive direct/reflected signals from the user. By exploiting this property, a covariance-based algorithm is proposed for estimating the statistical CSI in terms of the average channel power at a small number of 6DMA positions and rotations. Based on such limited channel power estimation, the average channel powers for all possible 6DMA positions and rotations in the BS movement region are reconstructed by further estimating the multi-path average power and direction-of-arrival (DOA) vectors of all users. Simulation results show that the proposed directional sparsitybased algorithm can achieve higher channel power estimation accuracy than existing benchmark schemes, while requiring a lower pilot overhead.

In this paper, we propose a novel service continuityaware Service Function Chain (SFC) embedding scheme for dynamic large-scale LEO satellite networks, where service disruptions occur when satellites hosting virtual network functions of an SFC move out of the service region. Particularly, we define a new metric, i.e., the Remaining Time to Migration (RTTM), which indicates the remaining functional time of an SFC before SFC reconfiguration is needed. We then formulate a service continuity-aware SFC embedding problem with the objective of maximizing the long-term acceptance ratio while increasing the normalized RTTM of accepted SFCs. We propose a scalable graph neural network-assisted deep reinforcement learning (DRL) approach to solve the embedding problem. By employing a differentiable pooling technique, we condense the feature representation of large-scale LEO satellite networks, thereby reducing the computational complexity of the down-stream DRL-based decision-making. Simulation results show that our approach reduces the proportion of reconfigured SFCs by 60 % compared to the benchmark, indicating its effectiveness in enhancing service continuity.

In this paper, we propose a novel road side unit (RSU)-assisted cooperative sensing scheme for connected autonomous vehicles (CAVs), with the objective to reduce completion time of sensing tasks. Specifically, LiDAR sensing data of both RSU and CAVs are selectively fused to improve sensing accuracy, and computing resources therein are cooperatively utilized to process tasks in real time. To this end, for each task, we decide whether to compute it at the CAV or at the RSU and allocate resources accordingly. We first formulate a joint task placement and resource allocation problem for minimizing the total task completion time while satisfying sensing accuracy constraint. We then decouple the problem into two subproblems and propose a two-layer algorithm to solve them. The outer layer first makes task placement decision based on the Gibbs sampling theory, while the inner layer makes spectrum and computing resource allocation decisions via greedy-based and convex optimization subroutines, respectively. Simulation results based on the autonomous driving simulator CARLA demonstrate the effectiveness of the proposed scheme in reducing total task completion time, comparing to benchmark schemes.

In this paper, we propose a novel digital twin (DT)assisted joint communication and control scheme, in which robots can achieve much better collaboration for search and rescue (SAR) tasks in disaster-affected areas. Particularly, the design of the scheme is decomposed into two sub-developments at different timescales. First, a communication-aware robust control policy is developed for each robot in a small timescale based on nonlinear model predictive control and control barrier function constraints, which mitigates the impact of device-todevice communication delay on task execution. Second, a controlaware radio spectrum resource allocation strategy is developed at the edge server in a large timescale to improve total control task effectiveness of robots. This is achieved by a DT-assisted deep reinforcement learning (DRL) algorithm, where DTs of robots are utilized to emulate potential robot movements for DRL state augmentation. Simulation results demonstrate that the proposed scheme outperforms benchmarks for SAR tasks. A simulation demo can be found at: https://youtu.be/9Salnh9EIVg.

Deploying and executing large model inference on edge devices is challenging due to their limited computational power and memory resources. To address this challenge, we present a novel Split-and-Pipeline, a collaborative inference scheme that partitions a large model into multiple submodels and executes them across distributed edge devices in a pipelined manner. The scheme parallelizes data transfer across multiple CPU cores to avoid transmission bottlenecks. We build a real-world testbed using NVIDIA Jetson series edge devices to demonstrate the proposed scheme, achieving 1.2×–3.0× throughput improvement over state-of-the-art baselines.

This paper presents a novel model-assisted learning scheme for resource allocation in environment-aware mobile augmented reality (AR) content delivery. The goal is to minimize the long-term communication resource consumption for delivering virtual content visible to an individual AR user by optimizing the communication resource allocation for user positioning and environment mapping. In specific, we first develop a mathematical model to estimate the content visibility uncertainty and the content delivery resource consumption. We then generate a reference resource allocation decision that guides a deep reinforcement learning-based decision process to efficiently adapt to non-stationary user and environment dynamics. We conduct trace-driven simulations to evaluate the performance of the proposed scheme, and the results demonstrate that, the proposed scheme significantly reduces communication resource consumption for delivering virtual content visible to an individual AR user, compared to benchmark schemes.

Deep learning plays increasingly important role in future wireless network management and optimization. Existing training methods such as label-based supervised learning and label-free learning have inherent limitations. The performance of supervised learning is limited by labels, while label-free training methods require extensive exploration. To address these limitations, this paper proposes a novel mixture of gradients (MoG) method, which integrates gradients from different sources within the training process in order to improve the convergence performance of neural networks (NNs). Particularly, MoG is a modular, plug-and-play solution requiring no structural modifications to existing NNs. Its implementation necessitates only minor modifications to the loss function, where the label-based supervised loss is combined with a label-free loss through weighted summation. The label-free loss can be either unsupervised loss or reinforcement learning loss. This flexibility allows seamless integration into nearly all NN-based methods, making it applicable to a wide range of wireless optimization problems with minimal implementation cost. Extensive simulations across multiple classic wireless scenarios demonstrate that MoG can significantly enhance the performance of NN decision-making, leading to higher transmission rates.

Given the limited onboard resources and operational time constraints, dynamic collaboration among moving intelligent machines, such as unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) through task offloading has become essential for effective task completion. However, the growing offloading complexity and mismatch between task specifics and distributed resources inevitably lead to resource wastage and potential task failures. Furthermore, malicious collaborators may sneak into offloading processes, which undermines collaborative system reliability. To tackle these challenges collectively, a goal-driven trusted task offloading strategy is proposed, which efficiently matches diverse tasks to optimal distributed resources. Specifically, multidimensional goals of complex tasks are modeled as distinct task completion metrics, jointly termed Value of Service (VoS). Moreover, we define task-specific trust as a goal-achieving mechanism that enables the construction of a reliable collaborator group for a given task with diverse VoS. Based on the task-specific trust evaluation of all potential collaborators, the task offloading process is transformed into a trust-guided bipartite graph matching problem. To mitigate the matching complexity in large-scale collaborative systems, decomposed subtasks with similar goals are initially clustered into limited categories and subsequently arranged by priorities. Simulation results show the proposed strategy efficiently selects capable and reliable collaborators who complete tasks as expected in unreliable dynamic environments.

Differentiable architecture search (DARTS) is a prevailing direction in automatic machine learning, but it may suffer from performance collapse and generalization issues. Recent efforts mitigate them by integrating regularization into architectural parameters or rule-based operations selection. These efforts primarily emphasize learning the global class-specific features through the image classification task, while overlooking the fine-grained local information during the search process. In this article, we take the first trial to observe that three semantic challenges arise from the classification-based DARTS: 1) inaccurate class-specific features; 2) partial target attention; and 3) blurred semantic regions. To tackle them in one shot, we propose Semantic-DARTS, combining the masked image modeling (MIM) paradigm with the classification task to incorporate local semantic information into the architecture search. Specifically, we design a lightweight reconstruction head that recovers the corrupted image based on the condensed latent feature, which learns both the local semantics and their relationship patch-wisely. Simultaneously, the concurrent classification head strengthens the connection between the global category of the target and the local semantics of their parts. As evidenced by our experiments, the proposed approach achieves state-of-the-art results on CIFAR-10, CIFAR-100, and ImageNet. Furthermore, the searched model is not only able to improve global class-specific features but also to capture fine-grained local representations, improving both the classification performance and the generalization ability.

In this article, we investigate infrastructure-assisted environment sensing in satellite-terrestrial vehicular networks (STVN) for connected autonomous vehicles (CAVs), where satellites and roadside units (RSUs) cooperate to provide CAVs with fresh sensing data. To support satellite- and RSU-assisted environment sensing for CAVs, we formulate a long-term resource scheduling problem in STVN to satisfy sensing data freshness requirements with efficient resource usage. To deal with the challenges posed by the dynamic network environment as well as stringent data freshness requirements, we propose a cooperative satellite-terrestrial resource scheduling (CSTRS) scheme. CSTRS is a model-data co-driven approach that can jointly optimize the sensing interval and resource allocation in STVN. Specifically, benefiting from the multicast feature of the low Earth orbit satellite, coalition game, and particle swarm optimization-based algorithms are designed to partition CAVs into groups and optimize sensing intervals in large timescales. Then, a reinforcement learning-based algorithm is developed to make real-time computing and communication resource allocation decisions based on the CAV partition. Simulation results demonstrate that the proposed scheme outperforms benchmark methods in terms of resource usage and reliability performance.

Practical applications in mobile ad-hoc networks (MANETs) require the support of diverse network services, e.g., host-centric, content-centric, and location-centric routing and forwarding services. However, existing solutions are typically designed over a single network service rather than integrated ones. To embed diverse network services into MANETs, the major challenge is enabling interoperability among various network-layer (L3) protocols without suffering complexity and scalability issues. In this article, we propose OpenL3, a programmable L3 approach to support the coexistence of diverse network services in MANETs. Specifically, OpenL3 first abstracts key attributes from network entities, such as content, locations, or groups of devices. These attributes are embedded into a network address, named multidimensional identifier (MID), to control the routing and forwarding processes. Then, a distributed MID mapping system is established to facilitate efficient MID registration and query. Based on the MID, a programmable routing and forwarding scheme is proposed, which incorporates a lightweight packet processing design using a P4 programmable data plane to enable interoperability among various L3 protocols. A cluster of SDN-based control plane devices collaboratively distribute flow rules to manage data plane behavior. Furthermore, a prototype system is built to implement and evaluate the proposed solutions. Experimental results show that OpenL3 outperforms the existing solutions in terms of end-to-end latency and network throughput while being deployable in MANETs without modifications to network protocols or sockets.

In this article, we propose a novel volumetric video caching and rendering approach for an edge-assisted extended reality (XR) system to enhance user Quality of Experience (QoE). Particularly, user QoE consists of visual quality and quality variation. Different quality of volumetric videos are required to be cached, rendered, and delivered to XR devices for different viewing distances within a time latency. Given the limited caching, computing, and communication resources on the edge server, we formulate a long-term user QoE maximization problem to jointly optimize video caching and rendering by considering user locations and viewing distances. To solve this problem, we first design an online optimization algorithm in which caching decisions are obtained using a regularization technique. We then develop a low-complexity binary search algorithm to determine optimal rendering quality. Extensive simulations are conducted to demonstrate that our proposed approach outperforms benchmark schemes by an average 46% improvement in terms of long-term user QoE.

The widely deployment of infrastructure and wireless interfaces increases industrial IoT (IIoT) vulnerability to network intrusions, highlighting the requirements for robust network intrusion detection systems (NIDSs). Although deep learning (DL) provides a promising solution for NIDSs, it remains susceptible to adversarial attacks as minor input perturbations can lead to major misclassifications. In this paper, we propose a packet-level adversarial traffic generation (PATG) approach for attacking NIDSs in IIoT, which not only aligns with domain constraints but also evades various DL-based NIDSs. Particularly, we introduce a reversible abstract traffic representation to ensure that the original traffic can be effectively modified while preserving its functionality. We propose a packet-level generative adversarial networks to craft adversarial traffic by learning benign data distribution in feature space and simulating evasion behaviors, which escapes the DL-based NIDSs. We further design two defense schemes to enhance system resilience against proposed adversarial attacks. We evaluate PATG on nine state-of-the-art DL-based NIDSs in the Kitsune and CICIoT23 datasets. Experimental results demonstrate that PATG can achieve a maximum evasion increase rate of 99% with cost-effective execution, while the defense methods significantly mitigate the impact of the adversarial attacks.

Semantic communication has emerged as a promising solution to meet the growing demand for efficient data transmission in the information age. Unlike traditional communication methods that focus on transmitting raw data, semantic communication prioritizes preserving the meaning of transmitted information, which significantly reduces the data volume. However, implementing semantic communication systems in resource-constrained environments, such as Internet of Things (IoT) devices, remains challenging due to limited computational resources. In this letter, we propose a novel lightweight deep learning (DL) model, termed the lightweight image compression and reconstruction network (LICRnet). LICRnet leverages depthwise separable convolution (DSC) and a local and nonlocal mixture (LNLM) block to significantly reduce computational costs. Additionally, the LNLM incorporates a variable window size-based multiscale attention mechanism (VW-MSA), enabling it to effectively learn from both local detailed features and global high-level meaningful features. Extensive simulations demonstrate that LICRnet significantly reduces computational complexity while maintaining satisfactory image compression and reconstruction performance, making it highly suitable for deployment in resource-constrained environments.

This paper proposes a novel integrated aerial-terrestrial multi-operator network in which each operator deploys a number of unmanned aerial vehicle-base stations (UAV-BSs) besides the terrestrial macro base station (MBS), where each BS reuses the operator’s licensed band to provide downlink connectivity for UAV-user equipment (UAV-UE). In addition, the operators allow the UAV-UE, whose demand cannot be satisfied by the licensed band, to compete with others to obtain bandwidth resources from the unlicensed spectrum. Considering inter-cell and inter-operator interference in the licensed and unlicensed spectrum, the user association, power allocation, and dynamic spectrum sharing are jointly optimized to maximize the network throughput while ensuring the UAV-UEs’ data rate requirements. The formulated optimization problem, which is an NP-hard problem, is divided into two sequential subproblems. We propose a distributed iterative algorithm composed of a matching game, coalition game, and successive convex approximation technique to jointly solve the user association and power control subproblems in the licensed spectrum. Afterwards, we propose a three-layer auction framework to allocate the unlicensed spectrum dynamically between operators. Simulation results show that the proposed algorithms with the additional use of the unlicensed spectrum achieve 86.8% higher system throughput than that of only using the licensed spectrum.

Distributed Artificial Intelligence-Generated Content (AIGC) has attracted significant attention, but two key challenges remain: maximizing subjective Quality of Experience (QoE) and improving energy efficiency, which are particularly pronounced in widely adopted Generative Diffusion Model (GDM)-based image generation services. In this paper, we propose a novel user-centric Interactive AI (IAI) approach for service management, with a distributed GDM-based AIGC framework that emphasizes efficient and cooperative deployment. The proposed method restructures the GDM inference process by allowing users with semantically similar prompts to share parts of the denoising chain. Furthermore, to maximize the users' subjective QoE, we propose an IAI approach, i.e., Reinforcement Learning With Large Language Models Interaction (RLLI), which utilizes Large Language Model (LLM)-empowered generative agents to replicate users interactions, providing real-time and subjective QoE feedback aligned with diverse user personalities. Lastly, we present the GDM-based Deep Deterministic Policy Gradient (G-DDPG) algorithm, adapted to the proposed RLLI framework, to allocate communication and computing resources effectively while accounting for subjective user traits and dynamic wireless conditions. Simulation results demonstrate that G-DDPG improves total QoE by 15% compared with the standard DDPG algorithm.

Achieving both a holistic and in-depth understanding of network dynamics through accurate modeling is essential for orchestrating future 6G networks, considering their increasing complexity and service diversity. However, traditional situation-agnostic data collection and network modeling approaches often undermine the efficacy and timeliness of network orchestration in such complex environments. Furthermore, temporal misalignments caused by varying modeling delays across distributed networks further impair centralized decision-making. To address these challenges, this paper proposes a hierarchical digital twin framework with an adaptive layered architecture designed for problem-oriented 6G network modeling and orchestration. At higher layers, we introduce an adaptive attribute selection mechanism that efficiently evaluates network situations and identifies problematic areas. This mechanism prioritizes critical attributes by jointly considering their relevance to current network objectives and modeling complexity. At lower layers, these prioritized attributes and critical users are selectively incorporated into scalable network modeling. More detailed digital twins are then created to deliver targeted solutions for optimizing user association and power allocation. Additionally, we implement a multi-level synchronization mechanism to ensure temporal alignment among the digital twins, thereby enhancing the effectiveness of model-based orchestration. Extensive simulations validate the efficient identification of pressing operational issues and the effective orchestration of complex 6G networks.

Integrated sensing and communications (ISAC) is one of the crucial technologies for 6G, and channel state information (CSI) based sensing serves as an essential part of ISAC. However, current research on ISAC focuses mainly on improving sensing performance, overlooking security issues, particularly the unauthorized sensing of users. Hence, this paper proposes a diffusion model based secure sensing system (DFSS). Specifically, we first propose a discrete conditional diffusion model to generate graphs with nodes and edges, which guides the ISAC system to appropriately activate wireless links and nodes, ensuring the sensing performance while minimizing the operation cost. Using the activated links and nodes, DFSS then employs the continuous conditional diffusion model to generate safeguarding signals, which are next modulated onto the pilot at the transmitter to mask fluctuations caused by user activities. As such, only authorized ISAC devices with the safeguarding signals can extract the true CSI for sensing, while unauthorized devices are unable to perform the effective sensing. Experiment results demonstrate that DFSS can reduce the activity recognition accuracy of the unauthorized devices by approximately 70%, effectively shield the user from the illegitimate surveillance.

Joint estimation is crucial in the industrial Internet of Things (IIoT) by integrating data from diverse devices to improve monitoring accuracy. Network slicing can meet the heterogeneous needs of devices through logical isolation. However, existing methods often overlook the interaction of multiple slices on estimation performance, leading to potential estimation bias and ineffective resource costs. To address this, we propose an Age of Task (AoT)-driven associated network slicing method tailored for joint estimation scenarios. Specifically, we design an association-oriented slicing architecture for joint estimation that considers both the heterogeneous requirements of individual slices and the interactive effects of multiple slices. We define slice association based on the AoT to quantify the coupling relationship between slicing strategies and estimated performances. Moreover, we develop a dynamic-fitness multivariable particle swarm optimization algorithm to achieve associated slicing. Simulation results show that the associated slicing scheme achieves a flexible balance between timeliness and accuracy.

Unmanned Aerial Vehicle (UAV)-assisted Low Earth Orbit (LEO) satellite edge computing (ULSE) networks can address the challenge communications issues in areas with harsh terrain and achieve global wireless coverage to provide services for mobile user devices (MUDs). This paper studies the LEO-UAV task offloading problem where MUDs compete for limited resources in the ULSE networks. We formulate the optimization problem with the goal of minimizing the cost of all MUDs while meeting resource constraint and satellite coverage time constraint. We first theoretically prove that this problem is NP-hard. We then reformulate the problem as a LEO-UAV task offloading game (LUTO-Game), and show that there is at least one Nash equilibrium solution for the LUTO-Game. We propose a joint UAV and LEO satellite task offloading (JULTO) algorithm to obtain the Nash equilibrium offloading strategy, and analyze the performance of the worst-case offloading strategy obtained by the JULTO algorithm. Finally, extensive experiments, including convergence analysis and comparison experiments, are carried out to validate the effectiveness of our JULTO algorithm.

Deterministic network services play a vital role for supporting emerging real-time applications with bounded low latency, jitter, and high reliability. The deterministic guarantee is penetrated into various types of networks, such as 5G, WiFi, satellite, and edge computing networks. From the user’s perspective, the real-time applications require end-to-end deterministic guarantee across the converged network. In this paper, we investigate the end-to-end deterministic guarantee problem across the whole converged network, aiming to provide a scalable method for different kinds of converged networks to meet the bounded end-to-end latency, jitter, and high reliability demands of each flow, while improving the network scheduling QoS. Particularly, we set up the global end-to-end control plane to abstract the deterministic-related resources from converged network, and model the deterministic flow transmission by using the abstracted resources. With the resource abstraction, our model can work well for different underlying technologies. Given large amounts of abstracted resources in our model, it is difficult for traditional algorithms to fully utilize the resources. Thus, we propose a deep reinforcement learning based end-to-end deterministic-related resource scheduling (E2eDRS) algorithm to schedule the network resources from end to end. By setting the action groups, the E2eDRS can support varying network dimensions both in horizontal and vertical end-to-end deterministic-related network architectures. Experimental results show that E2eDRS can averagely increase 1.33x and 6.01x schedulable flow number for horizontal scheduling compared with MultiDRS and MultiNaive algorithms, respectively. The E2eDRS can also optimize 2.65x and 3.87x server load balance than MultiDRS and MultiNaive algorithms, respectively. For vertical scheduling, the E2eDRS can still perform better on schedulable flow number and server load balance.

Mobile contact exhibits user co-traveling events within the same transportation tool, which is crucial for resident profiling, face-to-face interaction detection, etc. In this paper, we investigate urban user mobile contact detection with cellular signaling traces, which is cost-efficient to enable large-scale detection. Specifically, we develop a data collection platform to collect substantial user signaling traces, covering different types of road scenarios within a city. With the collected traces, we perform systematic data analysis to reveal several technical challenges, which are sparsity of signaling trajectory, remote base station noise, and fuzzy matching difficulties. To address challenges, we propose a mobile contact detection method namedMoCo. InMoCoframework, we first conduct data denoising to remove the noise from remote base stations. Then, we devise a spatio-temporal filter to eliminate unlikely mobile contact traces in both spatial and temporal domains, reducing the computational overhead. Finally, we design a detection network that integrates the submodules of data alignment, feature encoder, spatio-temporal representation learner, and user mobile contact detector. Extensive evaluation results demonstrate the superiority ofMoCoin comparison with state-of-the-art baselines. Robust experiments show thatMoCocan work efficiently in different transportation modes and urban densities.

Continual machine learning in the context of limited computational resources and data availability is critical in the connected digital world. Current intelligent applications predominantly rely on deep learning models requiring labor/computation-intensive training. These models often struggle to adapt effectively to new data while preserving performance on previously learned knowledge. In this paper, we introduce a lightweight method for continual knowledge adaptation that can address these challenges. To prevent disruption of the existing services, we propose a Mixture-of-Experts (MoE) adapter that integrates seamlessly with the existing vision model to encode new data. The weights of the original model are kept fixed during the adaptation process, ensuring the preservation of previously learned knowledge. The MoE technique enables scaling up the parameters of the adapter while maintaining a relatively low computation, making it fit for constrained devices in mobile computation scenarios. Furthermore, to enhance learning efficiency and accelerate convergence with new data, we implement a knowledge fusion mechanism that facilitates interaction between the existing knowledge and the information extracted from new data. The timing of employing the fusion module is further investigated. We find that it is conducive in scenarios where the task's performance requirements are enhanced. The MoE adapter and knowledge fusion module are integrated at each stage with minimal trainable parameters, efficiently optimizing resource usage. Extensive experiments and ablation studies validate the effectiveness of the proposed method. Specifically, the proposed method prevents an accuracy drop of 43.02% on the previous data compared to the continual train method, while achieving an accuracy of 44.81% on the new data, which is even 0.34% higher than fully training a new model.

Artificial intelligence-generated content (AIGC) has been proposed as a solution to meet the requirements of ultra-reliable, secure, and privacy-preserving connectivity in human digital twin (HDT) networks. In such an AIGC-enhanced HDT, contents representing the true statuses of physical twins are generated in the virtual environment for the immediate update and evolution of the corresponding virtual twins (VTs). However, adopting a distributed AIGC in HDT presents several challenges, including the need for personalized VTs, data privacy concerns, and insufficient contextual understanding. This paper introduces a multi-layer federated semantic learning framework to address these challenges, incorporating batch learning to meet the training requirements for semantic-channel encoders and decoders. Furthermore, we introduce a novel user association framework to maximize the overall system performance under shard formation constraints. We then formulate a long-term joint optimization problem for user selection over finite learning periods. A novel Lyapunov-based online optimization strategy was proposed to mitigate the impact of time-varying and unpredictable training conditions. Additionally, we introduce a multi-arm bandit-based method and a context-centric user selection approach to solve the optimization problem. The results demonstrate that the proposed user association framework addresses the limitations of existing approaches, thereby improving the overall performance of the multi-shard AIGC-enhanced HDT.

Underwater acoustic sensor networks (UASNs) drive toward strong environmental adaptability, intelligence, and multifunctionality. However, due to unique UASN characteristics, such as long propagation delay, dynamic channel quality, and high attenuation, existing studies present untimeliness, inefficiency, and inflexibility in real practice. Digital twin (DT) technology is promising for UASNs to break the above bottlenecks by providing high-fidelity status prediction and exploring optimal schemes. In this article, we propose a Digital Twin-based Network Architecture (DTNA), enhancing UASNs’ environmental adaptability, intelligence, and multifunctionality. By extracting real UASN information from local (node) and global (network) levels, we first design a layered architecture to improve the DT replica fidelity and UASN control flexibility. In local DT, we develop a resource allocation paradigm (RAPD), which rapidly perceives performance variations and iteratively optimizes allocation schemes to improve real-time environmental adaptability of resource allocation algorithms. In global DT, we aggregate decentralized local DT data and propose a collaborative Multi-agent reinforcement learning framework (CMFD) and a task-oriented network slicing (TNSD). CMFD patches scarce real data and provides extensive DT data to accelerate AI model training. TNSD unifies heterogeneous tasks’ demand extraction and efficiently provides comprehensive network status, improving the flexibility of multi-task scheduling algorithms. Finally, practical and simulation experiments verify the high fidelity of DT. Compared with the original UASN architecture, experiment results demonstrate that DTNA can: (i) improve the timeliness and robustness of resource allocation; (ii) greatly reduce the training time of AI algorithms; (iii) more rapidly obtain network status for multi-task scheduling at a low cost.

In this paper, we propose an efficient model training scheme, namedGroup-basedHierarchicalSplitLearning (GHSL), which can accelerate the artificial intelligence (AI) training process in edge networks in a “first-sequential-then-parallel” manner. Specifically, the proposed scheme hierarchically splits an AI model into a user-side and server-side model, while dividing a number of users into multiple groups. Users in each group train user-side models with the interaction of the shared server-side model sequentially; different groups perform the above training process parallelly; the AI models of each group are aggregated into a global model. We also carry out the convergence analysis for the proposed scheme over non-independent and identically distributed data, which reveals that the convergence rate depends on user grouping. Furthermore, we propose a data-driven two-stage user grouping algorithm to minimize the overall training delay, taking user resource heterogeneity and the black-box training process into account. The proposed algorithm first utilizes the Gaussian process regression approach to determine the number of groups, and then employs the coalition game theory to determine the optimal user grouping decision. Comprehensive simulation results demonstrate that the proposed scheme can reduce training delay, user-side computational workload, and communication overhead by up to 19%, 53%, and 54%, respectively, comparing to state-of-the-art benchmarks.

This paper investigates adaptive transmission strategies in embodied AI-enhanced vehicular networks by integrating vision language models (VLMs) for semantic information extraction and deep reinforcement learning (DRL) for decision-making. The proposed framework aims to optimize both data transmission efficiency and decision accuracy by formulating an optimization problem that incorporates the Weber-Fechner law, serving as a metric for balancing bandwidth utilization and quality of experience (QoE). Specifically, we employ the large language and vision assistant (LLAVA) model to extract critical semantic information from raw image data captured by embodied AI agents (i.e., vehicles), reducing transmission data size by approximately more than 90% while retaining essential content for vehicular communication and decision-making. In the dynamic vehicular environment, we employ a generalized advantage estimation-based proximal policy optimization (GAE-PPO) method to stabilize decision-making under uncertainty. Simulation results show that attention maps from LLAVA highlight the model's focus on relevant image regions, enhancing semantic representation accuracy. Additionally, our proposed transmission strategy improves QoE by up to 36% compared to DDPG and accelerates convergence by reducing required steps by up to 47% compared to pure PPO. Further analysis indicates that adapting semantic symbol length provides an effective trade-off between transmission quality and bandwidth, achieving up to a 61.4% improvement in QoE when scaling from 4 to 8 vehicles.

Differential privacy (DP) is widely used for protecting privacy in crowdsensing by adding noises. However, malicious attackers can exploit noise to launch covert data poisoning attacks. In this paper, we propose a game-based defense approach to resist such data poisoning attacks in DP-based crowdsensing systems. In this approach, attackers are believed to be powerful as they can refine their attack strategy based on the observations of deployed defenders’ defense strategy. Specifically,the defendersformulate the defense as a functional minimization problem (which cannot be directly solved by numerical optimization algorithms because its decision variable is a set of functions), resisting data poisoning attacks by deleting data shared by identified malicious workers through the log-likelihood ratio test. To obtain a current defense strategy, the decision variable of the problem is relaxed into the coefficients of basis-based linear combinations through the variable-basis approximation, and then solved using the simulated annealing genetic algorithm. Correspondingly,the attackersformulate their attack strategy as a bi-level maximization problem (which is an NP-hard problem), biasing crowdsensing results as much as possible while remaining undetected. Since the attackers can know the defense strategy, they may bypass the defenders by constraining the expected log-likelihood ratio test. Additionally, the attackers can evade truth discovery methods deployed in crowdsensing using DP noise. To determine a current attack strategy, the bi-level problem is decomposed into upper-level and lower-level sub-problems, wherein the upper-level sub-problem is solved by the variational methods, and then these sub-problems are alternately optimized. Finally, we propose a local minimax points calculating algorithm to obtain an equilibrium point in the defenders-attackers game, thereby finding an optimal defense strategy to resist the powerful data poisoning attack. Extensive experiments on real-world and synthetic datasets show that the proposed game-based defense approach can effectively defend powerful and covert attackers.

Uncrewed aerial vehicles (UAVs) play a crucial role in mobile edge computing (MEC) within space-air-ground integrated networks. They serve as aerial cloudlets, enabling task processing in close proximity to ground users. While numerous joint trajectory design and resource allocation schemes aim to enhance energy efficiency or computation rate, few focus on improving system reliability, which is often challenged by stochastic channels and node mobility. This paper presents a stochastic modeling perspective to derive a system reliability expression. Our reliability formulation incorporates the impacts of stochastic Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) air-to-ground communication channels, application data load, available bandwidth, offloading time, and transmission power. This comprehensive approach leads to a reliability-oriented joint optimization model that considers not only resource allocation and user data transmission scheduling but also the motion of UAVs. To solve this problem, we propose a low-complexity algorithm. By utilizing augmented Lagrangian multipliers, the algorithm transforms nonlinear constraints into a tractable formulation, enabling the utilization of legacy unconstrained optimization techniques. We provide a proof of convergence for this algorithm. Through simulations, we demonstrate that our proposed method guarantees convergence within finite iterations and improves the average communication reliability in comparison with several other joint optimization schemes.

Tile-based streaming has been proposed to address the challenge of high transmission rate demand in 360° virtual reality (VR) video streaming. However, it suffers from network and viewing behavior dynamics (i.e., head movements), while encoded video tiles have various properties in terms of transmission priority, deadline, and reliability requirement. Hence, a supporting transmission protocol is imperative. In this paper, we propose a customized transmission protocol based on Quick UDP Internet Connections (QUIC) which operates over a VR video network slice in the core network. The QUIC protocol is tailored to accommodate the characteristics of tile-based VR video streaming where explicit mapping relations between requested video tiles and QUIC streams are established. Two customized in-network protocol functionalities including packet filtering and caching-based packet retransmission are proposed, to filter out outdated video data due to field-of-view (FoV) prediction errors under viewing behavior dynamics and to achieve efficient packet retransmissions with disparate transmission reliability requirements. A slice-level packet header is designed to support enhanced slice-based VR video transmission with the proposed protocol functionalities. Key transport parameters are determined via theoretical analysis. Simulation results are presented to demonstrate the effectiveness of our proposed transmission protocol in achieving short average video segment downloading time and high average video segment quality.

In this paper, we present a performance analytical model for end-to-end (E2E) service provisioning (i.e., processing or transmission) of layer-encoded video packets over a network slice in the core network. The disparate service reliability requirements of base layer (BL) and enhancement layer (EL) packets are considered in the proposed analytical model for the E2E packet delays, deadline violation probabilities, and throughputs of BL and EL packets. Specifically, a network function virtualization (NFV) node along the routing path of the video streaming slice is split into two consecutive logical nodes, one for packet processing and the other for transmission, based on which a segment-based analysis framework is proposed for E2E service performance modeling. A two-stage queuing model is established to obtain the approximate steady-state probability distribution of queue length at the first node in the first segment, upon which the BL/EL packet delay, deadline violation probability, and throughput at the segment are derived. In addition, the inter-departure time of successive packets departing from the first segment is analyzed based on an approximate M/D/1 system, and the packet departure process at the first segment is approximated as a Poisson process under the assumption of a large packet service rate of the first node. The independence between two consecutive segments is then achieved for analysis tractability, based on which the E2E performance measures are derived. Extensive simulation results demonstrate the accuracy of our proposed performance analytical model and its effectiveness such as in transport parameter determination.

Generative Artificial Intelligenge (GAI) is revolutionizing the world with its unprecedented content creation ability. Large Language Model (LLM) is one of its most embraced branches. However, due to LLM’s substantial size and resource-intensive nature, it is cloud-hosted, raising concerns about privacy, usage limitations, and latency. In this paper, we propose to utilize ubiquitous distributed wireless edge computing resources for real-time LLM inference. Specifically, we introduce a novel LLM edge inference framework, incorporating batching and model quantization to ensure high throughput inference on resource-limited edge devices. Then, based on the architecture of transformer decoder-based LLMs, we formulate an edge inference optimization problem which is NP-hard, considering batch scheduling and joint allocation of communication and computation resources. The solution is the optimal throughput under edge resource constraints and heterogeneous user requirements on latency and accuracy. To solve this NP-hard problem, we develop an OT-GAH (Optimal Tree-search with Generalized Assignment Heuristics) algorithm with reasonable complexity and$\frac {1}{2}$-approximation ratio. We first design the OT algorithm with online tree-pruning for single-edge-node multi-user case, which navigates the inference request selection within the tree structure to miximize throughput. We then consider the multi-edge-node case and propose the GAH algorithm, which recrusively invokes the OT in each node’s inference scheduling iteration. Simulation results demonstrate the superiority of OT-GAH batching over other benchmarks, revealing an over 45% time complexity reduction compared to brute-force searching.

The demand for continual machine learning in the context of limited computational resources and data availability is critical in the evolving landscape of the connected digital world. Current network applications predominantly rely on deep learning models that require labor/computation-intensive training processes. These models often struggle to effectively adapt to new data while preserving performance on previously acquired knowledge. In this paper, we introduce a lightweight framework for continual knowledge adaptation and learning designed to address these challenges. To prevent disruption of existing services, we propose an attention-based adapter that integrates seamlessly with the existing vision model to encode new incoming data. The weights of the original model are kept fixed during the adaptation process, ensuring the preservation of previously learned knowledge. Furthermore, to enhance learning efficiency and accelerate convergence with new data, we implement a knowledge fusion mechanism that facilitates interaction between existing knowledge and information from new data. Our framework is modular, enabling flexible deployment across distributed devices. The adapter and knowledge fusion module are implemented at each stage with minimal trainable parameters, optimizing resource usage. Extensive experiments and ablation studies validate the effectiveness of the proposed framework.

A medium access control (MAC) protocol design is proposed in this paper for next-generation industrial Internet of Things (IIoT) networks. Considering a nonfully connected network with multiple access points (APs), we aim to connect a massive number of IIoT devices densely populating the network and minimize the delay in channel access without packet collisions. To achieve this objective, we propose a device location-based medium access control design, which integrates scheduled access and carrier sensing. In our design, devices are assigned to time slots based on their locations, and the assignments are coordinated among APs to eliminate collisions while maximizing channel utilization. To analyze the performance of the proposed design, we derive the average delay each device experiences with the proposed scheduling scheme and verify our analysis via simulations of an IIoT network with 19 APs and over 17000 devices. The results show the effectiveness of the proposed design in supporting massive connections while at the same time achieving low delay.

Integration of the fifth-generation mobile communication technology (5G) into time-sensitive networking (TSN) was first proposed in the 3GPP Release 16. However, this conceptual proposal lacks of detailed designs to guarantee bounded latency and high reliability of this integration. In this paper, we study a deterministic scheduling problem for TSN-5G integrated networks in industrial Internet of things (IIoT) scenarios, in which a unified control plane jointly allocates the time-frequency resources for TSN and 5G to support deterministic end-to-end transmission. Specifically, we design a novel control architecture, i.e., centralized network and distributed user, for the integrated networks to reduce the signaling overhead. Moreover, we formulate a stochastic optimization problem for IIoT scenarios to maximize the number of successfully scheduled flows as well as realize throughput fairness for wired and wireless equipment. Since the resource allocation of TSN and 5G are coupled, this problem is NP-hard. We propose a dueling double deep Q network (D3QN) based Joint Resource Allocation (DJRA) algorithm. By leveraging two convolution-enhanced neural networks, with their parameters periodically synchronized, the accuracy of the estimated Q-value can be increased and the convergence speed of DJRA can be accelerated. Simulation results show that the proposed algorithm can facilitate efficient cooperation between TSN and 5G as compared to the other heuristic and learning-based algorithms.

Satellite-terrestrial integrated networks (STIN) are envisioned as a promising architecture for ubiquitous network connections to support diversified services. In this paper, we pro-pose a novel resource slicing scheme with cross-cell coordination in STIN to satisfy distinct service delay requirements and efficient resource usage. To address the challenges posed by spatiotemporal dynamics in service demands and satellite mobility, we formulate the resource slicing problem into a long-term optimization problem and propose a distributed resource slicing (DRS) scheme for scalable and flexible resource management across different cells. Specifically, a hybrid data-model co-driven approach is developed, including an asynchronous multi-agent reinforcement learning- based algorithm to determine the optimal satellite set serving each cell and a distributed optimization-based algorithm to make the resource reservation decisions for each slice. Simulation results demonstrate that the proposed scheme outperforms benchmark methods in terms of resource usage and delay performance.

In this paper, we propose a digital twin (DT)-based user-centric approach for processing sensing data in an integrated sensing and communication (ISAC) system. The considered scenario involves an ISAC device with a lightweight deep neural network (DNN) and a mobile edge computing (MEC) server with a large DNN. After collecting sensing data, the ISAC device either processes the data locally or uploads them to the server for higher-accuracy data processing. To cope with data drifts, the server updates the lightweight DNN when necessary, referred to as continual learning. Our objective is to minimize the long-term average computation cost of the MEC server by jointly optimizing two decisions, i.e., sensing data offloading and sensing data selection for the DNN update. A DT of the ISAC device is constructed to predict the impact of potential decisions on the long-term computation cost of the server, based on which the decisions are made with closed-form formulas. Experiments on executing DNN-based human motion recognition tasks are conducted to demonstrate the outstanding performance of the proposed DT-based approach in computation cost minimization.

Electricity theft detection (ETD) aims to promptly identify electricity theft by vigilantly monitoring and analyzing atypical electricity consumption time series. Existing machine learning approaches to ETD demand large training sets, leading to degraded performance when limited training samples are available. In this paper, we introduce FL2ETD, a novel few-shot learning framework to ETD. The framework consists of three core components, i.e., a feature extraction module, a representation module, and a classification module. The feature extraction module processes the electricity consumption behavior of users in both the time and the frequency domains to extract distinctive features and increase the number and the diversity of features. The representation module utilizes contrast learning to pre-train unlabeled electricity consumption data for enhancing feature representation quality. The classification module integrates feature representations for making the final decision in ETD. Extensive experiments demonstrate that FL2ETD exhibits superior performance compared to baselines, and its advantage is significant when the number of available training samples is very small (with only 338 samples).

This paper introduces a real-time collaborative sensing scheme for wireless sensor networks in time-varying environments. The objective is to maximize the sensors' performance by effectively allocating communication resources for data sharing. Specifically, we utilize digital twins (DTs) to characterize dynamic collaborative sensing demands for each sensor through data-driven methods. Building on the DT design, we propose a resource allocation scheme to optimize the communication resources allocated at each stage of collaborative sensing and determine the most effective collaborative sensing policy. By profiling sensors using DTs, the network controller can effectively coordinate the sensors without exhaustively exploring all collaborative sensing policies. Numerical results demonstrate the effectiveness of our proposed scheme in optimizing the sensing performance for all sensors.

Ultra-dense low earth orbit (LEO) satellite terrestrial networks (ULSNs) are considered as a crucial component of future six generation (6G) networks, offering ubiquitous and massive services for various applications. However, for the development of advanced physical layer technologies for ULSNs, a comprehensive link-level simulation tool that integrates up-to-date satellite communication protocols becomes paramount and is urgently needed. In this paper, we develop a versatile simulator for the link-level performance analysis of ULSNs under the prevalent digital video broadcasting (DVB) standards. We first establish a complete satellite-terrestrial microwave channel model, taking practical factors such as rain attenuation, cloud attenuation, and Doppler frequency shift into consideration. Subsequently, the whole physical layer modules tailored for satellite-terrestrial microwave communication are implemented, including diverse physical layer modulation and coding schemes (MCSs). Furthermore, we realize adaptive coding and modulation (ACM) for adaptive channel performance simulation. Finally, comparative performance analysis using the established channel model is conducted to demonstrate the effectiveness of different MCSs of DVB standards. The complete link-level performance analysis based on our self-developed simulator can advance the field of satellite-terrestrial microwave communication and provide valuable insights for further exploration of ULSNs.

By enabling connectivity and collaboration among moving vehicles, Internet of Vehicles (IoV) is expected to bring dramatically improved road safety and traffic efficiency. With limited onboard resources and real-time operational constraints, achieving these goals through handling time-sensitive IoV services and tasks inevitably relies on rapid and reliable collaboration among moving vehicles. Due to safety-related considerations, such collaboration always requires complex evaluation of potential collaborative vehicles, resulting in increased latency in time-sensitive IoV task handling. To achieve rapid and reliable IoV collaboration, a comprehensive concept of trust among neighboring vehicles is first conceptualized in this article to maximize Quality of Experience (QoE) by expediting the IoV collaborator selection as well as overall task handling. Specifically, we propose a new concept of indirect trust and the related Rapid and reliable Trust Evaluation (RTE) mechanism by enabling trust transfer from reliable third parties to reduce the trust evaluation latency of potential collaborative peers. Furthermore, capability trust and direct experiential trust are introduced as two additional evaluation factors in RTE to assess the capability and reliability of collaborators and to reduce task computation time. Finally, the different factors of the proposed trust, i.e., indirect trust, direct experiential trust, and capability trust, are integrated and adaptively utilized at different stages of IoV collaboration by a proposed adaptive trust factor aggregation scheme. Simulation results demonstrate that the proposed RTE mechanism achieves higher QoE with reduced task completion latency by swiftly selecting the optimal IoV collaborator compared to existing trust evaluation mechanisms.

It is of the utmost importance to achieve flexible and fine-grained access control of electronic health records (EHR) in smart elderly healthcare (SEH) for providing high-quality healthcare services for the elderly and protecting their privacy simultaneously. In this paper, a flexible, fine-grained, and elderly-centric access control scheme is presented for EHR data in SEH. In the proposed scheme, Ciphertext Policy Attribute Based Encryption (CP-ABE), permission token, dual-key regression, and blockchain techniques are leveraged to realize multi-dimensional access control of EHR data in terms of data generation time, data user properties, access times, and access period. Moreover, a novel token segmentation algorithm is designed to transfer access rights between doctors efficiently for multi-party diagnosis and treatment. Since the elderly can define the attributes of users accessing his/her EHR data, the access number, the access time, and the access range of data from the time dimension of data generation with the cooperation of the Smart Elderly Healthcare (SEH) institution, the privacy of EHR data of the elderly is well protected. The security analysis demonstrates that our scheme can achieve EHR ciphertext indistinguishability under chosen-plaintext attacks and token unlinkability and unforgeability under data users’ collusion attacks. The experimental results show that our scheme performs well in terms of time cost and computational overhead.

As an integral component of the space-air-ground integrated network (SAGIN), the low Earth orbit (LEO) satellite network has displayed immense potential in providing ubiquitous connectivity and broadband mobile communication. However, the intrinsic dynamics of LEO satellites pose unprecedented challenges in network management and service delivery. In this paper, we investigate the service function chain (SFC) orchestration in dynamic LEO satellite networks to achieve flexible and efficient service provision. Considering the service requirements and the limitations of network resources, we formulate the SFC orchestration problem as the integer nonlinear programming (INLP) problem for maximizing the service acceptance and the load fairness of satellites. Then, an efficient heuristic algorithm is proposed to solve this problem. Addressing the situation with frequent service requests, a graph attention network (GAT)-based approach with low complexity is also presented. Simulation results demonstrate that our proposed approaches outperform the benchmarks by a substantial margin in terms of load fairness and service acceptance. Besides, the proposed GAT-based approach shows its advantage in computation complexity, and exhibits robustness in unstable network scenarios with intermittent link interruptions.

Device-edge collaboration on deep neural network (DNN) inference is a promising approach to efficiently utilizing network resources for supporting Artificial Intelligence of Things (AIoT) applications. In this article, we propose a novel digital twin (DT)-assisted approach to device-edge collaboration on DNN inference that determines whether and when to stop local inference at a device and upload the intermediate results to complete the inference on an edge server. Instead of determining the collaboration for each DNN inference task only upon its generation, multi-step decision making is performed during the on-device inference to adapt to the dynamic computing workload status at the device and the edge server. To enhance the adaptivity, a DT is constructed to evaluate all potential offloading decisions for each DNN inference task, which provides augmented training data for a machine learning-assisted decision-making algorithm. Then, another DT is constructed to estimate the inference status at the device to avoid frequently fetching the status information from the device, thus reducing the signaling overhead. We also derive necessary conditions for optimal offloading decisions to reduce the offloading decision space. Simulation results demonstrate the outstanding performance of our DT-assisted approach in terms of balancing the tradeoff among inference accuracy, delay, and energy consumption.

Timely and energy-efficient task offloading and computing can be challenging in mobile edge computing (MEC) networks when the communication links between devices and edge servers are unreliable. In this paper, we apply multiple aerial intelligent reflective surfaces (AIRSs) to assist devices in offloading computing tasks to the edge server in a timely and reliable manner in the MEC network with poor offloading environments. To evaluate the timeliness of offloading and computing, we derive the evolution process of age-of-information (AoI) under the random arrival of the computing tasks. The association between devices and AIRSs, offloading order of computing tasks, design of IRS phase shift, and allocation of communication and computing resources are jointly optimized to minimize the average AoI and system energy consumption given computing requirements. To solve the formulated minimization problem, we propose an efficient problem-solving framework to cope with the challenge of variable coupling. Firstly, we derive a closed-form optimal IRS phase shift to provide a reliable offloading environment. Then, we optimize the association between devices and AIRSs while reducing the offloading complexity and balancing the number of devices associated with each AIRS. Finally, we develop a low-complexity task offloading and resource allocation algorithm based on convex optimization to attain a good enough solution. Simulation results indicate the proposed solution outperforms benchmarks in timeliness and energy saving.

The increasing focus on privacy and security in 6G networks, which are intelligence-native, necessitates the use of quantum key distribution-secured semantic information communication (QKD-SIC) to protect confidential data. In QKD-SIC systems, edge devices connected via quantum channels can efficiently encrypt semantic information from the semantic source, and securely transmit the encrypted semantic information to the semantic destination. In this article, we consider an efficient resource (i.e., quantum key distribution (QKD) and KM wavelengths) sharing problem to support QKD-SIC systems under the uncertainty of semantic information generated by edge devices. In such a system, QKD service providers offer QKD services with different subscription options to the edge devices. The QKD services are envisioned to follow cloud computing that has the subscription in the reservation and on-demand options, i.e., for long and short (immediate) terms, respectively. As such, to reduce the cost for the edge device users, we propose a QKD resource management framework for the edge devices communicating semantic information. The framework is based on a two-stage stochastic optimization model to achieve optimal QKD deployment. Moreover, to reduce the deployment cost of QKD service providers, QKD resources in the proposed framework can be utilized based on efficient QKD-SIC resource management, including semantic information transmission among edge devices, secret-key provisioning, and cooperation formation among QKD service providers. In detail, the formulated two-stage stochastic optimization model can achieve the optimal QKD-SIC resource deployment while meeting the secret-key requirements for semantic information transmission of edge devices. Moreover, to share the cost of the QKD resource pool among cooperative QKD service providers forming a coalition in a fair and interpretable manner, the proposed framework leverages the concept of Shapley value from cooperative game theory as a solution. Experimental results demonstrate that the proposed framework can reduce the deployment cost by about 40% compared with existing noncooperative baselines.

In Part I of this two-part paper (Multitimescale Control and Communications with deep reinforcement learning (DRL)—Part I: Communication-Aware Vehicle Control), we decomposed the multitimescale control and communications (MTCCs) problem in cellular vehicle-to-everything (C-V2X) system into a communication-aware DRL-based platoon control (PC) subproblem and a control-aware DRL-based radio resource allocation (RRA) subproblem. We focused on the PC subproblem and proposed the MTCC-PC algorithm to learn an optimal PC policy given an RRA policy. In this article (Part II), we first focus on the RRA subproblem in MTCC assuming a PC policy is given, and propose the MTCC-RRA algorithm to learn the RRA policy. Specifically, we incorporate the PC advantage function in the RRA reward function, which quantifies the amount of PC performance degradation caused by observation delay. Moreover, we augment the state space of RRA with PC action history for a more well-informed RRA policy. In addition, we utilize reward shaping and reward backpropagation prioritized experience replay (RBPER) techniques to efficiently tackle the multiagent and sparse reward problems, respectively. Finally, a sample- and computational-efficient training approach is proposed to jointly learn the PC and RRA policies in an iterative process. In order to verify the effectiveness of the proposed MTCC algorithm, we performed experiments using real driving data for the leading vehicle, where the performance of MTCC is compared with those of the baseline DRL algorithms.

This article introduces an on-demand collaborative sensing scheme for industrial Internet of Things (IIoT) sensors in time-varying sensing environments, aiming to optimize the sensing performance by effectively allocating communication resources for sensory data sharing. Particularly, we propose a novel digital twins (DTs)-empowered resource allocation solution to facilitate scalable and flexible collaborative sensing. First, DTs create mathematical models using real-time network data to characterize the dynamic resource demands in collaborative sensing. Second, the performance of mathematical models in DTs is evaluated through data-driven methods. Building on our DT design, we propose a joint collaborative sensing and DT management scheme to optimize the resource allocation for sensory data sharing and DT operation. Furthermore, we develop a DT evaluation method featuring a variational autoencoder to evaluate the accuracy of DTs and enable closed-loop DT-based resource allocation. Numerical results demonstrate the effectiveness of our proposed collaborative sensing scheme in optimizing the sensing performance for all sensors.

An intelligent decision-making system enabled by vehicle-to-everything (V2X) communications is essential to achieve safe and efficient autonomous driving (AD), where two types of decisions have to be made at different timescales, i.e., vehicle control and radio resource allocation (RRA) decisions. The interplay between RRA and vehicle control necessitates their collaborative design. In this two-part paper (Part I and Part II), taking platoon control (PC) as an example use case, we propose a joint optimization framework of multitimescale control and communications (MTCCs) MTCCs based on deep reinforcement learning (DRL). In this article (Part I), we first decompose the problem into a communication-aware DRL-based PC subproblem and a control-aware DRL-based RRA subproblem. Then, we focus on the PC subproblem assuming an RRA policy is given, and propose the MTCC- PC algorithm to learn an efficient PC policy. To improve the PC performance under random observation delay, the PC state space is augmented with the observation delay and PC action history. Moreover, the reward function with respect to the augmented state is defined to construct an augmented state Markov decision process (MDP). It is proved that the optimal policy for the augmented state MDP is optimal for the original PC problem with observation delay. Different from most existing works on communication-aware control, the MTCC- PC algorithm is trained in a delayed environment generated by the fine-grained embedded simulation of cellular vehicle-to-everything communications rather than by a simple stochastic delay model. Finally, experiments are performed to compare the performance of MTCC- PC with those of the baseline DRL algorithms.

The low Earth orbit (LEO) satellite-assisted communications are envisioned as a prospective solution in next-generation networks to provide reliable, flexible, cost-effective, and globally seamless services. In this paper, we investigate satellite-terrestrial vehicular network (STVN) supporting connected autonomous vehicle (CAV) applications anytime and anywhere. We first establish a model for the LEO satellite-CAV communication system with different satellite orbital parameters. Then the LEO satellite-CAV communication performance in terms of service availability, outage probability, and system throughput is analyzed when considering practical satellite constellations. Furthermore, the impact of different terrestrial infrastructure deployment strategies on the STVN performance is investigated. Extensive numerical results are provided to validate our theoretical analysis and demonstrate the improvement of CAV network performance thanks to LEO satellites in the STVN.

This paper proposes a novel knowledge-driven approach for resource allocation in wireless networks using the graph neural network (GNN) architecture. To meet the millisecond-level timeliness and scalability required for the dynamic network environment, our proposed approach, named UWGNN, incorporates the deep unrolling of the weighted minimum mean square error (WMMSE) algorithm, referred to as domain knowledge, into GNN, thereby reducing computational delay and sample complexity while adapting to various data distributions. Specifically, by unrolling WMMSE algorithm into a series of interconnected submodules, UWGNN aligns closely with the optimization steps of the algorithm. Our analysis reveals the effectiveness of the deep unrolling method within UWGNN, which decomposes complicated end-to-end mappings, leading to a reduction in model complexity and parameter count. Experimental results demonstrate that UWGNN maintains optimal performance with computation latency 3 to 4 orders of magnitude lower than the WMMSE algorithm and exhibits strong performance and generalization across diverse data distributions and communication topologies without the need for retraining. Our findings contribute to the development of efficient and scalable wireless resource management solutions for distributed and dynamic networks with strict latency requirements.

Edge-device collaboration has the potential to facilitate compute-intensive device pose tracking for resource-constrained mobile augmented reality (MAR) devices. In this article, we devise a 3-D map management scheme for edge-assisted MAR, wherein an edge server constructs and updates a 3-D map of the physical environment by using the camera frames uploaded from an MAR device, to support local device pose tracking. Our objective is to minimize the uncertainty of device pose tracking by periodically selecting a proper set of uploaded camera frames and updating the 3-D map. To cope with the dynamics of the uplink data rate and the user’s pose, we formulate a Bayes-adaptive Markov decision process problem and propose a digital twin (DT)-based approach to solve the problem. First, a DT is designed as a data model to capture the time-varying uplink data rate, thereby supporting 3-D map management. Second, utilizing extensive generated data provided by the DT, a model-based reinforcement learning algorithm is developed to manage the 3-D map while adapting to these dynamics. Numerical results demonstrate that the designed DT outperforms Markov models in accurately capturing the time-varying uplink data rate, and our devised DT-based 3-D map management scheme surpasses benchmark schemes in reducing device pose tracking uncertainty.

The metaverse is envisioned to create immersive and virtual worlds for people to experience interoperable 3D applications. However, the real-time, interactive, and multimedia characteristics of the metaverse applications require strict quality-of-service (QoS) on the underlying networking architecture, including high throughput, ultra-low delay, and human-centric service configurations. Network function virtualization (NFV)-enabled networking resource management can provide a promising solution to service-oriented QoS satisfaction for metaverse users. In this paper, we propose a blockchain-based collaborative and verifiable virtualized network function (VNF) management scheme for metaverse, named BVNF+. BVNF+ enables multiple network providers across different trust domains to abstract their services as VNFs and collaboratively manage end-to-end network slices for human-centric network services in metaverse. To address the design challenge of balancing the on-chain and off-chain overheads, we decouple the computations of VNF queries into modular components based on software and hardware verifiable computation (vc) approaches. Our modular strategy can achieve on/off-chain computation and communication efficiency while keeping low usage of the secure hardware. We conduct security analysis and extensive experiments based on a real-world blockchain testing network. The analysis and experimental results demonstrate that BVNF+ is both secure and efficient as compared with the existing works.

In this paper, we address the problem of designing incentive mechanisms by a virtual service provider (VSP) to hire sensing IoT devices to sell their sensing data to help creating and rendering the digital copy of the physical world in the Metaverse. Due to the limited bandwidth, we propose to use semantic extraction algorithms to reduce the delivered data by the sensing IoT devices. Nevertheless, mechanisms to hire sensing IoT devices to share their data with the VSP and then deliver the constructed digital twin to the Metaverse users are vulnerable to adverse selection problem. The adverse selection problem, which is caused by information asymmetry between the system entities, becomes harder to solve when the private information of the different entities are multi-dimensional. We propose a novel iterative contract design and use a new variant of multi-agent reinforcement learning (MARL) to solve the modelled multi-dimensional contract problem. To demonstrate the effectiveness of our algorithm, we conduct extensive simulations and measure several key performance metrics of the contract for the Metaverse. Our results show that our designed iterative contract is able to incentivize the participants to interact truthfully, which maximizes the profit of the VSP with minimal individual rationality (IR) and incentive compatibility (IC) violation rates. Furthermore, the proposed learning-based iterative contract framework has limited access to the private information of the participants, which is to the best of our knowledge, the first of its kind in addressing the problem of adverse selection in incentive mechanisms.

Physical layer covert communication is a crucial secure communication technology that enables a transmitter to convey information covertly to a recipient without being detected by adversaries. Unlike typical cryptography and physical layer security systems that concentrate on protecting the sent signal content, covert communications seek to conceal the existence of legitimate transmission. Thus, with beyond fifth-generation (B5G) wireless communications, covert communications can operate in tandem or as a supplement to conventional security techniques. We provide an extensive overview of the basic theories and several strategies in physical layer covert communications in this article. In particular, we go into great detail about the basic theories of physical layer covert communications, such as channel models, codes, secret keys, and covertness metrics, as well as various covert schemes in progressively more complicated scenarios, such as covert communications in single-antenna and multiantenna three-node systems and covert communications in jammer-and relay-aided systems. In addition, we identify the challenges and future directions for research on covert communications in B5G wireless networks.

An introduction of intelligent interconnectivity for people and things has posed higher demands and more challenges for sixth-generation (6G) networks, such as high spectral efficiency and energy efficiency (EE), ultralow latency, and ultrahigh reliability. Cell-free (CF) massive multiple-input-multiple-output (mMIMO) and reconfigurable intelligent surface (RIS), also called intelligent reflecting surface (IRS), are two promising technologies for coping with these unprecedented demands. Given their distinct capabilities, integrating the two technologies to further enhance wireless network performances has received great research and development attention. In this article, we provide a comprehensive survey of research on RIS-aided CF mMIMO wireless communication systems. We first introduce system models focusing on system architecture and application scenarios, channel models, and communication protocols. Subsequently, we summarize the relevant studies on system operation and resource allocation, providing in-depth analyses and discussions. Following this, we present practical challenges faced by RIS-aided CF mMIMO systems, particularly those introduced by RIS, such as hardware impairments (HIs) and electromagnetic interference (EMI). We summarize the corresponding analyses and solutions to further facilitate the implementation of RIS-aided CF mMIMO systems. Furthermore, we explore an interplay between RIS-aided CF mMIMO and other emerging 6G technologies, such as millimeter wave (mmWave) and terahertz (THz), simultaneous wireless information and power transfer (SWIPT), next-generation multiple access (NGMA), and unmanned aerial vehicle (UAV). Finally, we outline several research directions for future RIS-aided CF mMIMO systems.

In a blockchain-based data trading platform, data users can purchase data sets and computing power through encrypted smart contracts. The security of smart contracts is important as it relates to that of the data platform. However, due to the inability to apply to detection rules with complex structures and the inefficiency of detection, existing malicious code detection methods are not suitable for the encrypted smart contracts in blockchain-based data trading platforms with high transaction rate requirements. In this paper, a practical and privacy-preserving malicious code detection method is proposed for encrypted smart contract in blockchain-based data trading platform. Specifically, we design two kinds of miners to act as the malicious rule processor and the detector respectively for inspecting the encrypted smart contract. The rule processor generates an obfuscated map with the original open-source malicious rule set. The detector performs a malicious inspection algorithm by inputting the obfuscated map and the randomized tokens, where the latter is generated from smart contract. Then, we theoretically analyze the security syntax of the proposed method. The analysis results demonstrate the proposed scheme can achieve$\mathcal {L}$-secure against adaptive attacks. Extensive experiments are carried out through the open-source real rule sets, which show that the proposed scheme can reduce communication time and communication overhead.

The ubiquitous data collection has raised potential risks of leaking physical and private attribute information associated with individuals in a collected dataset. A data collector who wants to collect data for provisioning its machine learning (ML)-based services requires establishing a privacy-preserving data collection protocol for data owners. In this work, we design, implement, and evaluate a novel privacy-preserving data collection protocol. Specifically, we validate the functionality of the data collection protocol on behalf of data owners. First, the ML-based services are not always predefined, it is challenging for a data collector to combat inference of private attributes and user identity from the collected data while maintaining the utility of data. To address the challenge, we reconstruct the data by designing a data transformation model based on the autoencoder and clustering. Second, it is necessary to ensure that the reconstructed data satisfy certain privacy-preserving properties as untrusted data collectors can provide the data transformation models. Therefore, we utilize detection models and design an efficient enclave-based mechanism to validate that the reconstructed data's private attribute estimation probability is bounded by the predefined thresholds. Extensive experiments demonstrate our protocol's effectiveness, such as significantly reducing the accuracy of private attribute detection

In this paper, we propose a multi-client secure and efficient keyword search scheme for cloud storage, which is built upon distributed point function (DPF). Specifically, outsourced keyword indexes are encoded by using garbled bloom filter and cuckoo filter, instead of bloom filter adopted by most of the state-of-the-art DPF-based schemes. In this way, clients can apply cuckoo hashing into DPF and utilize a segmentation method to interact with cloud servers for keyword search, and servers can obliviously aggregate DPF evaluation results to perform the search. Accordingly, the computational complexity at server side can be significantly reduced. Furthermore, the proposed scheme preserves constant downlink overheads, which is more communication-efficient for multi-keyword conjunctive search. To achieve privacy preservation and access control for multiple clients, we propose a double encryption method to encrypt outsourced indexes and correspondingly put forward an authorization algorithm from set-constrained pseudorandom functions by which fine-grained search-authorized keys can be generated, and collusion attacks among clients are addressed by integrating Wegman-Carter message authentication codes and cover-free systems. Since our scheme is designed under both semi-honest and malicious models (i.e., malicious servers may return incorrect query results), we use a simulation-based proof to formally demonstrate its security properties. Finally, we develop a proof-of-concept prototype and perform extensive experiments to show our scheme's practicality and efficiency in terms of computation, communication, and storage overheads.

In this paper, we explore data poisoning attacks and their defenses in local differential privacy (LDP)-based crowdsensing systems. First, we construct data poisoning attacks launched by corrupted workers to subvert crowdsensing results by tampering information reported. Specifically, the attacks are formulated as a bi-level optimization problem where attackers strive to conceal their malicious behavior by delicately exploiting noise perturbation introduced by LDP protocols. In this way, the attacks can not be detected, even with the weight-based truth discovery methods. Due to the NP-hard nature of the bi-level problem, we decompose it into upper-level and lower-level sub-problems and employ the augmented Lagrangian method to iteratively solve them, ultimately identifying optimal attack strategies. Second, we propose corresponding countermeasures to defend against the attacks. The countermeasures are formulated as a minimization problem, with the objective of minimizing disruptions caused by attacks through the identification and removal of corrupted workers from crowdsensing systems. To solve the problem, we utilize a differential evolution algorithm instead of gradient-based methods since the objective function of the problem is not differentiable. Extensive experiments on real-world datasets are conducted to evaluate the performance of the proposed attacks and defenses. The evaluation results demonstrate that LDP perturbation indeed facilitates the success of data poisoning attacks, and the proposed defenses can accurately distinguish malicious behaviors disguised.

The emergence of cloud computing enables various healthcare institutions to outsource pre-diagnostic models and provide timely and convenient services for patients. However, healthcare institutions and patients have serious concerns about potential privacy leakage as cloud servers cannot be fully trusted. In this paper, a privacy-preserving cloud-assisted medical pre-diagnosis scheme, named NAIAD, is proposed, where patients can securely query the outsourced model and obtain their pre-diagnostic results. Specifically, the pre-diagnostic model is constructed on$k$-Nearest Neighbor ($k$NN), and Mahalanobis Distance (MD) is chosen as the similarity metric to achieve high accuracy. Accordingly, a secure MD-based comparison method (SMDC) is designed based on a matrix encryption technique. The method is a basic module of NAIAD that enables cloud servers to compare encrypted medical records and achieve privacy-preserving$k$NN-based pre-diagnosis with linear complexity. To further improve the computational efficiency, medical records are first clustered and encrypted to construct a hierarchical index tree, then patients can query the tree to speed up the query process. Detailed security analysis indicates NAIAD can resist closeness-same-pattern chosen-plaintext attack, and extensive experiments on real-world and synthetic databases demonstrate NAIAD has high query efficiency and pre-diagnosis accuracy.

A reliable driving trajectory prediction of surrounding vehicles is an essential reference for decision-making and safe driving of an autonomous vehicle. Although predicting short-term trajectories can be well achieved, it is still very challenging for long-term prediction of trajectories since the prediction space grows exponentially. In this paper, we propose a novel architecture for trajectory prediction from factored intention estimation (I2T), which decouples the trajectory prediction space into a high-level space for intention estimation and a low-level space for motion prediction. The long-term dependencies between intention cues and future motions during driving are naturally extended to the internal sharing mechanism of I2T, leading to improved performance. Furthermore, we design a Prioriformer model to serve as the backbone network for I2T so that it can accurately capture the long-term dependency couplings related to the task of intention estimation or motion prediction. Prioriformer model adopts a personalized normalization method, which facilitates learning latent representations of long-term features and avoids getting stuck on local optimum. A designed multi-scale fusion encoder extracts features from various receptive fields and then learns richer information from the representation subspaces. An efficient non-autoregressive decoder reduces the pressure in long-term prediction of trajectories while avoiding cumulative errors. Experiments on three real-world motion datasets show that I2T can significantly outperform the state-of-the-art.

This artilce aims to develop a differential private federated learning (FL) scheme with the least artificial noises added while minimizing the energy consumption of participating mobile devices. By observing that some communication efficient FL approaches and even the nature of wireless communications contribute to the differential privacy (DP) preservation of training data on mobile devices, in this paper, we propose to jointly leverage gradient compression techniques (i.e., gradient quantization and sparsification) and additive white Gaussian noises (AWGN) in wireless channels to develop a piggyback DP approach for FL over mobile devices. Even with the piggyback DP approach, information distortion caused by gradient compression and noise perturbation may slow down FL convergence, which in turn consumes more energy of mobile devices for local computing and model update communications. Thus, we theoretically analyze FL convergence and formulate an energy efficient FL optimization under piggyback DP, transmission power, and FL convergence constraints. Furthermore, we propose an efficient iterative algorithm where closed-form solutions for artificial DP noise and power control are derived. Extensive simulation and experimental results demonstrate the effectiveness of the proposed scheme in terms of energy efficiency and privacy preservation.

Due to the limited computing resource and battery capability at the mobile devices, the computation-intensive tasks generated by mobile devices can be offloaded to edge servers or cloud for processing. In this paper, we study the multi-user task offloading problem in an end-edge-cloud system, in which all user devices compete for the limited communication and computing resources. Particularly, we first formulate the offloading problem with the goal of maximizing the Quality of Experience (QoE) of the users subject to resource constraints. Since each user focuses on maximizing its own QoE, we reformulate the problem as a Multi-User Task Offloading Game (MUTO-Game). We then identify an important property that for any device, both the communication interference and the degree of computing resource competition can be upper bounded. Based on the property, we further theoretically prove that there exists at least one Nash Equilibrium offloading strategy in the MUTO-Game. We propose the Game-based Decentralized Task Offloading (GDTO) approach to obtain the Nash Equilibrium offloading strategy. Finally, we analyze the upper bound for the convergence time and characterize the performance guarantee of the obtained offloading strategy for the worst case. A series of experimental results are presented, in comparison with both the centralized optimal approach and the approximate approaches.

Mobile edge computing is a promising computing paradigm enabling mobile devices to offload computation-intensive tasks to nearby edge servers. However, within small-cell networks, the user mobilities can result in uneven spatio-temporal loads, which have not been well studied by considering adaptive load balancing, thus limiting the system performance. Motivated by the data analytics and observations on a real-world user association dataset in a large-scale WiFi system, in this paper, we investigate the mobility-aware online task offloading problem with adaptive load balancing to minimize the total computation costs. However, the problem is intractable directly without prior knowledge of future user mobility behaviors and spatio-temporal computation loads of edge servers. To tackle this challenge, we transform and decompose the original task offloading optimization problem into two sub-problems, i.e., task offloading control (ToC) and server grouping (SeG). Then, we devise an online control scheme, namedMOTO(i.e.,Mobility-awareOnlineTaskOffloading), which consists of two components, i.e., Long Short Term Memory based algorithm and Dueling Double DQN based algorithm, to efficiently solve theToCandSeGsub-problems, respectively. Extensive trace-driven experiments are carried out and the results demonstrate the effectiveness ofMOTOin reducing computational costs of mobile devices and achieving load balancing when compared to the state-of-the-art benchmarks.

In this paper, we investigate dense short packet forwarding for clustering-based massive Internet-of-Things (mIoT). The objective is to support the data forwarding with minimal communication overhead while satisfying the differentiated latency constraints from the transport layer perspective. To this end, we propose a dense packet assembling scheme, named ShuttleBus, for forwarding devices in mIoT to achieve effective data merging. The assembling scheme is designed based on the stream multiplexing mechanism of the Quick UDP Internet Connection (QUIC) protocol. With ShuttleBus, the payload data sent from IoT devices are extracted as independent frames belonging to different data streams. The ShuttleBus can bundle data frames from multiple streams into a single packet while ensuring data integrity of these streams. Furthermore, we develop a resilient packing mechanism in packet assembling to merge data received from IoT devices within a cluster. In addition, a latency-oriented scheduling mechanism for backlogged QUIC data is established to guarantee satisfactory delivery of diverse transmission tasks. To accommodate the dynamic network environment, we tailor a learning-based algorithm to determine the optimal packet assembling time adaptively. We evaluate the performance of ShuttleBus under various network load conditions. Both analytical and experimental results demonstrate that the proposed scheme significantly reduces communication overhead and enhances data delivery performance under stringent latency constraints.

In this paper, we propose a privacy-preserving range-based positioning scheme, named PPRP, which can preserve the location privacy of both user equipment (UE) and anchors (ACs) in mobile networks. Specifically, PPRP is established on a decentralized trust-based framework that divides trust between two location management function (LMF) servers. With such a framework, UE and ACs are allowed to securely upload their range/range-difference measurement data to LMF servers using lightweight additive secret sharing techniques (ASS) instead of cumbersome cryptographic operations. Then, PPRP takes secret-shared measurement data as inputs and decomposes UE's location estimation procedures into secure two-party matrix computation sub-protocols, which are elaborately crafted using somewhat homomorphic encryption and randomization techniques to ensure both efficiency and privacy preservation in positioning. Furthermore, to mitigate the negative effects arising from non-line-of-sight (NLoS) ACs, PPRP achieves privacy-preserving residual-based NLoS analysis. To this end, we additionally propose a series of secure two-party sub-protocols to support various non-linear functions, including comparison, division, square root computation, oblivious shuffle and sorting. These sub-protocols serve as fundamental modules that can be effectively combined to perform sophisticated operations of NLoS analysis in a privacy-preserving manner. A comprehensive simulation-based security analysis demonstrates that PPRP can achieve location privacy preservation. Finally, we develop a proof-of-concept prototype and conduct extensive experiments to show PPRP's high performance in terms of positioning accuracy, computational efficiency, and communication complexity.

With the explosive development of mobile computing, federated learning (FL) has been considered as a promising distributed training framework for addressing the shortage of conventional cloud based centralized training. In FL, local model owners (LMOs) individually train their respective local models and then upload the trained local models to the task publisher (TP) for aggregation to obtain the global model. When the data provided by LMOs do not meet the requirements for model training, they can recruit workers to collect data. In this paper, by considering the interactions among the TP, LMOs and workers, we propose a three-layer hierarchical game framework. However, there are two challenges. First, information asymmetry between workers and LMOs may result in that the workers hide their types. Second, incentive mismatch between TP and LMOs may result in a lack of LMOs’ willingness to participate in FL. Therefore, we decompose the hierarchical-based framework into two layers to address these challenges. For the lower-layer, we leverage the contract theory to ensure truthful reporting of the workers’ types, based on which we simplify the feasible conditions of the contract and design the optimal contract. For the upper-layer, the Stackelberg game is adopted to model the interactions between the TP and LMOs, and we derive the Nash equilibrium and Stackelberg equilibrium solutions. Moreover, we develop an iterativeHierarchical-basedUtilityMaximizationAlgorithm (HUMA) to solve the coupling problem between upper-layer and lower-layer games. Extensive numerical experimental results verify the effectiveness of HUMA, and the comparison results illustrate the performance gain of HUMA.

Distributed Artificial Intelligence (AI) model training over mobile edge networks encounters significant challenges due to the data and resource heterogeneity of edge devices. The former hampers the convergence rate of the global model, while the latter diminishes the devices' resource utilization efficiency. In this paper, we propose a generative AI-empowered federated learning to address these challenges by leveraging the idea of FIlling the MIssing (FIMI) portion of local data. Specifically, FIMI can be considered as a resource-aware data augmentation method that effectively mitigates the data heterogeneity while ensuring efficient FL training. We first quantify the relationship between the training data amount and the learning performance. We then study the FIMI optimization problem with the objective of minimizing the device-side overall energy consumption subject to required learning performance constraints. The decomposition-based analysis and the cross-entropy searching method are leveraged to derive the solution, where each device is assigned suitable AI-synthetic data and resource utilization policy. Experiment results demonstrate that FIMI can save up to 50% of the device-side energy to achieve the target global test accuracy in comparison with the existing methods. Meanwhile, FIMI can significantly enhance the converged global accuracy under the non-independently-and-identically distribution (non-IID) data.

Mobile AI-Generated Content (AIGC) has achieved great attention in unleashing the power of generative AI and scaling the AIGC services. By employing numerous Mobile AIGC Service Providers (MASPs), ubiquitous and low-latency AIGC services for clients can be realized. Nonetheless, the interactions between clients and MASPs in public mobile networks, pertaining to three key mechanisms, namely MASP selection, payment scheme, and fee-ownership transfer, are unprotected. In this paper, we design the above mechanisms in a systematic approach and present the first blockchain to protect mobile AIGC, called ProSecutor. Specifically, by roll-up and layer-2 channels, ProSecutor forms a two-layer architecture, realizing tamper-proof data recording and atomic fee-ownership transfer with high resource efficiency. Then, we present the Objective-Subjective Service Assessment(OS2)framework, which effectively evaluates the AIGC services by fusing the objective service quality with the reputation-based subjective experience of the service outcome (i.e., AIGC outputs). DeployingOS2on ProSecutor, firstly, the MASP selection can be realized by sorting the reputation. Afterward, the contract theory is adopted to optimize the payment scheme and help clients avoid moral hazards in mobile networks. We implement the prototype of ProSecutor on BlockEmulator. Extensive experiments demonstrate that ProSecutor achieves 12.5× throughput and saves 67.5% storage resources compared with BlockEmulator. Moreover, the effectiveness and efficiency of the proposed mechanisms are validated.

Employing massive Mobile AI-Generated Content (AIGC) Service Providers (MASPs) with powerful models, high-quality AIGC services become accessible for resource-constrained end users. However, this advancement, referred to as mobile AIGC, also introduces a significant challenge: users should download large AIGC outputs from the MASPs, leading to substantial bandwidth consumption and potential transmission failures. In this paper, we apply cross-modalGenerativeSemanticCommunications (G-SemCom) in mobile AIGC to overcome wireless bandwidth constraints. Specifically, we utilize cross-modal attention maps to indicate the correlation between user prompts and each part of AIGC outputs. In this way, the MASP can analyze the prompt context and filter the most semantically important content efficiently. Only semantic information is transmitted, with which users can recover the entire AIGC output with high quality while saving mobile bandwidth. Since the transmitted information not only preserves the semantics but also prompts the recovery, we formulate a joint semantic encoding and prompt engineering problem to optimize the bandwidth allocation among users. Particularly, we present a human-perceptual metric named Joint Perceptual Similarity and Quality (JPSQ), which is fused by two learning-based measurements regarding semantic similarity and aesthetic quality, respectively. Furthermore, we develop the Attention-aware Deep Diffusion (ADD) algorithm, which learns attention maps and leverages the diffusion process to enhance the environment exploration ability of traditional deep reinforcement learning (DRL). Extensive experiments demonstrate that our proposal can reduce the bandwidth consumption of mobile users by 49.4% on average, with almost no perceptual difference in AIGC output quality. Moreover, the ADD algorithm shows superior performance over baseline DRL methods, with 1.74× higher overall reward.

In this paper, we propose an efficient and distributed service access control framework (E-DAC) in the pervasive edge computing (PEC) environment, where the resources of peer devices at the network edge are integrated to provide latencysensitive computing services to the nearby devices on behalf of edge servers. E-DAC addresses the challenge of efficient and distributed service access control, comprising edge service authorization, service access authorization, and mutual authentication between edge servers and edge devices. In dong so, E-DAC first extends a key-aggregate cryptosystem to enable batch service authorization, in which a service provider can aggregate the authorization keys of different services to produce a constant-size aggregate key for an edge server. Second, E-DAC enables users to acquire authorization from the service provider for service access on edge servers by using efficient secret sharing. Third, edge servers and users can authenticate with each other without interacting with a centralized server, while enabling secure zero-round trip communication, so that the service data is protected and the communication bandwidth cost is low. In addition, the service provider is capable of efficiently revoking the authorization of the dropout or compromised edge servers or users in response to the dynamics of the PEC environment. Finally, we prove the security of service access control in E-DAC, including unforgeability of service authorization and confidentiality of service data, and conduct extensive analysis and experiments to demonstrate that E-DAC is highly computational and communication-efficient on service authorization, authentication, and revocation.

By providing users with an immersive visual and acoustic experience, virtual reality (VR) serves as a foundational technique for the emerging metaverse. One of the most promising aspects of VR is its ability to protect users’ identities by transforming their physical appearances into avatars with arbitrary appearances in the virtual world. However, the increasing threat of de-anonymization attacks that seek to reveal users’ identities poses significant privacy risks. We propose AvatarHunter, a non-intrusive and user-unaware de-anonymization attack leveraging victims’ inherent movement signatures. AvatarHunter discreetly collects the avatar's gait information by recording videos in the VR scenario without requiring any permissions. Notably, we designed a Unity-based feature extractor that maintains the avatar's movement signature while enabling AvatarHunter to be resistant to changes in the avatar's appearance. We conduct real-world experiments on VRChat to evaluate AvatarHunter's effectiveness. The results demonstrate that in commercial settings, AvatarHunter achieves attack success rates (ASR) of 92.1% and 66.9% in closed-world and open-world avatar scenarios, respectively, significantly surpassing existing benchmarks. Additionally, simulations using an open-source dataset confirm that AvatarHunter can attain over 78% ASR in full-body tracking scenarios. Finally, we discuss several countermeasures and implement an obfuscation mechanism during the avatar rendering phase, significantly reducing the ASR.

To enable ubiquitous Artificial Intelligence (AI) in the next-generation wireless communications networks, computation-intensive tasks such as data processing and model training have to be performed by energy-constrained end users. In this paper, we present a hybrid coded edge computing network whereby users can choose to complete their computation task through: i) local computation with the wireless power transfer derived from base stations, ii) coded edge offloading, or iii) hybrid computation involving edge offloading and local computation. To minimize the overall network cost, we propose a stochastic resource optimization approach. Given the stochastic nature of wireless charging efficiency and edge servers computation capacities, which can only be observedex-post, a computation strategy for each user is determined using the two-stage stochastic integer programming (SIP). To address the complexity of the SIP problem which scales with the size of the network, we introduce the efficient computation methods of Benders’ decomposition and sample average approximation. Besides, we present a special case of$z$-stage stochastic offloading optimization that is applicable when the corrective edge offloading action can be executed in multiple stages, e.g., for non-time-sensitive tasks that do not need to be completed by stage two. Finally, we provide extensive sensitivity analyses to evaluate the performance of the proposed cost minimization approach amid varying network parameters. We demonstrate that our approach outperforms deterministic optimization approaches for in-network cost minimization.

With the recent development of the Metaverse, people are more connected with each other. Avatars are used to represent the people, to communicate with one another, and they can build the community virtually. In these processes, a massive amount of data is exchanged between the physical and the virtual world. However, the existing communication technologies are insufficient to support the Metaverse, and the energy consumption of the Metaverse is huge. Therefore, semantic communication is one of the emerging communication paradigms to reduce the size of the data transmitted and reduce energy consumption while maintaining its meaning. Virtual service providers (VSPs) who provide services in the Metaverse can purchase semantic data from the nearby edge sensing units by using two subscription plans: reservation and on-demand. However, in practice, the demand of the VSPs is uncertain due to the variability of the Metaverse. To minimize the cost of the network and prevent over- and under-subscription of the resources, we propose a two-phase stochastic semantic resource allocation (SSRA) scheme. In phase one, a double dutch auction performs a one-to-one matching between VSPs and edge sensing units. The matching is dynamic and depends on the quality of experience (QoE) from the Metaverse users and the semantic data transmission cost from the edge sensing units. The matching changes whenever QoE and the semantic data transmission cost vary. In phase two, we consider the demand uncertainty and matching result from the phase one to formulate a distributed robust optimization (DRO) problem to minimize the operation cost of the VSPs. Using a real-world dataset, simulation results demonstrate that our proposed scheme is fully dynamic and minimizes the operation cost/energy consumption of VSPs in the presence of stochastic uncertainties.

Federated learning (FL) has gained increasing attention due to its ability to collaboratively train while protecting client data privacy. However, vanilla FL cannot adapt to client heterogeneity, leading to a degradation in training efficiency due to stragglers, and is still vulnerable to privacy leakage. To address these issues, this paper proposes RingSFL, a novel distributed learning scheme that integrates FL with a model split mechanism to adapt to client heterogeneity while maintaining data privacy. In RingSFL, all clients form a ring topology. For each client, instead of training the model locally, the model is split and trained among all clients along the ring through a pre-defined direction. By properly setting the propagation lengths of heterogeneous clients, the straggler effect is mitigated, and the training efficiency of the system is significantly enhanced. Additionally, since the local models are blended, it is less likely for an eavesdropper to obtain the complete model and recover the raw data, thus improving data privacy. The experimental results on both simulation and prototype systems show that RingSFL can achieve better convergence performance than benchmark methods on independently identically distributed (IID) and non-IID datasets, while effectively preventing eavesdroppers from recovering training data.

With the significant advancements in artificial intelligence (AI) technologies and computational capabilities, generative AI (GAI) has become a pivotal digital content generation technique for offering superior digital services. However, due to the inherent instability of AI models, directing GAI towards the desired output remains a challenging task. Therefore, in this paper, we design a novel framework that utilizeswirelessperception to guideGAI(WiPe-GAI) in delivering AI-generated content (AIGC) service, within resource-constrained mobile edge networks. Specifically, we first propose a new sequential multi-scale perception (SMSP) algorithm to predict user skeleton based on the channel state information (CSI) extracted from wireless signals. This prediction then guides GAI to provide users with AIGC, i.e., virtual character generation. To ensure the efficient operation of the proposed framework in resource constrained networks, we further design a pricing-based incentive mechanism and propose a diffusion model based approach to generate an optimal pricing strategy for the service provisioning. The strategy maximizes the user's utility while incentivizing the participation of the virtual service provider (VSP) in AIGC provision. The experimental results demonstrate the effectiveness of the designed framework in terms of skeleton prediction and optimal pricing strategy generation, outperforming other existing solutions.

As a distributed learning paradigm, federated learning (FL) can be applied in mobile edge computing (MEC) to support real-time artificial intelligence by leveraging edge computation resources while preserving data privacy in the end devices. However, the unpredictable wireless connections between end devices and edge servers in MEC (e.g., frequent handovers and unstable wireless channels) may result in the loss of important model parameters, which slows down the FL training process and degrades the quality of the global model. In this paper, we propose an adaptive collaborative federated learning (ACFL) scheme to accelerate the convergence and improve model reliability by mitigating communication-based parameter loss under a three-layer MEC architecture. First, a dynamic epoch adjustment method is proposed to reduce communication rounds by dynamically adjusting the training epochs in end devices. In addition, to accelerate the FL convergence, we present an edge server collaborative training scheme by leveraging a multi-layer computing architecture, where edge servers utilize their maintained data to collaboratively train models with end devices. Finally, extensive simulations are conducted and show that ACFL can efficiently improve model reliability and accelerate the convergence of the FL process in MEC.

Ultra reliable low latency communication (URLLC) in vehicular networks is crucial for safety-related vehicular applications. Mini-slot with a short packet that carries only a few symbols is used to reduce the transmission time interval and enable quick scheduling for URLLC that requires extremely low latency. However, a single air interface transmission of URLLC packets may fail due to the high mobility of vehicles. Leveraging multi-connectivity technologies, the real-time reliability of URLLC can be greatly enhanced without relying on packet retransmission. In this paper, we propose a multi-connectivity URLLC downlink transmission scheme for vehicular networks, where the URLLC packet is duplicated and transmitted over multiple independent wireless links to improve packet reliability. Specifically, we design a multi-agent cooperative deep reinforcement learning algorithm, called transformer associated proximal policy optimization (TAPPO), to achieve real-time robust power allocation for multi-connectivity URLLC with imperfect channel state information (CSI). The transformer neural network architecture is employed to share the information among multiple links serving the same URLLC user and choose appropriate transmit powers, enabling cooperation to ensure reliability while minimizing inter-cell interference and energy consumption. Extensive simulation results validate the effectiveness of multi-connectivity packet duplication for URLLC and proposed TAPPO for power allocation.

Human digital twin (HDT) is an emerging paradigm that bridges physical twins (PTs) with powerful virtual twins (VTs) for assisting complex task executions in human-centric services. In this paper, we study a two-timescale online optimization for building HDT under an end-edge-cloud collaborative framework. As a unique feature of HDT, we consider that PTs' corresponding VTs are deployed on edge servers, consisting of not only generic models placed by downloading experiential knowledge from the cloud but also customized models updated by collecting personalized data from end devices. To maximize task execution accuracy with stringent energy and delay constraints, and by taking into account HDT's inherent mobility and status variation uncertainties, we jointly and dynamically optimize VTs' construction and PTs' task offloading, along with communication and computation resource allocations. Observing that decision variables are asynchronous with different triggers, we propose a novel two-timescale accuracy-aware online optimization approach (TACO). Specifically, TACO utilizes an improved Lyapunov method to decompose the problem into multiple instant ones, and then leverages piecewise McCormick envelopes and block coordinate descent based algorithms, addressing two timescales alternately. Theoretical analyses and simulations show that the proposed approach can reach asymptotic optimum within a polynomial-time complexity, and demonstrate its superiority over counterparts.

In this paper, we present a three-layer (i.e., device, field, and factory layers) deterministic federated learning (FL) framework, named DetFed, which accelerates collaborative learning process for ultra-reliable and low-latency industrial Internet of Things (IoT) via integrating 6G-oriented Time-sensitive Networks (TSN). Utilizing dispersive local data, industrial IoT devices distributively train a deep neural network (DNN) model, and the updated model parameters are aggregated at their associated field servers every round or at a centralized factory server every a few rounds. Aiming at optimizing the learning accuracy of FL without affecting the co-transmission of burst traffic (e.g., safety-critical traffic), an integrated TSN is considered to establish connections among the three layers, where a cyclic queuing and forwarding mechanism is deployed in each switch to support deterministic model parameter transmission with microsecond-level delay and near-zero packet loss requirements. To improve the FL performance, we formulate a multi-objective stochastic optimization problem to simultaneously maximize the scheduling success ratio and learning accuracy while satisfying the deterministic requirements of delay, jitter, and packet loss. Since the objective function is implicit and the available time slots of the considered TSN in each FL round are temporally correlated, the problem is difficult to solve in real time. Therefore, we transform the problem into a Markov decision process formulation and propose a dynamic resource scheduling algorithm, based on deep reinforcement learning, to make optimal resource scheduling decisions while adapting to device heterogeneity and network dynamics. Experimental results based on real-world dataset demonstrate that the proposed DetFed significantly accelerates FL convergence and improves learning accuracy as compared to state-of-the-art benchmarks.

To maintain high perception performance among connected and autonomous vehicles (CAVs), in this paper, we propose an accuracy-aware and resource-efficient raw-level cooperative sensing and computing scheme among CAVs and road-side infrastructure. The scheme enables fined-grained partial raw sensing data selection, transmission, fusion, and processing in per-object granularity, by exploiting the parallelism among object classification subtasks associated with each object. A supervised learning model is trained to capture the relationship between the object classification accuracy and the data quality of selected object sensing data, facilitating accuracy-aware sensing data selection. We formulate an optimization problem for joint sensing data selection, subtask placement and resource allocation among multiple object classification subtasks, to minimize the total resource cost while satisfying the delay and accuracy requirements. A genetic algorithm based iterative solution is proposed for the optimization problem. Simulation results demonstrate the accuracy awareness and resource efficiency achieved by the proposed cooperative sensing and computing scheme, in comparison with benchmark solutions.

Aerial unmanned vehicles (UAVs) play a significant role in improving the connectivity and coverage of terrestrial communication networks. However, UAV-assisted air-to-ground (A2G) data transmissions usually encounter several fundamental challenges, such as terminal mobility, random nature in channel fading and contention, resource constraints, and application-specific transmission requirements. To tackle these challenges, we formulate a bi-level optimization problem that jointly considers the control of the UAV mobility and transmission power and the scheduling of A2G data transmissions. The objective is to optimize energy consumption and maximize A2G transmission reliability. Particularly, we first theoretically characterize the A2G transmission reliability from a probabilistic perspective concerning the effects of channel fading, channel access contention, and application requirements. We then derive a closed-form expression for the optimal expected transmission reliability. Using the closed-form reliability, we transform the bi-level optimization into a mathematically-tractable optimal control problem and propose an efficient iterative algorithm to solve it. Simulation results show that our approach provides a comprehensive improvement in terms of both energy utilization and A2G transmission reliability, in particular, with a reduction of more than 12.1% in energy consumption and an increase of 7.53% in reliability on average, compared to several baselines.

In distributed IoT data systems, full-size data collection is impractical due to the energy constraints and large system scales. Our previous work has investigated the advantages of integrating matrix sampling and inference for compact distributed IoT data collection, to minimize the data collection cost while guaranteeing the data benefits. This paper further advances the technology by boosting fast and accurate inference for those distributed IoT data systems that are sensitive to computation time, training stability, and inference accuracy. Particularly, we proposeCODE$^{+}$+, i.e.,Compact Distributed IOTData CollEction Plus, which features a cluster-based sampling module and a Convolutional Neural Network (CNN)-Transformer Autoencoders-based inference module, to reduce cost and guarantee the data benefits. The sampling component employs a cluster-based matrix sampling approach, in which data clustering is first conducted and then a two-step sampling is performed in accordance with the number of clusters and clustering errors. The inference component integrates a CNN-Transformer Autoencoders-based matrix inference model to estimate the full-size spatio-temporal data matrix, which consists of a CNN-Transformer encoder that extracts the underlying features from the sampled data matrix and a lightweight decoder that maps the learned latent features back to the original full-size data matrix. We implementCODE$^{+}$+under three operational large-scale IoT systems and one synthetic Gaussian distribution dataset, and extensive experiments are provided to demonstrate its efficiency and robustness. With a 20% sampling ratio,CODE$^{+}$+achieves an average data reconstruction accuracy of 94% across four datasets, outperforming our previous version of 87% and state-of-the-art baseline of 71%.

Constructing earth-fixed cells with low-earth orbit (LEO) satellites in non-terrestrial networks (NTNs) has been the most promising paradigm to enable global coverage. The limited computing capabilities on LEO satellites however render tackling resource optimization within a short duration a critical challenge. Although the sufficient computing capabilities of the ground infrastructures can be utilized to assist the LEO satellite, different time-scale control cycles and coupling decisions between the space- and ground-segments still obstruct the joint optimization design for computing agents at different segments. To address the above challenges, in this paper, a multi-time-scale deep reinforcement learning (DRL) scheme is developed for achieving the radio resource optimization in NTNs, in which the LEO satellite and user equipment (UE) collaborate with each other to perform individual decision-making tasks with different control cycles. Specifically, the UE updates its policy toward improving value functions of both the satellite and UE, while the LEO satellite only performs finite-step rollout for decision-makings based on the reference decision trajectory provided by the UE. Most importantly, rigorous analysis to guarantee the performance convergence of the proposed scheme is provided. Comprehensive simulations are conducted to justify the effectiveness of the proposed scheme in balancing the transmission performance and computational complexity.

To alleviate the spectrum scarcity problem, exploiting the vast available spectrum provided by the Millimeter-Wave (mmWave) frequency band and underlaying cellular network by Device-to-Device (D2D) communication are two promising solutions. In this paper, we focus on D2D-underlaid cellular network, where the D2D communication is performed on a hybrid manner (i.e., operating over either mmWave or microwave frequency band). To secure the hybrid D2D communication against vigilant adversary, we apply covert communication to hide its presence. In particular, the D2D transmitters perform power control and communication mode switch as well as leveraging the cellular signal to avoid the transmission detection by the adversaries. We model the conflict between the D2D transmitters and adversaries in the framework of a two-stage Stackelberg game. The D2D transmitters are the leaders to maximize their utility subject to the constraints on communication covertness at the upper stage. The adversaries are the followers to minimize their detection errors at the lower stage. We apply stochastic geometry to mathematically characterize the network spatial configuration and consider a large-scale D2D-underlaid network, enabling the study from system-level perspective. We analyze the game equilibrium and obtain it by adopting a bi-level algorithm. Numerical results are provided and insightful conclusions are drawn. Compared with the conventional D2D communication, hybrid D2D communication shows a significant advantage regarding throughput under the same security requirement while weak resistance to the more stringent security requirement.

Integrated sensing and communication (ISAC) provides an emerging paradigm for enabling a variety of next-generation wireless services and applications. Due to the limited computation resources on ISAC devices and the latency as well as the reliability requirements, we propose a paradigm of mobile edge computing (MEC) aided ISAC with short-packet transmissions, where multiple ISAC devices adopt short-packet transmissions to offload their sensed radar data to an edge-server for analysis. We adopt the mutual information to measure the performance of radar sensing and quantify the reliability and latency performances for analyzing the radar-data via edge computing. We formulate an energy minimization problem that jointly optimizes the size of each short packet, the duration of each short packet, the computing-capacity allocations of edge-server, the beamforming of the radar sensing and the offloading transmission, while providing guaranteed performances for the radar sensing, the latency for radar-data analysis, and the reliability of offloading transmission. We identify the hierarchical structure of the formulated problem and divide the problem into three subproblems. For both the bottom-layer problem optimizing the computing-capacity allocations of the edge-server and the middle-layer problem optimizing the size of each short packet and the duration of each short packet, we derive their solutions analytically. Finally, for the top-layer problem optimizing the beamforming of the radar sensing and the offloading transmission, we transform it into a difference of convex (DC) problem which can be efficiently solved. We show the performance advantages of our proposed scheme. The simulation results show that our proposed algorithm can outperform the benchmark algorithms.

Multicast short video streaming can enhance bandwidth utilization by enabling simultaneous video transmission to multiple users over shared wireless channels. The existing network management schemes mainly rely on the sequential buffering principle and general quality of experience (QoE) model, which may deteriorate QoE when users’ swipe behaviors exhibit distinct spatiotemporal variation. In this paper, we propose a digital twin (DT)-based network management scheme to enhance QoE. Firstly, user status emulated by the DT is utilized to estimate the transmission capabilities and watching probability distributions of sub-multicast groups (SMGs) for an adaptive segment buffering. The SMGs’ buffers are aligned to the unique virtual buffers managed by the DT for a fine-grained buffer update. Then, a multicast QoE model consisting of rebuffering time, video quality, and quality variation is developed, by considering the mutual influence of segment buffering among SMGs. Finally, a joint optimization problem of segment version selection and slot division is formulated to maximize QoE. To efficiently solve the problem, a data-model-driven algorithm is proposed by integrating a convex optimization method and a deep reinforcement learning algorithm. Simulation results based on the real-world dataset demonstrate that the proposed DT-based network management scheme outperforms benchmark schemes in terms of QoE improvement.

In this paper, we investigate the joint device activity and data detection in massive machine-type communications (mMTC) with a one-phase non-coherent scheme, where data bits are embedded in the pilot sequences and the base station simultaneously detects active devices and their embedded data bits without explicit channel estimation. Due to the correlated sparsity pattern introduced by the non-coherent transmission scheme, the traditional approximate message passing (AMP) algorithm cannot achieve satisfactory performance. Therefore, we propose a deep learning (DL) modified AMP network (DL-mAMPnet) that enhances the detection performance by effectively exploiting the pilot activity correlation. The DL-mAMPnet is constructed by unfolding the AMP algorithm into a feedforward neural network, which combines the principled mathematical model of the AMP algorithm with the powerful learning capability, thereby benefiting from the advantages of both techniques. Trainable parameters are introduced in the DL-mAMPnet to approximate the correlated sparsity pattern and the large-scale fading coefficient. Moreover, a refinement module is designed to further advance the performance by utilizing the spatial feature caused by the correlated sparsity pattern. Simulation results demonstrate that the proposed DL-mAMPnet can significantly outperform traditional algorithms in terms of the symbol error rate performance.

Cooperative perception (CP) is a key technology to facilitate consistent and accurate situational awareness for connected and autonomous vehicles (CAVs). To tackle the network resource inefficiency issue in traditional broadcast-based CP, unicast-based CP has been proposed to associate CAV pairs for cooperative perception via vehicle-to-vehicle transmission. In this paper, we investigate unicast-based CP among CAV pairs. With the consideration of dynamic perception workloads and channel conditions due to vehicle mobility and dynamic radio resource availability, we propose an adaptive cooperative perception scheme for CAV pairs in a mixed-traffic autonomous driving scenario with both CAVs and human-driven vehicles. We aim to determine when to switch between cooperative perception and stand-alone perception for each CAV pair, and allocate communication and computing resources to cooperative CAV pairs for maximizing the computing efficiency gain under perception task delay requirements. A model-assisted multi-agent reinforcement learning (MARL) solution is developed, which integrates MARL for an adaptive CAV cooperation decision and an optimization model for communication and computing resource allocation. Simulation results demonstrate the effectiveness of the proposed scheme in achieving high computing efficiency gain, as compared with benchmark schemes.

This paper investigates the age of information (AoI) and energy efficiency of wireless powered industrial Internet of Everything (IIoE) network, where multiple low-power IIoE devices (IIoEDs) are wirelessly charged by a hybrid access point (HAP) to transmit their sensing information to the control nodes. To enhance the system’s information timeliness with high energy efficiency, we define a novel performance metric, i.e., age-energy efficiency (AEE), which depicts the achievable AoI gain per unit energy consumption. Then, an optimization problem is formulated to maximize the system long-term AEE by jointly optimizing the IIoEDs scheduling and the HAP’s transmit power. Due to the non-convexity of the formulated problem and the intractable challenges with discrete binary variables, we first model the problem as a two-stage discrete-time Markov decision process (MDP) with carefully designed state spaces, action spaces, and reward functions. We then propose a deep reinforcement learning (DRL)-based approach to find the effective scheduling strategy and transmit power. To improve the accuracy of the learned policy, we design a dual-layer deep Q-network (DLDQN) algorithm with fast convergence. Simulation results show that our proposed DLDQN algorithm can improve the AEE by at least 25% when the number of IIoEDs exceeds 50 compared with benchmarks. Moreover, with the proposed DLDQN algorithm, the system long-term AEE can be improved with the increase of the number of IIoEDs.

In this paper, we propose a value of information (VoI)-based packet scheduling scheme (VBPS) in autonomous underwater vehicle (AUV)-assisted underwater acoustic sensor networks (UASNs), where AUVs act as mobile sensor nodes to collect data from areas not accessible to static nodes and then relay data via static nodes. VoI is a performance metric to measure the importance of data packets with different levels of urgency. The proposed scheme aims to avoid collision with the ongoing packet transmission of static nodes without their accurate global information. In specific, the static node localization stage and the topology construction stage are carried out to obtain the local information. Furthermore, the transmission scheduling stage is implemented to avoid packet collision and formulates a combinatorial optimization problem maximizing VoI under the constraint of packet collision avoidance. To solve this complicated problem, a low-complexity distributed search algorithm is proposed, which exploits the spatial-temporal reuse to establish data packet collision constraints and then determines the next-hop node and data transmission time for AUVs. In addition, a collaborative search algorithm is proposed to avoid packet collision among different AUVs by enabling collaboration among AUVs. Extensive simulation results under various scenarios demonstrate the superior performance of the proposed scheme.

Ultra-dense low earth orbit (LEO) satellite network (UD-LSN) is one of the most promising architectures in the sixth-generation (6G) systems, providing several types of services with different service level agreements (SLAs). Network slicing technology effectively meets these SLAs by building multiple logical networks isolated from each other on the physical network. In the UD-LSN, due to the spatiotemporal variations of users and available satellites, it poses a considerable challenge to make dynamic slicing decisions individually for each LEO satellite. This paper proposes a two-layer dynamic reconfigurable radio access network (RAN) slicing architecture for the UD-LSN. We consider the characteristics of enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communications (uRLLC) services and formulate a stochastic optimization problem to maximize the long-term slicing utility, which consists of resource utilization, throughput, and reconfiguration cost. The original problem is transformed into a Markov Decision Process (MDP) and solved with the Branch Dueling Q-Network (BDQ)-based dynamic reconfigurable RAN slicing (DRRS) algorithm in a large slicing window and the priority-based user access algorithm in a small time slot. The simulation results validate the effectiveness of the proposed two-layer DRRS strategy, which has a better performance in the slicing utility, resource utilization, and throughput.

In this paper, we propose an adaptive distributed learning algorithm that not only resists three types of Byzantine attacks (i.e., gradient negative direction attacks, gradient partial dimension zeroing attacks, gradient scaling attacks) but also ensures high model accuracy. The proposed algorithm is built on a fully distributed model: clients share their local model updates with a group of dynamic committee clients, who cooperatively and iteratively train a global model. Specifically, to counter gradient negative direction attacks, we design a method based on gradient projection that maps clients' local gradients into small subspaces. The design allows committee clients to efficiently and precisely filter out adversarial clients by comparing angles between these subspaces. Moreover, considering that data heterogeneity among clients may cause misdetections of gradient partial dimension zeroing and scaling attacks, thereby reducing model accuracy, we introduce an adaptive multi-dimensional scoring method, which is applied after the gradient-projection-based filtering. The method assists committee clients in scoring and selecting most suitable clients for model aggregation using three hyperparameters, and thus achieves a balance between model accuracy and security. Finally, we conduct extensive experiments on real-world datasets to show the proposed algorithm's effectiveness: it can achieve Byzantine robustness and simultaneously maintain high model accuracy.

The increasing network traffic and dynamic user connections have posed challenges for cellular operators in reducing operating costs while ensuring the quality of service (QoS) for users. Cloud radio access network (C-RAN) addresses these issues by separating baseband units (BBUs) and remote radio heads (RRHs), creating a centralized BBU pool. To optimize C-RAN performance, the key is to dynamically assigning RRHs to BBUs, which is challenging due to cost and QoS constraints. In this paper, we propose a data-driven RRH-BBU mapping scheme (KC-A3C) with deep reinforcement learning (DRL) to improve the performance of large-scale C-RANs. First, we analyze a dataset from a cellular operator containing approximately 26,652 active base stations and use the features of the dataset to construct an RRH popularity metric to cluster RRHs. Second, we model the RRH-BBU mapping as a Markov decision process and use the synchronous Advantage Actor-Critic (A3C) algorithm to find the optimal mapping scheme with the highest long-term gain in a dynamic environment, considering resource utilization, RRH migration, and BBU load balancing. Evaluations using real-world datasets show that our proposed scheme outperforms baseline methods.

In this paper, we propose the multi-autonomous underwater vehicle (AUV) collaborative data collection in integrated underwater acoustic communication and detection networks (UCDNs). Specifically, multiple AUVs collaboratively traverse the sensor nodes to collect data while detecting the environment to avoid obstacles along the trajectory. We first propose a time division multiple access (TDMA)-based packet transmission and active bistatic sonar detection strategy for UCDNs to transmit the sensor data and detect the unknown environment. Furthermore, we formulate the collaborative data collection problem as a mixed combinatorial and sequential quadratic optimization problem to minimize the trajectory length of multiple AUVs. To solve this problem, we decouple it into two subproblems, i.e., the node traversal subproblem and the trajectory planning subproblem. The former subproblem is converted into the multi-traveling salesman problem (MTSP), which is solved by the Q-learning-based algorithm to improve the robustness. The latter subproblem is optimally planning each AUV's trajectory while avoiding obstacles, which is solved by the soft actor-critic (SAC) algorithm to online make continuous trajectory decisions. Simulation results demonstrate that the proposed scheme outperforms benchmarks in terms of energy consumption and overall trajectory length.

In this paper, we propose a digital twin (DT)-assisted cloud-edge collaborative transcoding scheme to enhance user satisfaction in live streaming. We first present a DT-assisted transcoding workload estimation (TWE) model for the cloud-edge collaborative transcoding. Particularly, two DTs are constructed for emulating the cloud-edge collaborative transcoding process by analyzing spatial-temporal information of individual videos and transcoding configurations of transcoding queues, respectively. Two light-weight Bayesian neural networks are adopted to fit the TWE models in DTs, respectively. Moreover, we formulate a transcoding-path selection problem to maximize long-term user satisfaction within an average service delay threshold, taking the dynamics of video arrivals and video requests into account. The problem is transformed into a standard Markov decision process by using the Lyapunov optimization, which is further solved by a deep reinforcement learning algorithm. Simulation results based on the real-world dataset demonstrate that the proposed scheme can effectively enhance user satisfaction compared with benchmark schemes.

In this paper, we study the abnormal behaviors detection and the corresponding data poisoning attacks in digital twin (DT)-based networks. We first analyze the abnormal behaviors existing in the DT-based networks, including environment anomalies, hardware and software faults, and network attacks. Specially, we design a machine learning (ML)-based anomaly detector to identify network attacks. Furthermore, due to the strong dependency of ML models on training data, in which the outputs of the trained ML models can be affected by the poisoned samples. We design a data poisoning attack scheme against the proposed ML-based anomaly detector, in which attackers can effectively compromise the output of anomaly detectors. Extensive experimental results adopting three commonly used ML-based models demonstrate that the attack can compromise these detectors with over 80% probability.

In this paper, we investigate joint caching and computing resource reservation for supporting location-aware augmented reality (AR) applications in an edge-assisted two-tier radio access network. We aim at minimizing the caching and computing resource consumption while satisfying the AR service delay requirement. Specifically, to capture the spatio-temporal AR service dynamics, the resource consumption minimization problem is formulated as a long-term stochastic optimization problem. Due to the time-varying service demands and tightly coupled multi-resource reservation decisions, we propose a novel resource reservation algorithm based on the Lyapunov optimization technique to solve the problem. We first transform the original long-term problem into multiple one-shot optimization problems, each of which is then solved by our designed iterative algorithm in an online manner. Simulation results demonstrate that the proposed algorithm can significantly reduce the overall resource consumption compared to benchmark algorithms.

This paper studies autonomous underwater vehicles (AUV)-assisted underwater acoustic sensor networks (UASNs), where AUVs act as mobile sensor nodes to collect information from areas not accessible to static nodes and then relay data via static nodes. Due to the difficulty of obtaining the accurate global information of all the static nodes, we propose a novel packet scheduling scheme by utilizing local information obtained by AUVs. In the proposed scheme, the localization of static nodes stage and the topology construction stage are carried out beforehand to obtain the local information, based on which the transmission scheduling stage is implemented. Furthermore, in the transmission scheduling stage, we design a value of information (VoI)-based packet transmission scheduling (VBPS) strategy to avoid packet collision. Specifically, we introduce a performance metric, i.e., VoI, to measure the importance of data packets with different levels of urgency. Then, we formulate a combinatorial optimization problem to maximize VoI taking packet collision avoidance into consideration. A low-complexity distributed search algorithm is proposed to solve the problem, which exploits the spatial-temporal reuse to establish data packet collision constraints and then determines the next-hop node and data transmission time for AUVs. Extensive simulations under various scenarios are carried out to evaluate the performance of the proposed algorithm.

In this paper, we propose a digital twin (DT)-assisted resource demand prediction scheme to enhance prediction accuracy for multicast short video streaming. Particularly, we first construct user DTs (UDTs) for collecting real-time user status, including channel condition, location, watching duration, and preference. A reinforcement learning-empowered K-means++ algorithm is developed to cluster users based on the collected user status in UDTs. We then analyze users' watching duration and preferences in each multicast group to obtain the swiping probability distribution and recommended videos, respectively. The obtained information is utilized to predict radio and computing resource demand of each multicast group. Initial simulation results demonstrate that the proposed scheme can accurately predict resource demand.

Virtual reality (VR) can provide users with an immersive experience in the metaverse. One of the most promising properties of VR is that users’ identities can be protected by changing their physical world appearances into arbitrary virtual avatars. However, recent proposed de-anonymization attacks demonstrate the feasibility of recognizing the user’s identity behind the VR avatar’s masking. In this paper, we propose AvatarHunter, a non-intrusive and user-unconscious de-anonymization attack based on victims’ inherent movement signatures. AvatarHunter imperceptibly collects the victim avatar’s gait information via recording videos from multiple views in the VR scenario without requiring any permission. A Unity-based feature extractor is designed that preserves the avatar’s movement signature while immune to the avatar’s appearance changes. Real-world experiments are conducted in VRChat, one of the most popular VR applications. The experimental results demonstrate that AvatarHunter can achieve attack success rates of 92.1% and 66.9% in closed-world and open-world avatar settings, respectively, which are much better than existing works.

Non-terrestrial networks (NTNs) with low-earth orbit (LEO) satellites have been regarded as promising remedies to support global ubiquitous wireless services. Due to the rapid mobility of LEO satellite, inter-beam/satellite handovers happen frequently for a specific user equipment (UE). To tackle this issue, earth-fixed cell scenarios have been under studied, in which the LEO satellite adjusts its beam direction towards a fixed area within its dwell duration, to maintain stable transmission performance for the UE. Therefore, it is required that the LEO satellite performs real-time resource allocation, which however is unaffordable by the LEO satellite with limited computing capability. To address this issue, in this paper, we propose a two-time-scale collaborative deep reinforcement learning (DRL) scheme for beam management and resource allocation in NTNs, in which LEO satellite and UE with different control cycles update their decision-making policies through a sequential manner. Specifically, UE updates its policy subject to improving the value functions of both the agents. Furthermore, the LEO satellite only makes decisions through finite-step rollouts with a reference decision trajectory received from the UE. Simulation results show that the proposed scheme can effectively balance the throughput performance and computational complexity over traditional greedy-searching schemes.

In this paper, we propose a novel traffic prediction scheme for multiple radio access technology (multi-RAT) heterogeneous network. The scheme is named user-Cybertwin asynchronous learning (UCAL), which aims to extract meaningful patterns from noisy network traffic measurements and mitigate the impact of highly nonstationary measurements for ensuring the prediction accuracy. Specifically, we design a pattern extraction method that minimizes the Frobnius norm between the collected measurements and the expected k-rank approximation of the measurements in order to extract useful information. Then, by transforming the conventional long short term memory (LSTM) model into a nonlinear state space and incorporating Gaussian noise, we develop an online LSTM algorithm to adapt fast to changing environments. As a result, the parameter updating of the new online LSTM model can keep up with data changes while capturing complicated and nonlinear relationships among measurements. We consider both the surrounding environment conditions on the mobile user side and end-to-end link conditions on the Cybertwin side, and iteratively update the model parameters in both Cybertwin and MU. Simulation results demonstrate that the proposed UCAL scheme can achieve high traffic prediction accuracy in comparison to existing schemes. It can also significantly improve the efficiency in maintaining the prediction accuracy even when the dimension of traffic measurements increases.

Deep learning has been successfully adopted in mobile edge computing (MEC) to optimize task offloading and resource allocation. However, the dynamics of edge networks raise two challenges in neural network (NN)-based optimization methods: low scalability and high training costs. Although conventional node-output graph neural networks (GNN) can extract features of edge nodes when the network scales, they fail to handle a new scalability issue whereas the dimension of the decision space may change as the network scales. To address the issue, in this paper, a novel link-output GNN (LOGNN)-based resource management approach is proposed to flexibly optimize the resource allocation in MEC for an arbitrary number of edge nodes with extremely low algorithm inference delay. Moreover, a label-free unsupervised method is applied to train the LOGNN efficiently, where the gradient of edge tasks processing delay with respect to the LOGNN parameters is derived explicitly. In addition, a theoretical analysis of the scalability of the node-output GNN and link-output GNN is performed. Simulation results show that the proposed LOGNN can efficiently optimize the MEC resource allocation problem in a scalable way, with an arbitrary number of servers and users. In addition, the proposed unsupervised training method has better convergence performance and speed than supervised learning and reinforcement learning-based training methods. The code is available at https://github.com/UNIC-Lab/LOGNN.

Cellular vehicle-to-everything (C-V2X) network is a promising solution to support on road diverse quality of services (QoS) such as ultra reliable low latency communication (URLLC) and enhanced mobile broadband (eMBB). However, satisfying the stringent QoS requirements in high-dynamic C-V2X environment is very challenge. In this paper, we leverage the multi-connectivity technology to enhance the reliability of downlink URLLC in C-V2X. Specifically, with the aid of the cloud radio access network (C-RAN), the network controller duplicates each URLLC packet and transmits its replicas over multiple independent wireless links. To ensure the reliability of URLLC links while maximizing the average rate of eMBB links, we design a coordinated multi-agent deep reinforcement learning algorithm for real-time power allocation of multi-connectivity URLLC links. Each URLLC link is treated as an agent here, and its transmit power is its action. The multiple links serving the same URLLC user are coordinated with a three-layer neural network for information sharing, allowing them to cooperatively choose transmit powers in terms of ensuring reliability while minimizing inter-cell interference and energy consumption. Extensive simulation results validate the effectiveness of the proposed power allocation algorithm for multi-connectivity downlink URLLC.

We consider a fully-decoupled radio access network (FD-RAN), where base stations (BSs) are physically decoupled into control BSs, uplink BSs and downlink BSs, and multi-connectivity becomes the default user equipment (UE) association mode. Specifically, we study the inter-frequency multi-connectivity in downlink of FD-RAN and present a deep reinforcement learning based online multi-connectivity mobility management scheme. We formulate a UE dynamic multiple access problem and transform it into a handover decision problem, then apply the double deep Q-network (DDQN) algorithm to make real time mobility management decisions. Simulation results show that the proposed scheme outperforms benchmarks in terms of handover frequency and quality of service, while ensuring real-time performance.

In this paper, we propose collaborative beamforming (CB) in unmanned aerial vehicle (UAV)-assisted communication networks to improve transmission data rate with minimum energy consumption. Specifically, CB allows a group of UAVs forming a virtual element antenna array (VEAA) and transmitting data collaboratively in a synchronous manner through a high-gain mainlobe (ML) beam. The goal is to optimize the deployment locations of UAVs in the VEAA and excitation current weights for performing CB transmissions considering the energy cost for UAV deployment. Accordingly, we formulate an Energy-Efficient Communication Multi-objective Optimization Problem (EECMOP) to jointly maximize the transmission rate and minimize the maximum sidelobe level (SLL) as well as UAV energy consumption. Then, we propose an Enhanced Multi Objective Ant Lion Optimizer (EMOALO) algorithm which incorporates a chaos theory to develop chaotic initialization and adjustable mode operators for solving the problem. Simulation results demonstrate the effectiveness of the EMOALO algorithm in improving energy efficiency for UAV-assisted communication networks.

Thanks to the ubiquitous and easily accessible nature of wireless signals, wireless sensing is regarded as one of the promising techniques in the next-generation Internet of Things. In this paper, we propose the inverse semantic communications as a new paradigm to achieve lightweight wireless sensing using the reconfigurable intelligent surface (RIS). Instead of extracting semantic information from messages, we aim to encode the task-related source messages into a hyper-source message. Specifically, we first develop a novel RIS hardware for encoding several signal spectrums into one MetaSpectrum. We then propose a self-supervised learning method for decoding the MetaSpectrums to obtain the original signal spectrums. Using the sensing data collected from the real world, we show that our framework can reduce the data volume by 90% compared to that before encoding, without affecting the execution of various sensing tasks. Experiment results also demonstrate that the amplitude response matrix of the RIS enables the encryption of the sensing data.

Network topology obfuscation (NTO) is generally considered as a promising proactive mechanism to mitigate traffic analysis attacks. The main challenge is to strike a balance among energy consumption, reliable routing, and security levels due to resource constraints in sensor nodes. Furthermore, software-defined wireless sensor networks (WSNs) are more vulnerable to traffic analysis attacks due to the uncovered pattern of control traffic between the controller and the nodes. In this article, a new energy-aware NTO mechanism is proposed, which maximizes the attack costs and is efficient and practical to be deployed. Specifically, first, a route obfuscation method is proposed by utilizing ranking-based route mutation, based on four different critical criteria: 1) route overlapping; 2) energy consumption; 3) link costs; and 4) node reliability. Then, a sink node obfuscation method is introduced by selecting several fake sink nodes that are indistinguishable from actual sink nodes, according to the$k$-anonymity model. As a result, the most suitable routes and sink nodes can be selected, and a highest obfuscation level can be reached without sacrificing energy efficiency. Finally, extensive simulation results demonstrate that the proposed methods can strongly mitigate traffic analysis attacks and achieve effective NTO for software-defined WSNs. In addition, the proposed methods can reduce the success rate of the attacks while achieving lower energy consumption and higher network lifetime.

Mobile-edge computing (MEC) is a promising approach to support high-quality time-sensitive applications. With the increasing number of mobile devices, achieving efficient service migration management has become nontrivial in MEC. In addition, the service migration issue is difficult to be solved in real time due to user mobility and dynamic network conditions. In this article, we investigate the mobility-aware service migration problem in MEC by introducing a data-driven framework. First, service migration is formulated as an optimization problem for minimizing the long-term system delay that consists of computing, communication, and migration delays. Second, we propose a Mobility-aware Service Migration scheme, named MSM, consisting of three layers: 1) the data collection layer; 2) the association patterns analysis layer; and 3) the service migration layer. Specifically, we first collect users’ historical Wi-Fi traces to mine the association patterns. We then design a user management mechanism to reduce the complexity of decision making by using user association patterns. Finally, we formulate the service migration as a 2-D-Markov decision process and devise a deep reinforcement learning (DRL)-based algorithm to obtain service migration decisions in a large-scale MEC scenario. Extensive data-driven experiments are conducted to demonstrate the efficacy of MSM in reducing the system delay.

High-speed train (HST) communications are envisioned to provide diversified broadband services by integrating with 5G while the high mobility induces fast-fading channels and potentially degrades the system performance. To address this issue, we investigate an HST communication network empowered by intelligent reflecting surfaces (IRSs) with the multiple-input–multiple-output (MIMO) technology. Statistical channel state information (CSI) is exploited to mitigate the impact of the fast time-varying fading. The transceiver beamforming vectors and the IRS phase shift matrix are optimized to improve the system performance in terms of the outage probability and the ergodic capacity considering the channel uncertainty. First, we derive the analytical expression of the outage probability with a generalized Marcum$Q $-function. Then, we develop an alternating optimization algorithm to minimize the outage probability by capitalizing on the generalized eigenvalue–eigenvectors. Moreover, the ergodic capacity is deduced with statistical CSI and then optimized by analyzing the upper bound with Jensen’s approximation. Extensive simulations show that simulation results are consistent with the theoretical analysis, and the IRS-assisted system significantly outperforms the system without IRS in terms of the outage probability and the ergodic capacity.

Metaverse, also known as the Internet of 3-D worlds, has recently attracted much attention from both academia and industry. Each virtual subworld, operated by a virtual service provider (VSP), provides a type of virtual service. Digital twins (DTs), namely, digital replicas of physical objects, are key enablers. Generally, a DT belongs to the party that develops it and establishes the communication link between the two worlds. However, in an interoperable metaverse, data-like DTs can be “shared” within the platform. Therefore, one set of DTs can be leveraged by multiple VSPs. As the quality of the shared DTs may not always be satisfying, in this article, we propose an agile solution, i.e., a dynamic hierarchical framework, in which a group of Internet of Things devices in the lower level are incentivized to collectively sense physical objects’ status information and VSPs in the upper level determine synchronization intensities to maximize their payoffs. We adopt an evolutionary game approach to model the devices VSP selections and a simultaneous differential game to model the optimal synchronization intensity control problem. We further extend it as a Stackelberg differential game by considering some VSPs to be first movers. We provide open-loop solutions based on the control theory for both formulations. We theoretically and experimentally show the existence, uniqueness, and stability of the equilibrium to the lower level game and further provide a sensitivity analysis for various system parameters. Experiments show that the proposed dynamic hierarchical game outperforms the baseline.

In this article, we propose an integrated satellite–terrestrial network (ISTN) architecture to support delay-sensitive task offloading for remote Internet of Things (IoT), in which satellite networks serve as a complement to terrestrial networks by providing additional communication resources, backhaul capacities, and seamless coverage. Under this architecture, we investigate how to jointly make offloading link selection and bandwidth allocation decisions for BSs and IoT users. Considering the differentiated decision-making time granularities, we formulate a two-timescale stochastic optimization problem to minimize the overall task offloading delay. To accommodate the two-timescale network dynamics and characterize state–action relations, we establish a hierarchical Markov decision process (H-MDP) framework with two separate agents tackling two-timescale network management decisions, and two evolved MDP-based subproblems are formulated accordingly. To efficiently solve the subproblems, we further develop a hybrid proximal policy optimization (H-PPO)-based algorithm. Specifically, a hybrid actor–critic architecture is designed to deal with the mixed discrete and continuous actions. In addition, an action mask layer and an action shaping function are designed to sample feasible task offloading decisions from the time-variant action set. Extensive simulation results have validated the superiority of the proposed ISTN architecture and the H-PPO-based algorithm, especially, in scenarios with scarce spectrum resources and heavy traffic loads.

Accurate and efficient digital twin construction through real-time multi-attribute sensing and remote concurrent data analysis is essential in supporting complex connected industrial applications. Given the unsynchronized nature and heterogeneous sampling rates of distributed sensing processes, the varying time misalignment among different attributes will inevitably deteriorate the remote correlation analysis and digital twin construction. Furthermore, application-agnostic digital twin construction approaches could potentially involve high communication and computation overhead for comprehensive digital twin construction. In this article, a concurrent end-to-end time synchronization and multi-attribute data resampling scheme is proposed to enable accurate and efficient digital twin construction at the remote end. Specifically, digital clocks are concurrently established at the remote end, with each of them associated with a sampling rate of a unique sensing attribute. To tackle the temporal misalignment among multiple sensing attributes, raw data are accurately resampled according to the same reference frequency, with attribute-specific synchronized digital clocks providing cohesively aligned time information. An edge-centric platform is established to efficiently guide the multidimensional data processing during digital twin construction. Simulation results demonstrate that the proposed scheme can achieve more accurate and efficient digital twin construction than existing modeling methods. In the end, the digital twin-driven predictive maintenance is presented as a case study, aiming at illustrating the potential applications and benefits expected of the proposed scheme in industrial environments.

Aerial intelligent reconfigurable surface (IRS) is a promising technology to enhance channel quality in data delivery. In this article, we study an aerial IRS deployment problem to enable timely and reliable data delivery in a remote Internet of Things (IoT) scenario, in which an IRS mounted on an unmanned aerial vehicle (UAV) is adopted as a mobile relay to assist devices in uploading data to the base station (BS). The objective is to minimize the average Age of Information (AoI) of the data received by the BS over time by jointly determining the aerial IRS deployment position and phase shift, transmit power of devices, and data uploading time. Under the requirements of peak AoI (PAoI) and communication reliability, we formulate an average AoI minimization problem. Since the nonlinear relations among optimization variables make the formulated problem nonconvex and intractable to solve, we propose a block coordinate descent (BCD)-based iterative algorithm which decomposes the formulated problem into several subproblems. The variables are optimized in each subproblem individually in an alternately iterative manner to attain a near-optimal solution. Simulation results demonstrate the superiority of the proposed algorithm in improving the information freshness compared with the benchmark schemes.

Edge sensing can achieve high-performance state estimation in industrial IoT systems by supporting task offloading and data processing at powerful edge estimators. Accurate edge sensing depends on low offloading delay. However, it is challenging to decrease offloading delay due to the harsh industrial environment and limited communication-and-computation resources. In this article, a closed-form expressing of estimation error with respect to offloading delay is derived to indicate that adjusting offload delay on demand is necessary for estimation error reduction. Then, we propose an adaptive edge sensing scheme, aiming to minimize estimation error by jointly optimizing task offloading and sensor scheduling. The required optimization is formulated as a mixed-integer nonlinear programming problem and solved by the designed decomposition and approximation methods. Specifically, the maximum matching is used for sensor scheduling to assign the optimal edge estimator for each sensor. The task offloading algorithm is designed based on the inner approximation method to reduce the offloading delay. Finally, simulation results demonstrate that the proposed scheme has superiorities in reducing estimation error compared with centralized sensing and distributed sensing schemes. Moreover, we find an interesting result that estimation error is delay sensitive when the offloading delay is large.

In this article, we investigate the scheduling issue of diesel generators (DGs) in an Internet of Things (IoT)-Driven isolated microgrid (MG) by deep reinforcement learning (DRL). The renewable energy is fully exploited under the uncertainty of renewable generation and load demand. The DRL agent learns an optimal policy from history renewable and load data of previous days, where the policy can generate real-time decisions based on observations of past renewable and load data of previous hours collected by connected sensors. The goal is to reduce operating cost on the premise of ensuring supply–demand balance. In specific, a novel finite-horizon partial observable Markov decision process (POMDP) model is conceived considering the spinning reserve. In order to overcome the challenge of discrete-continuous hybrid action space due to the binary DG switching decision and continuous energy dispatch (ED) decision, a DRL algorithm, namely, the hybrid action finite-horizon RDPG (HAFH-RDPG), is proposed. HAFH-RDPG seamlessly integrates two classical DRL algorithms, i.e., deep$Q$-network (DQN) and recurrent deterministic policy gradient (RDPG), based on a finite-horizon dynamic programming (DP) framework. Extensive experiments are performed with real-world data in an IoT-driven MG to evaluate the capability of the proposed algorithm in handling the uncertainty due to interhour and interday power fluctuation and to compare its performance with those of the benchmark algorithms.

The advances in artificial intelligence (AI) and edge computing enable edge intelligence to support pervasive intelligent Internet of Things (IoT) applications in the future wireless networks. We focus on deep neural network (DNN)-based classification tasks, and investigate how to improve the confidence level and delay performance of DNN inference via device-edge collaboration. We first develop a stochastic cumulative DNN inference scheme that aggregates multiple random DNN inference results and generates a cumulative DNN inference result with improved confidence level. Then, based on a computation-efficient DNN model deployment strategy with shared computation between a locally deployed fast DNN model and a full DNN model partitioned between the device and edge, a closed-loop adaptive device-edge collaboration scheme is developed to support cumulative DNN inference for multiple devices. We adaptively determine how to offload DNN inference computation to the edge and how to allocate transmission and edge-computing resources among multiple devices, for Quality-of-Service (QoS) satisfaction in terms of both confidence level and inference delay with resource and energy efficiency. A reinforcement learning (RL) approach is used for adaptive offloading decision, which relies on a resource allocation solution for reward calculation. Simulation results demonstrate the effectiveness of the adaptive device-edge collaboration scheme for cumulative DNN inference, in terms of confidence level improvement, delay violation minimization, network resource efficiency, and device energy efficiency.

Multidomain Internet of Things (IoT) is faced with serious domain interoperability (DI) and compatibility issues since different intradomain authorization and authentication (A&A) mechanisms are deployed without the consideration of interdomain A&A. This article proposes a blockchain-assisted scheme to achieve flexible intra- and inter-domain A&A simultaneously and seamlessly. Specifically, we first design a contract-based mutual access control agreement on top of a consortium blockchain, where domain managers can manage their access permission without any trusted parties. Based on the agreement, a secure and privacy-preserving authentication protocol is further proposed by tailoring one-out-of-many proof techniques, which enables IoT devices to anonymously access authorized IoT domains. We additionally design a voting-based protocol by using a threshold-based cryptosystem. The protocol allows domain managers to transparently audit resource access with the assistance of the blockchain. Detailed security analysis demonstrates that the proposed scheme achieves the security properties, such as DI, privacy protection, and accountability. Finally, we develop two proof-of-concept prototypes in a physical testbed and virtual machine, respectively, based on an open-source blockchain platform to show our scheme’s efficiency in terms of computation and communication overhead.

With the ever-growing communication demands and the unceasing miniaturization of mobile devices, the Internet of Things is expanding the amount of mobile terminals to an enormous level. To deal with such numbers of communication data, plenty of base stations (BSs) need to be deployed. However, denser deployments of heterogeneous networks (HetNets) lead to more frequent handovers, which could increase network burden and degrade the users experience, especially in traffic hotspot areas. In this article, we develop a unified framework to investigate the handover performance of wireless networks with traffic hotspots. Using the stochastic geometry, we derive the theoretical expressions of average distances and handover metrics in HetNets, where the correlations between users and BSs in hotspots are captured. Specifically, the distributions of macro cells are modeled as independent Poisson point processes (PPPs), and the two tiers of small cells outside and inside the hotspots are modeled as PPP and Poisson cluster process (PCP) separately. A modified random waypoint (MRWP) model is also proposed to eliminate the density wave phenomenon in traditional models and to increase the accuracy of handover decision. By combining the PCP and MRWP model, the distributions of distances from a typical terminal to the BSs in different tiers are derived. Afterward, we derive the expressions of average distances from a typical terminal to different BSs and reveal that the handover rate, handover failure rate, and ping-pong rate are deduced as the functions of BS density, scattering variance of clustered small cell, user velocity, and threshold of triggered time. Simulation results verify the accuracy of the proposed analytical model and closed-form theoretical expressions.

Semantic communications can reduce the resource consumption by transmitting task-related semantic information extracted from source messages. However, when the source messages are utilized for various tasks, e.g., wireless sensing data for localization and activities detection, semantic communication technique is difficult to be implemented because of the increased processing complexity. In this paper, we propose the inverse semantic communications as a new paradigm. Instead of extracting semantic information from messages, we aim to encode the task-related source messages into a hyper-source message for data transmission or storage. Following this paradigm, we design an inverse semantic-aware wireless sensing framework with three algorithms for data sampling, reconfigurable intelligent surface (RIS)-aided encoding, and self-supervised decoding, respectively. Specifically, on the one hand, we propose a novel RIS hardware design for encoding several signal spectrums into one MetaSpectrum. To select the task-related signal spectrums for achieving efficient encoding, a semantic hash sampling method is introduced. On the other hand, we propose a self-supervised learning method for decoding the MetaSpectrums to obtain the original signal spectrums. Using the sensing data collected from real-world, we show that our framework can reduce the data volume by 95% compared to that before encoding, without affecting the accomplishment of sensing tasks. Moreover, compared with the typically used uniform sampling scheme, the proposed semantic hash sampling scheme can achieve 67% lower mean squared error in recovering the sensing parameters. In addition, experiment results demonstrate that the amplitude response matrix of the RIS enables the encryption of the sensing data. The code for this paper is available athttps://github.com/HongyangDu/SemSensing.

Next generation cellular network is expected to provide the simultaneous high-accuracy localization and ultra-reliable communication services, even in high mobility scenarios. To that end, the novel orthogonal time frequency space (OTFS) modulation has been developed as a promising physical-layer transmission technique, evident by the outstanding performance in terms of robustness against time-frequency selective fading over the orthogonal frequency division multiplexing (OFDM) counterpart. However, when OTFS meets massive multiple-input multiple-output (MIMO), the specific conditions, under which the delay-Doppler (DD) domain channel model holds, are not identified. In addition, the channel estimation and localization performance in such system is rarely studied. In this work, we target at these new challenges, and conduct comprehensive modelling, performance analysis, and algorithm design for massive MIMO-OTFS based simultaneous localization and communications. Specifically, we derive new channel models for the massive MIMO-OTFS system, which captures both time-frequency dispersion and spatial wideband effects. The specific conditions, under which the new models hold has been unveiled as well. Based on the new models, we establish the theoretical foundations for channel estimation and localization, by deriving the Cramér-Rao lower bounds of channel parameter and location estimation errors. Such bounds have been achieved with the newly designed low-complexity channel estimation and localization algorithms. Numerical simulations of the proposed framework with prevailing pulse functions are also conducted and the results validate the proposed designs and analysis.

Map matching is a fundamental component for location-based services (LBSs), such as vehicle mobility analysis, navigation services, traffic scheduling, etc. In this paper, we investigate federated learning augmented map matching based on heterogeneous cellular moving trajectories from different operator systems, the goal of which is to improve matching accuracy without violating the user privacy. First, we develop a data collection platform with one Android-based application, and conduct rigorous data collection campaigns. Second, we perform systematic data analytics to reveal the data-driven technical challenges, including the impact of sampling rate, high location error of cellular moving data, and poor heterogeneous matching performance. Third, we propose an augmented map matching model, named FL-AMM, i.e.,FederatedLearningAugmentedMapMatching, in which we i) adopt the vertical federated learning framework to achieve data collaboration and privacy protection for heterogeneous operators; ii) devise a data augmentation component to enhance the capability of representing the raw cellular data; and iii) design a map matching model to further learn the mapping function from cellular trajectory points to road segments. Finally, we conduct extensive data-driven experiments to corroborate the efficiency and robustness of the proposed FL-AMM.

Split learning (SL) is a collaborative learning framework, which can train an artificial intelligence (AI) model between a device and an edge server by splitting the AI model into a device-side model and a server-side model at a cut layer. The existing SL approach conducts the training process sequentially across devices, which incurs significant training latency especially when the number of devices is large. In this paper, we design a novel SL scheme to reduce the training latency, namedCluster-basedParallelSL(CPSL) which conducts model training in a “first-parallel-then-sequential” manner. Specifically, the CPSL is to partition devices into several clusters, parallelly train device-side models in each cluster and aggregate them, and then sequentially train the whole AI model across clusters, thereby parallelizing the training process and reducing training latency. Furthermore, we propose a resource management algorithm to minimize the training latency of CPSL considering device heterogeneity and network dynamics in wireless networks. This is achieved by stochastically optimizing the cut layer selection, device clustering, and radio spectrum allocation. The proposed two-timescale algorithm can jointly make the cut layer selection decision in a large timescale and device clustering and radio spectrum allocation decisions in a small timescale. Extensive simulation results on non-independent and identically distributed data demonstrate that the proposed solution can greatly reduce the training latency as compared with the existing SL benchmarks, while adapting to network dynamics.

In this article, we propose a blockchain-based fair and privacy-preserving data trading scheme that supports fine-grained data selling. First, to achieve fairness for trading participants, by incorporating attribute-based credentials, encryption, and zero-knowledge proof, we design a data trading scheme where a buyer first publishes the required data attributes on the blockchain, and a data seller can demonstrate data availability in ciphertext by only disclosing the required attributes to a data buyer and proving the authenticity of data. A data buyer transfers funds only if the correct key material is uploaded to the blockchain. Second, to guarantee fine-grained data trading and preserve identity privacy, we build a Merkle hash tree on the ciphertexts of data with a signature on its root node, which allows a data seller to split data into blocks and remove the sensitive information from the data without affecting data availability verification. The public key of the data seller is not leaked to the data buyer during the trading. Moreover, different trading transactions from the same data seller cannot be linked. We formally prove that our scheme achieves the desired security properties: fairness and privacy preservation. Simulation results demonstrate the feasibility and efficiency of the proposed scheme.

Decentralized anonymous payment (DAP) enables users to directly transfer cryptocurrencies privately without passing through a central authority. Anonymous cryptocurrencies have been proposed to improve the privacy degree of DAP systems, such as Zerocash and Monero. However, the strong degree of privacy may cause new regulatory concerns, i.e., the anonymity of transactions can be used for illegal activities, such as money laundering. In this paper, we propose a novel DAP scheme that supports regulatory compliance and enforcement. We first introduce regulators into the system, who define regulatory policies for anonymous payment, and the policies are enforced through commitments and non-interactive zero-knowledge proofs for compostable statements. By doing so, users can prove that transactions are valid and comply with regulations. A tracing mechanism is embedded in the scheme to allow regulators to recover the real identities of users when suspicious transactions are detected. The formal security model and proof are provided to demonstrate that the proposed scheme can achieve desired security properties, and the performance evaluation shows its high efficiency.

In this paper, we propose a servers-aided password-based subsequent-key-locked encryption mechanism to ensure the confidentiality of outsourced electronic health records (EHRs). The encryption mechanism achieves conditional forward transparency: a doctor can only access a patient's EHRs related to the current diagnosis with the patient's delegation. It also achieves portability: to delegate a doctor for accessing a specific part of EHRs, the patient only needs to send one key (at most 256 bits) in addition to the delegation information to the doctor; the patient does not need to maintain any secret in a local device. Then, we propose a blockchain-based secure EHR provenance mechanism, where a data structure of EHR provenance record is designed to precisely reflect the EHRs’ provenance information; a smart contract on a public blockchain is deployed to secure both EHRs and the corresponding provenance records. Finally, we develop a cloud-based eHealth system, dubbed HealthFort, based on the two mechanisms. Security analysis and comprehensive performance evaluation are conducted to demonstrate that HealthFort is secure and efficient.

Mobile devices have become near-ubiquitous tools in our daily lives. Following this trend, mobile commence is developed rapidly which in turns stimulates interests in mobile payment. Some prominent examples include Google’s Wallet, WeChat Pay, and Apple Pay. Most of these technologies, however, are designed for users to be able to pay conveniently to the business. In other words, they are designed with the business to user model in mind. Besides, an active network connection with an external payment server is required either from payer or payee during transaction. Our work intends to supplement existing solutions, which allows payment to be made in an off-line and dual-anonymous manner. In doing so, a dual-anonymous off-line electronic cash scheme is proposed by utilizing BBS+ signature. The feature of our scheme is dual-anonymous payment, which means that both the payer and the payee in any transaction cannot be identified even all other users and the payment server collude. Through security proof and performance analysis, we also demonstrate that the security of the proposed scheme can be reduced to standard assumptions and it is suitable for applications in mobile commerce.

Deterministic transmission guarantee in time-sensitive networks (TSN) relies on queue models (such as CQF, TAS, ATS) and resource scheduling algorithms. Thanks to its ease of use, the CQF queue model has been widely adopted. However, the existing resource scheduling algorithms of CQF model only focus on periodic time-triggered (TT) flows without consideration of bursting flows. Considering that the bursting flows often carry high-priority data in real systems, in this paper we investigate the mixed-flow (i.e., TT and bursting flows) scheduling problem in CQF-based TSN aiming to maximize the number of schedulable flows and system load balance while satisfying the deterministic demands of delay, jitter, and reliability for both TT and bursting flows. Unfortunately, it is challenging to schedule the mixed flows with the original CQF model because of the huge difference between TT and bursting flows. To resolve this problem, we firstly design an enhanced Multi-CQF model to satisfy the basic demands of bursting flows sent at any time without affecting the deterministic transmission of TT flows. Given the complexity of mixed-flow scheduling and the proposed queue model, it is difficult for traditional algorithms to fully utilize network resources. Thus, we further propose a uline time-correlated uline DRL uline resource uline scheduling (TimeDRS) algorithm to optimize the resource allocation. TimeDRS can be extended to other time-related resource scheduling scenarios, such as TDMA-based scheduling. Experimental results demonstrate that our proposed approaches can greatly reduce frame loss and end-to-end latency for bursting flows, and well balance runtime and schedulability compared with state-of-the-art benchmarks.

In this paper, we propose a novel channel-aware latency taming scheme, called Optimal Transmission Latency Taming (OTLT), to detect hidden channel state and tame the distribution tail of the packet sojourn time in Industrial Internet of Things (IIoT) devices. Specifically, we design a forward algorithm based on a hidden semi-Markov model to detect the hidden channel state, with a particular emphasis on the state sojourn duration, and to calculate the corresponding channel access probability. Then we develop a time-sensitive model to investigate the minimum sojourn time a packet spends in the IIoT device before leaving successfully. With the obtained channel access probability, the first passage probability of the proposed model is explored to find the maximum probability of a packet being successfully transmitted in a given back-off sojourn duration (BSD). The distribution tail of the packet sojourn time can be tamed by minimizing the cumulative summation of each BSD in consideration of the quadratic penalty latency constraints. Simulation results demonstrate that, in the industrial environment, the OTLT scheme can keep the packet’s sojourn duration within a quantifiable limit and variance. It can also obtain considerably efficient control over packet transmission latency in a time-varying wireless propagation channel even with the increasing number of IIoT devices.

This paper studies an unmanned aerial vehicle (UAV)-assisted wireless powered sensor network (WPSN), where sensor nodes of multiple types can simultaneously harvest radio-frequency energy from the UAV and then transmit sensing data by using harvested energy. A joint distributed beamforming (DBF) and backscattering scheme is designed, in which the sensor nodes of one type can perform DBF while the sensor nodes of other types perform distributed backscattering (DBS) to improve the received signal strength. A sum-throughput maximization problem is formulated by jointly optimizing DBF phases, DBS phases, and time allocation (TA), subject to the received signal-to-noise ratio constraints. Since the formulated problem is difficult to be solved due to the tightly coupled optimizing variables, the problem is decoupled into a TA subproblem and a phase optimization subproblem, and then a two-step algorithm is proposed to solve them. Firstly, the closed-form solution for the TA subproblem is derived according to Karush-Kuhn-Tucker conditions. Secondly, based on iterative optimization and one-dimensional search methods, a centralized algorithm is proposed to obtain the optimal solution for the phase optimization subproblem. Moreover, a decentralized algorithm that obtains the suboptimal solution is proposed to reduce the computational complexity. Extensive simulation results validate the effectiveness of the proposed scheme on throughput enhancement.

With the advancement of network and computer technologies, virtual cyberspace keeps evolving, and Metaverse is the main representative. As an irreplaceable technology that supports Metaverse, the sensing information transmission from the physical world to Metaverse is vital. Inspired by emerging semantic communication, in this paper, we propose a semantic transmission framework for transmitting sensing information from the physical world to Metaverse. Leveraging the in-depth understanding of sensing information, we define the semantic bases, through which the semantic encoding of sensing data is achieved for the first time. Consequently, the amount of sensing data that needs to be transmitted is dramatically reduced. Unlike conventional methods that undergo data degradation and require data recovery, our approach achieves the sensing goal without data recovery while maintaining performance. To further improve Metaverse service quality, we introduce contest theory to create an incentive mechanism that motivates users to upload data more frequently. Experimental results show that the average data amount after semantic encoding is reduced to about 27.87% of that before encoding, while ensuring the sensing performance. Additionally, the proposed contest theoretic based incentive mechanism increases the sum of data uploading frequency by 27.47% compared to the uniform award scheme.

Small cell networks (SCNs) have emerged as a promising solution to meet the demand for increasing data traffic for the sixth generation and beyond wireless networks. However, power consumption and two-tier interference issues are two bottlenecks that hinder further development. This paper proposes a novel reconfigurable intelligent surface (RIS)-based SCN in which an RIS serves multiple micro users as a small cell base station while assisting the macro user’s transmission. Compared to the conventional SCNs, the RIS-based SCN can achieve significant power reduction. Meanwhile, the reflected signal can be regarded as a multipath component instead of interference to the macro user. We propose two transmission schemes and formulate the design of the phase shift matrix at the RIS and the beamforming vector at the macro base station as an optimization problem. The alternating optimization algorithm is developed to optimize the phase shift matrix and the beamforming vector to minimize the total power consumption under the user rate and phase shift constraints. Simulation results show that the total power consumption can be reduced significantly by deploying the RIS in the SCN when the number of reflective elements is sufficiently large.

To enable flexible base stations (BS) association and dynamic resource management for personalized user equipment (UE) download service provision in the next-generation mobile communication network (6G), in this paper, we investigate the downlink (DL) transmission scenario in an origin fully-decoupled radio access network (FD-RAN) architecture. Considering the unique fully-decoupled UL/DL access feature, we propose an efficient two-stage DL channel estimation method in the FD-RAN. We formulate a novel multi-connectivity and dynamic resource cooperation problem with joint multiple-BS and multiple-UE association and coordinated beamforming, aiming at maximizing the weighted sum achievable rate in DL FD-RAN. By leveraging the many-to-many swap-matching theory and fractional relaxation approach, we solve the dynamic UE scheduling problem with multiple-BS and multiple-UE association and the coordinated beamforming problem, respectively. Extensive simulation results based on standard 3GPP 36.873 urban micro channel demonstrate that the proposed framework can improve the average spectral efficiency by 34.9% as compared to the traditional maximum ratio transmission beamforming method.

To cope with the even more urgent spectrum and energy efficiency challenge for trillion-level terminal access and data uploading in the next generation mobile communication network (6G), in this paper, we investigate the uplink transmission in an original fully-decoupled radio access networks (FD-RAN) architecture. Specifically, we propose a resilient uplink base station cooperative reception framework in FD-RAN, which is a large-scale fading based two-tier signal combination approach for the uplink transmission, including the localized signal combination at the base station and centralized signal combination at the edge cloud, respectively. Then, we formulate a weighted sum-rate maximization problem for the uplink transmission optimization, and decompose it into two subproblems. A spectrum-efficiency maximized virtual service cluster selection (SEMVS) algorithm is designed by leveraging the channel statistical information for solving subproblem one, and a fractional programming based power control (FPPC) algorithm is introduced for the power optimization of subproblem two. Compared to the typical RAN architectures with corresponding access and power control methods, simulation results demonstrate the significant performance improvements of uplink FD-RAN with the proposed solution.

In this paper, we investigate digital twin (DT)-assisted adaptive deep neural network (DNN) inference in the Industrial Internet of Things (IIoT). We consider a scenario that an edge server has a full-size DNN for high-accuracy inference, while an IIoT device has a lightweight DNN for fast on-device inference. The IIoT device generates computing tasks, such as object recognition, to be processed by DNN. For each task, a local controller at the network edge determines whether or not to offload the task to the edge server before it enters each layer of the lightweight DNN. The objective is to find the task offloading point that maximizes a utility including delay, inference accuracy, and on-device energy consumption. To achieve this objective, we propose an online DT-assisted task offloading scheme, which exploits DTs to capture the task processing status at the IIoT device and the workload at the edge server. Simulation results demonstrate the excellent performance of the proposed adaptive DT-assisted DNN inference on delay, inference accuracy, and on-device energy consumption.

In this paper, we present a digital twin (DT)-assisted adaptive video streaming scheme to enhance personalized quality-of-experience (PQoE). Since PQoE models are user-specific and time-varying, existing schemes based on universal and time-invariant PQoE models may suffer from performance degradation. To address this issue, we first propose a DT-assisted PQoE model construction method to obtain accurate user-specific PQoE models. Specifically, user DTs (UDTs) are respectively constructed for individual users, which can acquire and utilize users' data to accurately tune PQoE model parameters in real time. Next, given the obtained PQoE models, we formulate a resource management problem to maximize the overall long-term PQoE by taking the dynamics of users' locations, video requests, and buffer statuses into account. To solve this problem, a deep reinforcement learning algorithm is developed to jointly determine segment version selection, and communication and computing resource allocation. Simulation results on the real-world dataset demonstrate that the proposed scheme can effectively enhance PQoE compared with benchmark schemes.

The uplink (UL)/downlink (DL) decoupled access has been emerging as a novel access architecture to improve the performance gains in cellular networks. In this paper, we investigate the UL/DL decoupled access performance in cellular vehicle-to-everything (C-V2X). We propose a unified analytical framework for the UL/DL decoupled access in C-V2X from the perspective of spectral efficiency (SE). By modeling the UL/DL decoupled access C-V2X as a Cox process and leveraging the stochastic geometry, we obtain the joint association probability, the UL/DL distance distributions to serving base stations and the SE for the UL/DL decoupled access in C-V2X networks with different association cases. We conduct extensive Monte Carlo simulations to verify the accuracy of the proposed unified analytical framework, and the results show a better system average SE of UL/DL decoupled access in C-V2X.

This paper presents a novel approach for computing resource management of edge servers in vehicular networks based on digital twins and artificial intelligence (AI). Specifically, we construct two-tier digital twins tailored for vehicular networks to capture networking-related features of vehicles and edge servers. By exploiting such features, we propose a two-stage computing resource allocation scheme. First, the central controller periodically generates reference policies for real-time computing resource allocation according to the network dynamics and service demands captured by digital twins of edge servers. Second, computing resources of the edge servers are allocated in real time to individual vehicles via low-complexity matching-based allocation that complies with the reference policies. By leveraging digital twins, the proposed scheme can adapt to dynamic service demands and vehicle mobility in a scalable manner. Simulation results demonstrate that the proposed digital twin-driven scheme enables the vehicular network to support more computing tasks than benchmark schemes.

Pervasive edge computing (PEC) integrates the re-sources of peer devices at the network edge to serve users' latency-sensitive computation needs. Due to the high dynamics of the PEC environment, it is very challenging to achieve efficient service access control of edge servers and users without an “always-online” centralized server. In this paper, we propose a secure, efficient, and distributed service access control frame-work (SE-DAC) in the PEC environment. Specifically, SE-DAC extends the key-aggregate cryptosystem to achieve batch service authorization, where the service provider aggregates the access keys of different services to produce a constant-size aggregate key for the edge servers. Meanwhile, user authentication tasks are delegated to the edge servers by integrating secret sharing. The mutual authentication between the edge servers and the users is based on zero-round trip communication, such that the communication bandwidth cost is low. In addition, the service provider can efficiently revoke the authorization of the dropout or compromised edge servers in response to the dynamics of the PEC environment. Finally, we conduct numerical analysis and experiments to demonstrate that SE-DAC is highly computational efficient on service authorization, authentication, and revocation.

IoTs generally rely on resource-constrained devices to sense, relay, and collect data, which are highly vulnerable to Distributed-Denial-of-Services (DDOS) attacks on network throughput. In this paper, we propose a trust-based method to optimize the network throughput of IoTs under DDOS attacks. Specifically, with the assistance of a small number of dedicatedly deployed defense nodes as defenders, a network controller can first measure the behavior of other IoT nodes and categorize them into three types (i.e., innocent, selfish, and attack) through a well-designed trust evaluation model. Then, a Stackelberg game model is constructed accordingly, where defenders are leaders and other nodes are followers. We carefully define the utilities of the leaders and the followers in the game, and transform the optimization problem of the network throughput into the maximization problem of the defenders' utilities considering the utilities of the followers. We adopt the Dinkelbach Programming (DP)-based algorithm to solve the maximization problem such that a Stackelberg equilibrium can be reached with optimized network throughput. Extensive simulations are performed to demonstrate that the proposed defense method can significantly increase the IoT network throughput under different DDOS attack intensities.

Federated learning (FL) is a distributed machine learning paradigm with a promising future, which can preserve data privacy while training the global model collaboratively. However, FL is still facing model confidentiality issues. Therefore, in this paper, we propose a quantum key distribution (QKD) based secure FL scheme to facilitate FL model encryption against network eavesdropping attacks. Specifically, we introduce a stochastic resource allocation scheme for QKD to support FL networks. In the network, remote FL workers are connected to the server to train an aggregated global model in a distributed manner. However, due to the unpredictable number of workers at each location, the demand for secret-key rates to support secure model transmission to the server is not uniform. The proposed scheme can allocate QKD resources (i.e., wavelengths) in a way that minimizes the total cost given the stochastic demand. We formulate the optimization problem for the proposed scheme as a stochastic programming model. Numerical results demonstrate that the proposed scheme can successfully achieve the cost-minimizing objective while satisfying all uncertain demands and other security constraints.

Traffic flow estimation (TFE) plays an important role in intelligent transportation systems (ITS). Considering the prohibitive overhead of collecting and processing massive data in vehicular networks, it is more practical to use the sparse vehicular big data. In this paper, we focus on accurately estimating the urban traffic flow of vehicular networks only using a small portion of vehicular data. A new spatiotemporal machine learning model, named Graph Sampling and Aggregate Transformer (GSAT), is developed to improve the estimation accuracy by leveraging the inner correlation of traffic data. Specifically, the GSAT uses the graph sample and aggregate (GraphSAGE) model, a variety of graph neural network (GNN), to aggregate the spatial correlation and applies the Transformer model to capture the temporal correlation. We evaluate GSAT at multiple sparsity on the real world dataset of vehicular network, and it is demonstrated that GSAT can achieve accurate estimation with sparse big data.

Searchable Symmetric Encryption (SSE) is a promising method for users to store data in remote clouds securely and search them using keywords over an encrypted index. In this paper, we explore a new function named “keyword diverting” and propose a variant of SSE named Divertible Searchable Symmetric Encryption (DivSSE). Specifically, the index in DivSSE is encoded into an inverted, compressed, and encrypted format, by using the super-increasing sequence, symmetric homomorphic encryption (SHE), and a secure hash function. According to the homomorphic properties of SHE, users can construct a unique keyword diverting token, which can be utilized to update the encrypted index by obliviously merging data identifiers corresponding to different keywords without searching in advance and thus achieve keyword diverting. Moreover, based on function secret sharing, DivSSE can protect users' search patterns and reduce communication costs with the assistance of two independent clouds. Detailed security proof demonstrates that DivSSE can achieve parallel privacy, forward privacy, and backward privacy. Extensive performance evaluation also shows that DivSSE is efficient in terms of computational and communication overheads.

With AI-derived knowledge graph (KG), medical centers can recommend appropriate treatment options to physicians as references based on their patients' personal healthcare information (PHI). However, the treatment recommendation services may also cause serious privacy concerns. In this paper, we propose an efficient and privacy-preserving personalized treatment recommendation scheme with the aid of a third-party server. Specifically, a medical center denotes the KG of each disease by a directed graph with conditional edges and vertices that describe the treatment options and costs in different states. To prevent privacy leakage while reducing computational and management cost, the graphs are encrypted and then delegated to an honest-but-curious server. With the assistance of the server, physicians can set proper illness states and cost requirements according to patients' PHI, and correspondingly generate personalized ciphertexts to retrieve appropriate treatment options. The key component of the proposed scheme is a novel designed secure and flexible path comparison protocol, by tailoring a symmetric homomorphic encryption algorithm and combining it with a secure hash function. The protocol can enable the server to compare the uploaded ciphertexts with encrypted graphs in a secure and efficient way. Comprehensive security analysis indicates that the proposed scheme can meet desirable privacy requirements, and extensive experimental results demonstrate its practicality.

Mobile edge computing (MEC) is a promising computing paradigm enabling mobile devices to offload computation-intensive tasks to nearby edge servers for fast processing. In this paper, we investigate the computing task offloading in small-cell MEC systems. Considering the unevenly distributed mobile users, it is critical to balance the computing load among edge servers to better utilize the computing resources. To this end, we formulate a joint task offloading control and load balancing problem to minimize the average computational cost of users. The formulated problem is a mixed-integer nonlinear optimization problem and is intractable with system scale. To solve the problem in real time, we propose a reinforcement learning-based grouping and task offloading control (RLGTC) scheme. Specifically, we first decompose the problem into two sub-problems with the Tammer method, i.e., the task offloading control (ToC) and server grouping (SeG) sub-problems. Then, we devise two algorithms based on the Kalman Filter technique and reinforcement learning with Dueling Double DQN to solve them, respectively. Extensive data-driven experiments demonstrate the effectiveness of the RLGTC scheme in achieving load balancing and reducing UEs’ computational costs compared to the state-of-the-art benchmarks.

Mobile Edge Computing (MEC) is a promising paradigm to support high-quality time-sensitive applications. In this paper, we investigate the service migration (i.e., whether, when, and where to migrate the services) to seamlessly serve mobile users in small-cell MEC systems. The service migration is formulated as an optimization problem to minimize the long-term system average delay that consists of queuing, communication, and migration delays. Considering the dynamic user mobility and network conditions, the formulated problem is non-convex and difficult to solve in real time. To this end, we propose a Mobility-aware Service Migration scheme, named MSM, to make real-time decisions on service migrations by utilizing reinforcement learning (RL) approaches. Specifically, we first design a user classification mechanism based on users’ mobility patterns to reduce the complexity of decision-making. We then formulate the service migration as a Markov decision process and devise an RL-based framework to make service migration decisions in real time in the dynamic MEC environment. Extensive data-driven experiments demonstrate the efficacy of MSM in reducing the system average delay.

In this paper, we propose a Secure and Flexible Data Sharing (SFDS) scheme for distributed storage, where data owners can outsource their data to a distributed storage network and share the data with authorized users. To preserve confidentiality, all data are encrypted by data owners’ secret keys before being outsourced, and fine-grained access policies are enforced on the encrypted data (ciphertexts) to achieve flexible data sharing. Furthermore, based on the ciphertext puncturable encryption and the hierarchical identity-based encryption, we design an efficient key and ciphertext update mechanism, which enables data owners to update their secret keys and the corresponding ciphertexts periodically to deal with side-channel attacks and system vulnerabilities. Update tokens are constructed to directly derive new keys and ciphertexts. Through detailed security analysis, it is demonstrated that SFDS can achieve all three essential security properties, i.e., forward security, post-compromise security, and collusion attack resistance.

It is unpractical to conduct full-size data collection in ubiquitous IoT data systems due to the energy constraints of IoT sensors and large system scales. Although sparse sensing technologies have been proposed to infer missing data based on partial sampled data, they usually focus on data inference while neglecting the sampling process, restraining the inference efficiency. In addition, their inferring methods highly depend on data linearity correlations, which become less effective when data are not linearly correlated. In this paper, we propose, Compact IOT Data CollEction, namely CODE, to conduct precise data matrix sampling and efficient inference. Particularly, CODE integrates two major components, i.e., cluster-based matrix sampling and Generative Adversarial Networks (GAN)-based matrix inference, to reduce the data collection cost and guarantee the data benefits, respectively. In the sampling component, a cluster-based sampling approach is devised, in which data clustering is first conducted and then a two-step sampling is performed in accordance with the number of clusters and clustering errors. For the inference component, a GAN-based model is developed to estimate the full matrix, which consists of a generator network that learns to generate a fake matrix, and a discriminator network that learns to discriminate the fake matrix from the real one. A reference implementation of CODE is conducted under three operational large-scale IoT systems, and extensive data-driven experiment results are provided to demonstrate its efficiency and robustness.

Software-defined wireless sensor networks (SDWSNs) can be functionally affected by malicious sensor nodes that perform arbitrary actions, e.g., message dropping or flooding. The malicious nodes can degrade the availability of the network due to in-band communications and the inherent lack of secure channels in SDWSNs. In this article, we design a hierarchical trust management scheme for SDWSNs (namely, TSW) to detect potential threats inside SDWSNs while promoting node cooperation and supporting decision making in the forwarding process. TSW evaluates the trustworthiness of involved nodes and enables the detection of malicious behavior at various levels of the SDWSN architecture. We develop sensitive trust computational models to detect several malicious attacks. Furthermore, we propose separate trust scores and parameters for control and data traffic, respectively, to enhance the detection performance against attacks directed at the crucial traffic of the control plane. Furthermore, we develop an acknowledgment-based trust recording mechanism by exploiting some built-in SDN control messages. To ensure the resilience and honesty of the trust scores, a weighted averaging approach is adopted, and a reliability trust metric is defined. Through extensive analyses and numerical simulations, we demonstrate that TSW is efficient in detecting malicious nodes that launch several communications and trust management threats, such as black-hole, selective forwarding, denial of service, bad mouthing, and ON–OFF attacks.

Unmanned-aerial-vehicle (UAV)-assisted communications has attracted increasing attention recently. This article investigates air–ground coordinated communications system, in which trajectories of air UAV base stations (UAV-BSs) and access control of ground users (GUs) are jointly optimized. We formulated this optimization problem as a mixed cooperative–competitive game, where each GU competes for the limited resources of UAV-BSs to maximize its own throughput by accessing a suitable UAV-BS, and UAV-BSs cooperate with each other and design their trajectories to maximize the definedfair throughputto improve the total throughput and keep the GU fairness. Moreover, the action space of GUs is discrete, while that of UAV-BS is continuous. To tackle this hybrid action space issue, we transform the discrete actions into continuous action probabilities and propose a multiagent deep reinforcement learning (MADRL) approach, named air–ground probabilistic multiagent deep deterministic policy gradient (AG-PMADDPG). With well-designed rewards, AG-PMADDPG can coordinate two types of agents, UAV-BSs and GUs, to achieve their own objectives based on local observations. Simulation results demonstrate that AG-PMADDPG can outperform the benchmark algorithms in terms of throughput and fairness.

With the rapid development of mobile computing, mobile-edge computing (MEC) has increasingly become an essential means to meet the computing power requirements of intelligent networked vehicles. However, users with high mobility and coupled dynamics are rarely considered in the edge computing paradigms. In this article, we studied a UAV-assisted MEC system with multiplatoon vehicles. Our article aims to maximize the system’s weighted global energy efficiency, which can flexibly adjust each vehicle’s energy consumption according to user preferences and system needs. In particular, we design a controller for platooning vehicles based on a 2-D path-following model and Frenet frames, and model the coupled characteristics of air-to-ground communications and onboard computation. Furthermore, due to the nonconvexity of the objective function and constraints of the optimization problem, we propose an optimization algorithm based on the sequential quadratic programming (SQP) method. The simulation results show that the proposed method significantly surpasses conventional schemes.

To handle the exponentially growing service expectations from mobile users and circumvent the band switching slow rate, device-to-device (D2D) communication is receiving much research attention in the Internet of Things (IoT). While the emerging D2D nodes can support heterogeneous frequency bands [radio frequency (RF) including 2.4 GHz/5 GHz wireless local area network (WLAN), 38-GHz millimeter wave (mmWave), and visible light communication (VLC)], the physical constraints (e.g., blocking) require the user devices to dynamically switch between the bands in order to avoid the loss of connectivity and throughput degradation. In this article, we investigate an effective online link selection in hybrid RF-VLC scenarios for direct user data handling. First, we model the multiband selection issue as a multiarmed bandit (MAB) problem. The source/relay node acts as a player who gambles to maximize its long-term feedback/reward via selecting suitable arms, i.e., available bands (WLAN, mmWave, or VLC). Then, we propose an online, energy-aware band selection (EABS) methodology by leveraging three theoretically guaranteed MAB techniques [upper confidence bound (UCB), Thompson sampling (TS), and minimax optimal stochastic strategy (MOSS)] to derive optimal band selection policies. Based on these adopted policies, we propose three algorithms, namely, EABS-UCB, EABS-TS, and EABS-MOSS, to implement the EABS strategy, respectively. Extensive simulations demonstrate our proposed algorithms’ superior performance compared to the traditional link selection schemes regarding energy efficiency, average throughput, and convergence rate. In particular, EABS-MOSS emerges as the best algorithm as it exhibits near-optimal performance due to its flexibility to both stochastic and adversarial environments.

With the maturity of 5G cellular communication systems and mobile-edge computing (MEC), a large number of base stations (BSs) with edge-computing servers are densely deployed. There are extensive overlapping coverage areas among the BSs in which some heavy computational tasks from Internet of Things (IoT) devices can be divided and offloaded to multiple BSs via the coordinated multipoint (CoMP) technique for parallel processing. However, it is a challenging issue about how to make proper task offloading decisions among multiple connected BSs while satisfying delay requirements of multiple devices. To address this challenge, this article presents an efficient multidevice and multi-BSs task offloading scheme with the goal of minimizing the delay for completing the tasks of the devices. By conducting quantitative analysis of local delay and offloading delay, a nonlinear and nonconvex delay optimization offloading problem, which is based on the theory of noncooperative game, is formulated. We prove the existence of Nash equilibrium by analyzing the feature of the proposed offloading problem and further propose a distributed task offloading algorithm called DOLA. Finally, simulation experiments based on real-world data set from the Melbourne CBD area of Australia are conducted to validate the efficacy of our DOLA algorithm. Comparison experiments are also carried out to demonstrate the superiority of DOLA in comparison with some existing schemes.

Secure cross-domain authorization and authentication (AA) enable application service providers (ASPs) to allow users for resource access from different trusted domains. In this article, we propose a unified blockchain-assisted secure cross-domain AA framework for smart city, which can guarantee transparent cross-domain resource access while preserving user privacy. In the framework, ASPs can flexibly delegate their authentication capabilities to the blockchain, and users authorized by different ASPs can be authenticated by the blockchain where the authentication events are publicly audited and traced. Since the blockchain is publicly accessible, users’ sensitive identity attributes may be exposed during the authentication process. To address privacy leakage caused by the authentication events, several privacy-preserving techniques, including threshold-based homomorphic encryption, zero-knowledge proof, and random permutation, are exploited to hide users’ sensitive information on the blockchain. Moreover, to improve user revocation efficiency, we integrate a cryptographic accumulator and secure hash functions into the framework where ASPs are allowed to revoke their users through a global revocation contract. Our security analysis shows that the proposed framework can achieve all desirable security and privacy properties, and a proof-of-concept prototype has been developed to demonstrate the correctness and efficiency of the proposed framework.

The periodic transmission of the customers’ power consumption readings in the advanced metering infrastructure (AMI) is essential for energy management and billing. To collect the readings efficiently, the change and transmit approach is adopted in AMI (CAT AMI) so that the readings are reported only when there is enough change in the consumption. However, CAT AMI suffers from malicious customers who launch electricity-theft cyberattacks by manipulating their readings to illegally reduce their bills. These attacks can cause hefty financial losses and degrade the grid performance because the readings are used for grid management. In this article, the electricity-theft problem in CAT AMI networks is investigated. We first process a real power consumption readings data set to create a benign data set and propose a new set of cyberattacks to create malicious samples. We then develop a deep-learning-based electricity-theft detection solution to identify malicious customers for the CAT AMI network. The proposed detector uses both the customers’ transmission pattern and CAT readings to learn the correlation between them in order to enhance the detector’s ability in identifying electricity thefts. We conduct extensive experiments to evaluate the performance of our electricity-theft detector, and the results indicate that our detector can accurately detect malicious customers and achieve higher detection rate and lower false alarm than the detectors that are trained only on the CAT readings.

Space–air–ground-integrated networks (SAGINs) are deemed as a promising solution to support multifarious Internet of Vehicles (IoV) services with diversified Quality-of-Service (QoS) requirements in future communication networks. Network function virtualization (NFV) and software-defined networking (SDN) are two complementary and promising technologies to reduce the function provisioning cost and coordinate the heterogeneous physical resources in SAGIN. In this article, we investigate the online dynamic virtual network function (VNF) mapping and scheduling in SAGIN, considering the dynamicity of IoV services. The VNF live migration, VNF reinstantiation, and VNF rescheduling are enabled to increase the service acceptance ratio and service provider’s profits. Considering the heterogeneity of space, air, and ground nodes, we first model the migration cost and additional delay incurred by VNF live migration and reinstantiation. We then formulate the dynamic VNF mapping and scheduling jointly as a mixed-integer linear programming (MILP) problem with specified cost and delay models. We propose two Tabu search (TS)-based algorithms, i.e., TS-based VNF remapping and rescheduling (TS-MAPSCH) algorithm and TS-based pure VNF rescheduling (TS-PSCH) algorithm, to obtain suboptimal solutions to the MILP problem efficiently. Simulation results show that the proposed solution is very close to the optimum and that the proposed dynamic algorithms outperform existing works with respect to multiple performance metrics, including the service provider’s profit, service acceptance ratio, and QoS satisfaction level.

Real-time search is essential for accessing specific sensing data (SD) in vehicular networks to support safe, efficient, and intelligent road services. Considering the tremendous data volume, the SD search process should be carefully devised to avoid excessive retrieval and transmission delay. To alleviate the communication and computational burden for sensing devices and the cloud server, roadside edges are adopted to cache the SD in advance. Given a short lifetime of SD, the caching scheme is required to be efficient in facilitating both the search process and uplink/downlink transmission, which is quite challenging due to the coupling of resource allocation decisions. To guarantee the search efficacy and enhance the caching resource utilization, we propose a real-time search-driven caching (RSC) paradigm to enable the cooperation among storage-constrained edges. A hierarchical indexing framework is first introduced for cached data, based on which we then devise a search utility model to quantify the expected data freshness and response delay. With the objective of maximizing the long-term search reward, the RSC problem is formulated by jointly considering the search requests and utility model. A deep-reinforcement-learning-based caching (DRLC) method is proposed to solve the problem. Specifically, an action transition module is introduced to lower the computational complexity via reducing the selection space of caching actions. Extensive simulations are carried out based on the real trace data in Creteil, France, and results show that the intelligent DRLC method can improve the real-time search performance significantly comparing to the benchmark methods.

The fundamental communication paradigms in the next-generation mobile networks are shifting from connected things to connected intelligence. The potential result is that current communication-centric wireless systems are greatly stressed when supporting computation-centric intelligent services with distributed big data. This is one reason that makes federated learning come into being, it allows collaborative training over many edge devices while avoiding the transmission of raw data. To tackle the problem of model aggregation in federated learning systems, this article resorts to multiple reconfigurable intelligent surfaces (RISs) to achieve efficient and reliable learning-oriented wireless connectivity. The seamless integration of communication and computation is actualized by over-the-air computation (AirComp), which can be deemed as one of the uplink nonorthogonal multiple access (NOMA) techniques without individual information decoding. Since all local parameters are uploaded via noisy concurrent transmissions, the unfavorable propagation error inevitably deteriorates the accuracy of the aggregated global model. The goals of this work are to 1) alleviate the signal distortion of AirComp over shared wireless channels and 2) speed up the convergence rate of federated learning. More specifically, both the mean-square error (MSE) and the device set in the model uploading process are optimized by jointly designing transceivers, tuning reflection coefficients, and selecting clients. Compared to baselines, extensive simulation results show that 1) the proposed algorithms can aggregate model more accurately and accelerate convergence and 2) the training loss and inference accuracy of federated learning can be improved significantly with the aid of multiple RISs.

Spatially beamformed communication, which is achieved by antenna array and multiple-input and multiple-output (MIMO) technologies, has become the most critical technology to drastically increase spectrum utilization rate of 5G. To guarantee the successful deployment of 5G, different aspects of device design, particularly those related to radio-frequency front end and antenna array for enabling beamformed communication, have to be accurately verified. It is more cost effective to identify design imperfections through lab testing rather than using field testing-based trial and error approaches, especially for the explosive growth of 5G-enabled Internet of Things devices. In this article, a directly connected over-the-air (OTA) test solution for 5G MIMO OTA evaluations with the focus on dynamic beamforming devices is proposed. The proposed solution can mathematically achieve constructions among the S ports of base station (BS) and U ports of the receiving antenna array, which makes it possible for measuring the throughput of terminal with an adaptive beamforming array in an OTA way. All system parameters are emulated, including S elements BS antenna gain, U elements of receiving terminal performance, and 3-D$S{\times }U$propagation channel characteristics. To further validate the theoretical analysis and test procedure, a newly developed 4${\times }$4 5G MIMO device is measured in terms of its throughput. The results exactly reflect the true performance of the proposed solution under realistic operational conditions.

With the proliferation of cloud computing, data owners can outsource the spatial data from the Internet of Things devices to a cloud server to enjoy the pay-as-you-go storage resources and location-based services. However, the outsourced services may raise privacy concerns, since the cloud server may not be fully trusted for both data owners and search users. If the data owners and search users conventionally encrypt the spatial data and query requests, the efficiency and functionality of query processing are weakened. Most of the existing works only focus on spatial data search or keyword search and do not consider spatial keyword search over encrypted data. In this article, we first design a geometric range query (GRQ) scheme, which can generate an arbitrary geometric range to fit the search user’s desired spatial data while protecting location privacy. Furthermore, based on GRQ, we propose a multidimensional spatial keyword similarity search scheme with access control (MSSAC) by integrating the polynomial function and matrix transformation. Specifically, an access control strategy is defined by a role-based polynomial function, which is embedded in the vectors of indices and trapdoors to achieve efficient and lightweight access control. Moreover, MSSAC enables the cloud server to execute compute-then-compare operations for spatial keyword search in a privacy-preserving manner by leveraging techniques of randomizable permutation and matrix multiplication. The formal security analyses and extensive experiments demonstrate that GRQ and MSSAC preserve the privacy of data owners and search users while achieving efficient spatial keyword search.

Digital twin (DT) has attracted a lot of attention from both industry and academia since it was proposed over a decade ago. A DT can be viewed as a virtual implementation of a real physical system (PS) and used as a representation of the PS for various applications. Despite the great potential of DTs in various fields, implementing DTs to obtain the desired functionality is not always straightforward. Specifically, accurate real-time synchronization between the features at a PS and its DT is essential for the DT to represent the PS. In this case, appropriate networking support is a key component to enable future DT development and applications. Currently, the research on DTs from a networking standpoint is still at an early stage, and only limited work has been done on DT implementation in practical systems. To fill this gap, this article investigates networking-related issues for DTs. Based on the existing literature, a feature-based method is provided for describing the desired properties and quality of DTs from the networking perspective. A stage-based implementation framework is presented for creating large-scale DTs for complex PSs by considering various networking constraints. Networking-related challenging issues and open research topics are discussed at the end.

Renewable energy sources (RES) and low-carbon technology users play a vital role in modern power systems. However, RES generation is easily affected by the environment. Meanwhile, the load, such as electric vehicles (EVs) and prosumers, accounts for most low-carbon technology users. Their power is usually superimposed on peak loads without dispatching, which also exacerbates the instability of the power system. Current optimal dispatching mechanisms mainly rely on centralized organizations, while their dispatching process is not open and transparent. In this article, we propose a blockchain-based trustworthy dispatching approach for the distribution network in high renewable energy penetrated power systems. We first develop an optimal dispatching model considering EVs’ charging behavior and the prosumers’ economic benefits. With the model, prosumers can be dispatched to balance power and consume renewable energy, reducing the impact of disorderly charging on the grid and the abandonment of RES generation. An orderly charging iteration optimization (OCIO) algorithm is proposed to implement orderly EV charging while considering the charging cost and the period. We also propose a modified particle swarm optimization (mPSO) algorithm to publish dispatching tasks based on real-time power balance. Furthermore, blockchain is applied as an open and transparent ledger to record each entity’s power generation and consumption information, ensuring that the dispatching process is trustworthy. Finally, the effectiveness of the dispatching approach is verified in the modified IEEE 33-bus test system and Ethereum-based smart contracts.

In this article, we develop an analytical framework to study the impact of location-based relay selection strategy on the reliability of cognitive relay networks. By utilizing the tool of stochastic geometry, we first derive a closed-form expression for the reliability-enhanced region (RER), where relaying transmission can achieve higher transmission reliability than direct transmission. Then, we adopt the normalized reliability gain (NRG) to quantify the reliability benefit obtained by using relaying transmission compared to direct transmission, and we obtain the spatial distribution of NRG in the RER. Subsequently, by taking the spatial random nature of relays’ distribution into account, we investigate the reliability benefit obtained by secondary networks with the optimal location-based relay selection (OLB-RS) strategy. To reduce the feedback overhead during relay selection, we propose a region-aware relay selection (RA-RS) strategy and obtain the achievable reliability benefit. The results indicate that the reliability is highly dependent on the location of relay, and the OLB-RS strategy is to select the relay closest to the midpoint between the corresponding secondary source and destination.

Generally, power utilities can utilize smart-meter data to extract load patterns through load-profiling technologies, such as$K$-means clustering. To improve the efficiency of load profiling, both$K$-means clustering and smart-meter data can be outsourced to powerful clouds. However, clouds are not completely trustworthy: private meter data may be used for commercial interests;$K$-means clustering may also be performed with fewer iterations to save computational costs, which violates the integrity of outsourced clustering. In this article, therefore, a secure$K$-means-clustering scheme is proposed, called privacy-preserving and integrity-preserving clustering (PIPC), which aims to protect the privacy and integrity of load profiling. To this end, two techniques are designed: 1) encrypted distance measurement, in which a public comparison matrix is constructed by securely embedding a secret key matrix and 2) integrity assurance, in which a specific Stackelberg game is designed to create economic incentives. The former, as the core of$K$-means clustering, can protect the privacy of meter data. The latter ensures that clouds can obtain the maximum utility only when clouds execute$K$-means clustering in an honest manner, thereby preserving the integrity of outsourced computing. Experimental results demonstrate that PIPC reaches high clustering accuracy and computational efficiency for load profiling while retaining smart-meter data privacy and outsourced-clustering integrity.

In this paper, we propose a blockchain-based collaborative credential management scheme for anonymous authentication in space-air-ground integrated vehicular networks (SAGVN), namedSAG-BC. First, we build a consortium blockchain among service providers and design a distributed system setup (DSS) scheme to securely generate public parameters for issuing credentials. Second, we design a collaborative credential issuance (CCI) scheme to generate a succinct and easy-to-manage subscription credential. The credential can be used by users to access different access points in SAGVN efficiently without revealing true identities from the authentication messages. With co-designs of zero-knowledge proofs and succinct on-chain commitments,SAG-BCprovides efficient verifiability and incentives for credential management operations in SAGVN. By doing so, expensive on-chain storage and computational overheads are reduced in the DSS and CCI. Finally, we conduct a thorough security analysis to demonstrate thatSAG-BCachieves security and verifiability for credential management in SAGVN. We set up a real-world blockchain network and conduct extensive experiments to show the feasibility and efficiency ofSAG-BC.

As the long-term evolution (LTE) system is reaching maturity and the fifth-generation (5G) systems are being commercially deployed, researchers have turned their attention to the development of next-generation wireless networks. Compared to current wireless networks, on the one hand, next-generation wireless networks are expected to achieve significantly higher capacity, extremely low latency, ultra-high reliability, as well as massive and ubiquitous connectivity for supporting diverse disruptive applications (e.g., virtual reality (VR), augmented reality (AR), and industry 4.0). On the other hand, the evolution toward next-generation wireless networks requires a paradigm shift from the communication-oriented design to a multi-functional design, including communication, sensing, imaging, computing, and localization. Looking back at the history of wireless communication systems, multiple access (MA) techniques have been key enablers. From the first generation (1G) to the fifth generation (5G), orthogonal multiple access (OMA) schemes are mainly employed, where multiple users are allotted in orthogonal frequency/time/code resources, and the uplink transmission of the code code-division multiple-access (CDMA) uses non-orthogonal code resources. However, given the enormous challenges and diverse services of next-generation wireless networks, which significantly differ from that in current and previous wireless networks, existing MA schemes may not be applicable. As a result, a fundamental issue is the design of next-generation multiple access (NGMA) techniques. The key concept of NGMA is to enable a very large number of users/devices to be efficiently, flexibly, and intelligently connected with the network over the given wireless radio resources to not only satisfy stringent communication requirements but also realize heterogeneous functions. The investigation of NGMA is still in the infancy stage, and extensive research efforts have to be devoted to areas, including but not limited to 1) the development of new MA schemes, such as non-orthogonal multiple access (NOMA) and space division multiple access (SDMA), which are capable of achieving higher bandwidth efficiency and higher connectivity compared with conventional MA schemes; 2) the development of innovative techniques, such as reconfigurable metasurfaces, random access, advanced modulation, and channel coding, which are beneficial to the overall design of NGMA; and 3) the exploitation of advanced machine learning (ML) tools and big data techniques for providing effective solutions to address newly emerging NGMA problems.

As the long-term evolution (LTE) system is reaching maturity and the fifth-generation (5G) systems are being commercially deployed, researchers have turned their attention to the development of next-generation wireless networks. Compared to current wireless networks, on the one hand, next-generation wireless networks are expected to achieve significantly higher capacity, extremely low latency, ultra-high reliability, as well as massive and ubiquitous connectivity for supporting diverse disruptive applications (e.g., virtual reality (VR), augmented reality (AR), and industry 4.0). On the other hand, the evolution toward next-generation wireless networks requires a paradigm shift from the communication-oriented design to a multi-functional design, including communication, sensing, imaging, computing, and localization. Looking back at the history of wireless communication systems, multiple access (MA) techniques have been key enablers. From the first generation (1G) to the fifth generation (5G), orthogonal multiple access (OMA) schemes are mainly employed, where multiple users are allotted in orthogonal frequency/time/code resources, and the uplink transmission of the code code-division multiple-access (CDMA) uses non-orthogonal code resources. However, given the enormous challenges and diverse services of next-generation wireless networks, which significantly differ from that in current and previous wireless networks, existing MA schemes may not be applicable. As a result, a fundamental issue is the design of next-generation multiple access (NGMA) techniques. The key concept of NGMA is to enable a very large number of users/devices to be efficiently, flexibly, and intelligently connected with the network over the given wireless radio resources to not only satisfy stringent communication requirements but also realize heterogeneous functions. The investigation of NGMA is still in the infancy stage, and extensive research efforts have to be devoted to areas, including but not limited to 1) the development of new MA schemes, such as non-orthogonal multiple access (NOMA) and space division multiple access (SDMA), which are capable of achieving higher bandwidth efficiency and higher connectivity compared with conventional MA schemes; 2) the development of innovative techniques, such as reconfigurable metasurfaces, random access, advanced modulation, and channel coding, which are beneficial to the overall design of NGMA; and 3) the exploitation of advanced machine learning (ML) tools and big data techniques for providing effective solutions to address newly emerging NGMA problems.

Due to the explosive growth in the number of wireless devices and diverse wireless services, such as virtual/augmented reality and Internet-of-Everything, next generation wireless networks face unprecedented challenges caused by heterogeneous data traffic, massive connectivity, and ultra-high bandwidth efficiency and ultra-low latency requirements. To address these challenges, advanced multiple access schemes are expected to be developed, namely next generation multiple access (NGMA), which are capable of supporting massive numbers of users in a more resource- and complexity-efficient manner than existing multiple access schemes. As the research on NGMA is in a very early stage, in this paper, we explore the evolution of NGMA with a particular focus on non-orthogonal multiple access (NOMA), i.e., the transition from NOMA to NGMA. In particular, we first review the fundamental capacity limits of NOMA, elaborate on the new requirements for NGMA, and discuss several possible candidate techniques. Moreover, given the high compatibility and flexibility of NOMA, we provide an overview of current research efforts on multi-antenna techniques for NOMA, promising future application scenarios of NOMA, and the interplay between NOMA and other emerging physical layer techniques. Furthermore, we discuss advanced mathematical tools for facilitating the design of NOMA communication systems, including conventional optimization approaches and new machine learning techniques. Next, we propose a unified framework for NGMA based on multiple antennas and NOMA, where both downlink and uplink transmissions are considered, thus setting the foundation for this emerging research area. Finally, several practical implementation challenges for NGMA are highlighted as motivation for future work.

Future networks incorporate artificial intelligence to enable smart resource management and adaptive service provisioning. With a heterogeneous architecture and a large number of users in future networks, transparent and decentralized data sharing is required to promote data circulation and break data silos, for which blockchain is a potential solution to allow intelligent access permission control. However, it remains a challenging task to achieve flexible authorization management for blockchain-based data sharing and efficient key update for multi-users in case of key exposure. In this paper, we propose an intelligent blockchain-based data-sharing scheme with key update for future networks. First, we design a new encryption scheme, where keywords of data are extracted using machine learning algorithms that are published on the blockchain. Then, keywords of data and time validity are used to encrypt different types of data for flexible data authorization. Second, using hierarchical identity-based encryption, we construct an efficient key update mechanism, where update tokens are generated by invoking a smart contract deployed on the blockchain to facilitate key and ciphertext updates. We formally prove that the proposed scheme can guarantee three essential security properties: forward security, post-compromise security, and collusion attack resistance. On-chain and off-chain experiment results are provided to demonstrate that the proposed scheme can achieve computational and communication efficiency for key and ciphertext updates.

Data are generated by Internet of Things (IoT) devices and centralized at a cloud server, that can later be traded with third parties, i.e., data marketing, to enable various data-intensive applications. However, the centralized approach is recently under debate due to the lack of (1) transparent and distributed marketplace management, and (2) marketing fairness for both IoT users (data sellers) and third parties (data buyers). In this paper, we propose a Blockchain-Cloud Transparent Data Marketing (Block-DM) with consortium management and executable fairness. First, we introduce a hybrid data-marketing architecture, where the cloud acts as an efficient data management unit and a consortium blockchain serves as a transparent marketing controller. Under the architecture, consent-based secure data trading and identity privacy for data owners are achieved with the distributed credential issuance and threshold credential openings. Second, with a consortium committee, we design a fair on/off-chain data marketing protocol. By financial incentives and succinct ‘commitments’ of marketing operations, the protocol can achieve the marketing fairness and effective detection of unfair marketing operations. We demonstrate the security of Block-DM with thorough analysis. We conduct extensive experiments with a consortium blockchain network on Hyperledger Fabric to show the feasibility and practicality of Block-DM.

With the increasing traffic volume, enterprises choose to outsource their middlebox services, such as deep packet inspection, to the cloud to acquire rich computational and communication resources. However, since the traffic is redirected to the public cloud, information leakages, such as packet payload and inspection rules, arouse privacy concerns of both middlebox owner and packet senders. To address the concerns, we propose an efficient verifiable deep packet inspection (EV-DPI) scheme with strong privacy guarantees. Specifically, a two-layer architecture is designed and deployed over two non-collusion cloud servers. The first layer fast filters out most of legitimate packets and the second layer supports exact rule matching. During the inspection, the privacy of packet payload and the confidentiality of inspection rules are well preserved. To improve the efficiency, only fast symmetric crypto-systems, such as hash functions, are used. Moreover, the proposed scheme allows the network administrator to verify the execution results, which offers a strong control of outsourced services. To validate the performance of the proposed EV-DPI scheme, we conduct extensive experiments on the Amazon Cloud. Large-scale dataset (millions of packets) is tested to obtain the key performance metrics. The experimental results demonstrate that EV-DPI not only preserves the packet privacy, but also achieves high packet inspection efficiency.

Middlebox is an intermediary network equipment which can be outsourced to remote cloud servers for low-cost and customizable network services, such as load balancer and intrusion detection. A fundamental function of the middlebox is packet inspection, where both the packet header and payload are extracted and analyzed based on inspection rules. However, as the packet may contain sensitive individual or organizational information, it may raise severe privacy concerns without proper countermeasures. In this article, we propose a secure and versatile packet inspection scheme for outsourced middlebox. The proposed scheme builds upon two non-collusion cloud servers, where the first server conducts the inspection task over the ciphertext domain and the second reveal the inspection results. By doing so, the proposed scheme achieves versatile inspection functionalities: range-query-based header inspection and token-based payload inspection, while preserving the privacy of packet header, payload, and inspection rules. Moreover, we identify and address two challenging issues in the state-of-the-art literatures. First, we tailor the design of mis-operation resistant searchable homomorphic encryption (MR-SHE) and somewhat homomorphic encryption in the two-server model, to resistoffline dictionary attack on payload headers. Second, we propose a key management mechanism with compelled access for the middlebox, to achievefine-grained probable cause privacy. We also conduct extensive experiments and compare the results with existing schemes to demonstrate the feasibility of the proposed scheme.

In this article, a fine-grained query authorization scheme with integrity verification is proposed over encrypted spatial data for location-based services (LBS). The fine-grained query authorization is enabled based on a distribution of the spatial data by employing a non-uniform partition in the spatial domain to generate a density-based space filling curve (DSC), which can be used to generate index values for querying and transformation keys. The transformation keys can be used to generate query tokens for a secure spatial query as well as construct a transformation key tree whose subtree can be distributed by the LBS provider to an authorized user as transformation key for query tokens generation. Furthermore, the proposed scheme constructs a Merkle quad tree (MQ-tree) to support integrity verification by aggregating a digest of the spatial data based on the DSC and employing the MQ-tree as a verification structure. The LBS provider can share a subtree of the MQ-tree to authorized user as his verification structure, which corresponds to the transformation key of the authorized user. In this way, the authorized user can only generate the valid query tokens and verify the query results in his authorized region. The security properties of the proposed scheme is discussed, and extensive experimental results demonstrate the high efficiency of verification structure generation and verification operations.

DNA similarity search has been widely applied in human genomic studies including DNA marking, genomic sequencing and genetic disease prediction. Meanwhile, with the explosive growth of data, users are increasingly inclining to store DNA data on the cloud for saving local cost. However, the high sensitivity of DNA data has forced the government to strictly control its acquisition and utilization. One potential solution is to encrypt DNA data before outsourcing them to the cloud. Nevertheless, private DNA similarity query has been an active research issue, state-of-the-art results are still defective in security, functionality, and efficiency. In this article, we propose EFSS, an efficient and fine-grained similarity search scheme over encrypted DNA data. In specific, first, we design an approximation algorithm to efficiently calculate the edit distances between two sequences. Second, we put forward a novel Boolean search strategy to achieve complicated logic queries such as mixed “AND” and “NO” operations on genes. Third, data access control is also supported in our EFSS through a variant of polynomial based design. Moreover, the K-means clustering algorithm is exploited to further improve the efficiency of execution. In the end, security analysis and extensive experiments demonstrate the high performance of EFSS compared with existing schemes.

In this article, we propose SPADE, an encrypted data deduplication scheme that resists compromised key servers and frees users from the key management problem. Specifically, we propose a proactivization mechanism for the servers-aided message-locked encryption (MLE) to periodically substitute key servers with newly employed ones, which renews the security protection and retains encrypted data deduplication. We present a servers-aided password-hardening protocol to resist dictionary guessing attacks. Based on the protocol, we further propose a password-based layered encryption mechanism and a password-based authentication mechanism and integrate them into SPADE to enable users to access their data only using their passwords. Provable security and high efficiency of SPADE are demonstrated by comprehensive analyses and experimental evaluations.

The rapid development of the smart healthcare system makes the early-stage detection of dementia disease more user-friendly and affordable. However, the main concern is the potential serious privacy leakage of the system. In this article, we take Alzheimer's disease (AD) as an example and design a convenient and privacy-preserving system namedADDetectorwith the assistance of Internet of Things (IoT) devices and security mechanisms. Particularly, to achieve effective AD detection,ADDetectoronly collects user's audio by IoT devices widely deployed in the smart home environment and utilizes novel topic-based linguistic features to improve the detection accuracy. For the privacy breach existing in data, feature, and model levels,ADDetectorachieves privacy-preserving by employing a unique three-layer (i.e., user, client, cloud, etc.) architecture. Moreover,ADDetectorexploitsfederated learning (FL) based schemeto ensure the user owns the integrity of raw data and secure the confidentiality of the classification model and implementdifferential privacy (DP) mechanismto enhance the privacy level of the feature. Furthermore, to secure the model aggregation process between clients and cloud in FL-based scheme, a novelasynchronous privacy-preserving aggregation frameworkis designed. We evaluateADDetectoron 1010 AD detection trials from 99 health and AD users. The experimental results show thatADDetectorachieves high accuracy of 81.9% and low time overhead of 0.7 s when implementing all privacy-preserving mechanisms (i.e., FL, DP, and cryptography-based aggregation).

For several industrial applications, a sole data owner may lack sufficient training samples to train effective machine learning based models. As such, we propose a federated learning (FL) based approach to promote privacy-preserving collaborative machine learning for applications in smart industries. In our system model, a model owner initiates an FL task involving a group of workers, i.e., data owners, to perform model training on their locally stored data before transmitting the model updates for aggregation. There exists a tradeoff between service latency, i.e., the time taken for the training request to be completed, and age of information (AoI), i.e., the time elapsed between data aggregation from the deployed industrial Internet of Things devices to completion of the FL-based training. On one hand, if the data are collected only upon the model owner's request, the AoI is low. On the other hand, the service latency incurred is more significant. Furthermore, given that different training tasks may have varying AoI requirements, we propose a contract-theoretic task-aware incentive scheme that can be calibrated based on the weighted preferences of the model owner toward AoI and service latency. The performance evaluation validates the incentive compatibility of our contract amid information asymmetry, and shows the flexibility of our proposed scheme toward satisfying varying preferences of AoI and service latency.

In this article, a two-level soft-slicing scheme is proposed for 5G-and-beyond radio access networks to support ultrareliable and low-latency communications (URLLC) and enhanced mobile broadband (eMBB) services with delay/reliability and throughput requirements, respectively. At the network level, we first determine the number of radio resources required for eMBB services and analyze the delay violation probability for URLLC services. Then, an integer nonlinear program is formulated for the network-level resource preallocation. Since the formulated problem is NP-complete, a low-complexity heuristic algorithm is proposed to obtain near-optimal solutions. Given the preallocated resources at each gNodeB (gNB), a gNB-level resource scheduling scheme is designed to enable real-time resource sharing among URLLC services considering the reliability and delay requirements. Simulation results show that the proposed soft-slicing scheme meets stringent quality-of-service requirements for both URLLC and eMBB services and achieves high resource utilization efficiency when compared with conventional hard resource slicing schemes.

In this paper, we study Space-Air-Ground integrated Vehicular Network (SAGVN), and propose an online control framework to dynamically slice the SAG spectrum resource for isolated vehicular services provisioning. In particular, at a given time slot, the system makes online decisions on the request admission and scheduling, UAV dispatching, and resource slicing for different services. To characterize the impact of those parameters, we construct a time-averaged queue stability criteria by taking queue backlogs of all services into consideration, and formulate a system revenue function which incorporates the time-averaged system throughput and UAV dispatching cost. The objective is to maximize the system revenue while stabilizing the time-averaged queue, which falls into the scope of Lyapunov optimization theory. By bounding the drift-plus-penalty, the original problem can be decoupled into four independent subproblems, each of which is readily solved. The merits of our control framework are three-fold: 1) the system is able to admit and process as many requests as possible (i.e., maximizing the time-averaged throughput); 2) the time-averaged UAV dispatching cost is minimized; and 3) service queues are stabilized in the long-term. Extensive simulations are carried out, and the results demonstrate that the control framework can effectively achieve the system revenue maximization and queueing stabilization. Moreover, it can balance the trade-off among system throughput, UAV dispatching cost, and queueing states via parameter tuning. Compared with the fixed slicing, our dynamic slicing can react to the vehicular environment rapidly and achieve an average 26% of throughput improvement.

In this paper, we propose a two-level adaptive resource allocation (TARA) framework to support vehicular safety message transmissions. In particular, three types of safety messages are considered in urban vehicular networks, i.e., event-triggered messages for urgent condition warnings, periodic messages for vehicular status notifications, and messages for environmental perception. Roadside units are deployed for network management, and thus messages can be transmitted through either vehicle-to-infrastructure or vehicle-to-vehicle connections. To satisfy the requirements of different message transmissions, TARA framework consists of a group-level resource reservation module and a vehicle-level resource allocation module. Particularly, the resource reservation module is designed to allocate resources to support different types of message transmissions for each vehicle group at the first level. To learn the implicit relationship between the resource demand and message transmission requests, a supervised learning model is devised in the resource reservation module, where to obtain the training data we further propose a sequential resource allocation (SRA) scheme. Based on historical network information, SRA scheme offline optimizes the allocation of sensing resources, i.e., choosing vehicles to provide perception data, and communication resources. With resources reserved for each group, the vehicle-level resource allocation module is then devised to distribute specific resources for each vehicle to satisfy the differential requirements in real-time. Extensive simulation results demonstrate the effectiveness of TARA framework in terms of the high packet delivery ratio and low latency for message transmissions, and the high quality of collective environmental perception.

In this paper, we propose an efficient and anonymous authentication protocol with a succinct multi-subscription credential (AnMsc) in Space-air-ground integrated vehicular networks (SAGVN). First, we adopt a subscription-based service model in SAGVN. Specifically, vehicular users (VEs) can subscribe to network services and conduct direct mutual authentication with subscribed access points (APs) to avoid message exchanges with VEs’ home network. Early application data can also be transmitted with authentication messages to improve communication efficiency. Second, we carefully tailor the design of the redactable signature and propose an efficient credential management mechanism in SAGVN. Multiple service subscriptions can be embedded into a succinct (constant-size) credential. With the credential, VEs can anonymously access any subscribed AP without revealing other subscription information. Thorough security analysis and comprehensive performance evaluation demonstrate that AnMsc can guarantee key-exchange security, VE anonymity, and service fairness while ensuring credential management and authentication efficiency.

Secure and efficient access authentication is one of the most important security requirements for vehicular networks, but it is difficult to fulfill due to potential security attacks and long authentication delay caused by high vehicle mobility, etc. Most of the existing authentication protocols, either do not consider attacks like single point of failure or do not focus on reducing authentication delay. To address these issues, we introduce an edge-assisted decentralized authentication (EADA) architecture, which provides secure and more communication-efficient authentication by enabling an authentication server to delegate its authentication capability to distributed edge nodes (ENs) such as roadside units (RSUs) and base stations (BSs). Under the architecture, we propose a threshold mutual authentication protocol that supports fast handover, which involves two scenarios, Auth-I and Auth-II. Auth-I only happens once when a vehicle tries to access the network for the first time, while Auth-II happens when a vehicle seamlessly roams between two ENs, i.e., handover. Specifically, for Auth-I, each vehicle can be cooperatively authenticated by$t$out of$n$ENs with identity-based signature techniques to obtain an authentication token and the involved ENs can be efficiently authenticated in a batch by the vehicle. For Auth-II, the vehicle can utilize the token as its private credential to achieve fast handover based on identity-based signature without interacting with multiple ENs, which further reduces the authentication delay significantly. In addition, we design a flexible method to support dynamic joining and leaving of ENs without the assistance of a trusted center. We demonstrate that the proposed protocol is secure and efficient through security analysis and performance evaluation.

In this paper, we investigate unmanned aerial vehicle (UAV) assisted physical layer security in multi-beam satellite enabled vehicle communications. Particularly, the UAV is exploited as a relay to improve the secure satellite-to-vehicle link, and simultaneously serves as a jammer by deliberately generating artificial noise (AN) to confuse Eve. The satellite beamforming (BF) and UAV power allocation (PA) are jointly optimized to maximize the secrecy rate of the legitimate user within a target beam while guaranteeing the quality of service (QoS) of users within other beams. Since the problem is nonconvex, we first convert it into an equivalent two-stage problem. Then, the outer-stage problem is solved by using one-dimensional search, and the inner-stage problem is transformed to a bi-convex problem by using the semi-definite relaxation (SDR) and Charnes Cooper transformation. To solve the inner-stage bi-convex problem, we propose an iterative alternating optimization algorithm, where the optimal BF is obtained by semi-definite programming (SDP), and the optimal UAV PA is subsequently obtained by solving the reformulated fractional programming problem with an iterative Dinkelbach method. The tightness of SDR and the complexity of our proposed approach are analyzed, and extensive simulations are carried out to evaluate the effectiveness of our proposed approach.

Platoon-based vehicular cyber-physical systems have gained increasing attention due to their potentials in improving traffic efficiency, capacity, and saving energy. However, external uncertain disturbances arising from mismatched model errors, sensor noises, communication delays and unknown environments can impose a great challenge on the constrained control of vehicle platooning. In this paper, we propose a closed-loop min-max model predictive control (MPC) with causal disturbance feedback for vehicle platooning. Specifically, we first develop a compact form of a centralized vehicle platooning model subject to external disturbances, which also incorporates the lower-level vehicle dynamics. We then formulate the uncertain optimal control of the vehicle platoon as a worst-case constrained optimization problem and derive its robust counterpart by semidefinite relaxation. Thus, we design a causal disturbance feedback structure with the robust counterpart, which leads to a closed-loop min-max MPC platoon control solution. Even though the min-max MPC follows a centralized paradigm, its robust counterpart can keep the convexity and enable the efficient and practical implementation of current convex optimization techniques. We also derive a linear matrix inequality (LMI) condition for guaranteeing the recursive feasibility and input-to-state practical stability (ISpS) of the platoon system. Finally, simulation results are provided to verify the effectiveness and advantage of the proposed MPC in terms of constraint satisfaction, platoon stability and robustness against different external disturbances.

Differential privacy (DP) has gained popularity in truth discovery recently due to its strong privacy guarantee. However, existing DP mechanisms for streaming data publication are not suitable for truth discovery as they fail to consider the different reliabilities of individuals, while the DP-based approaches for truth discovery are not suitable for streaming data because they ignore the correlations between truths over time. Directly applying these existing methods to streaming crowdsourced data would lead to low accuracy of the discovered truth. To solve this problem, in this paper, we propose an edge computing based privacy-preserving truth discovery mechanism, named PrivSTD, for streaming crowdsourced data to realize high accuracy of discovered truth while protecting the privacy of workers. Specifically, edge servers are introduced between the untrusted cloud server and workers to securely calculate the local truths and workers’ reliabilities. A truth-dependent budget recycle mechanism is proposed for each edge server to adaptively determine the perturbed timestamp and allocate the privacy budget according to the changing pattern of local truths. Besides, a reliability-based perturbation mechanism is proposed to reduce the perturbation magnitude on the basis of worker's reliability. We theoretical analyze the data utility and computation cost of PrivSTD, and prove that PrivSTD can satisfy$w$-event ($\epsilon,\delta$)-differential privacy. Extensive experimental results on synthetic and real-world datasets demonstrate that PrivSTD achieves better utility than the state-of-the-art approaches.

In this paper, the content service provision of information-centric vehicular networks (ICVNs) is investigated from the aspect of mobile edge caching, considering the dynamic driving-related context information. To provide up-to-date information with low latency, two schemes are designed for cache update and content delivery at the roadside units (RSUs). The roadside unit centric (RSUC) scheme decouples cache update and content delivery through bandwidth splitting, where the cached content items are updated regularly in a round-robin manner. The request adaptive (ReA) scheme updates the cached content items upon user requests with certain probabilities. The performance of both proposed schemes are analyzed, whereby the average age of information (AoI) and service latency are derived in closed forms. Surprisingly, the AoI-latency trade-off does not always exist, and frequent cache update can degrade both performances. Thus, the RSUC and ReA schemes are further optimized to balance the AoI and latency. Extensive simulations are conducted on SUMO and OMNeT++ simulators, and the results show that the proposed schemes can reduce service latency by up to 80 percent while guaranteeing content freshness in heavily loaded ICVNs.

In mobile crowdsensing, location-based task recommendation requires each data requester to submit a task-related geometric range to crowdsensing service providers such that they can match suitable workers within this range. Generally, a trusted server (i.e., database owner) should be deployed to protect location privacy during the process, which is not desirable in practice. In this paper, we propose the location privacy-preserving task recommendation (PPTR) schemes with geometric range query in mobile crowdsensing without the trusted database owner. Specifically, we first propose a PPTR scheme with linear search complexity, named PPTR-L, based on a two-server model. By leveraging techniques of polynomial fitting and randomizable matrix multiplication, PPTR-L enables the service provider to find the workers located in the data requester’s arbitrary geometric query range without disclosing the sensitive location privacy. To further improve query efficiency, we design a novel data structure for task recommendation and propose PPTR-F to achieve faster-than-linear search complexity. Through security analysis, it is shown that our schemes can protect the confidentiality of workers’ locations and data requesters’ queries. Extensive experiments are performed to demonstrate that our schemes can achieve high computational efficiency in terms of geometric range query.

In this article, we investigate the routing problem of packet networks through multiagent reinforcement learning (RL), which is a very challenging topic in distributed and autonomous networked systems. In specific, the routing problem is modeled as a networked multiagent partially observable Markov decision process (MDP). Since the MDP of a network node is not only affected by its neighboring nodes' policies but also the network traffic demand, it becomes a multitask learning problem. Inspired by recent success of RL and metalearning, we propose two novel model-free multiagent RL algorithms, named multiagent proximal policy optimization (MAPPO) and multiagent metaproximal policy optimization (meta-MAPPO), to optimize the network performances under fixed and time-varying traffic demand, respectively. A practicable distributed implementation framework is designed based on the separability of exploration and exploitation in training MAPPO. Compared with the existing routing optimization policies, our simulation results demonstrate the excellent performances of the proposed algorithms.

The emergency of federated learning (FL) enables distributed data owners to collaboratively build a global model without sharing their raw data, which creates a new business chance for building data market. However, in practical FL scenarios, the hardware conditions and data resources of the participant clients can vary significantly, leading to different positive/negative effects on the FL performance, where the client selection problem becomes crucial. To this end, we proposeAUCTION, anAutomated and qUality-awareClient selecTIONframework for efficient FL, which can evaluate the learning quality of clients and select them automatically with quality-awareness for a given FL task within a limited budget. To designAUCTION, multiple factors such as data size, data quality, and learning budget that can affect the learning performance should be properly balanced. It is nontrivial since their impacts on the FL model are intricate and unquantifiable. Therefore,AUCTIONis designed to encode the client selection policy into a neural network and employ reinforcement learning to automatically learn client selection policies based on the observed client status and feedback rewards quantified by the federated learning performance. In particular, the policy network is built upon an encoder-decoder deep neural network with an attention mechanism, which can adapt to dynamic changes of the number of candidate clients and make sequential client selection actions to reduce the learning space significantly. Extensive experiments are carried out based on real-world datasets and well-known learning models to demonstrate the efficiency, robustness, and scalability ofAUCTION.

After several decades of development on cyber security techniques, one clear conclusion can be drawn: no cyber security solution can completely remove the risks faced by the users. In this regard, cyber-insurance has been introduced as a means to enable the users to alleviate the damage from the cyber threats by transferring the cyber risks to an insurer. In this article, we study a cloud security service market, which is composed of cloud users and cloud security service vendors (CSSVs). The CSSVs work as the insurers for selling the cloud security plan, which is consisted of cloud security service and cloud-insurance. The users in the cloud platform can purchase the cloud security plan from the CSSVs to secure their cloud service. If the cloud service is attacked and loss happens, the users will receive the claim from the CSSVs. To lower the successful attack probability, the CSSV has an incentive to invest in improving its cloud security service. Specifically, we model and study the cloud security service market in the framework of a two-stage Stackelberg game. On the upper stage, the CSSVs lead to decide on their own strategies, i.e., the price of the cloud security plan and the security investment to improve their offered cloud security service. On the lower stage, the users follow to decide on the purchase of the cloud security plan according to the price of the cloud security plan and the perceived cyber breach probability of the cloud security service. We analytically verify that the Stackelberg equilibrium exists and is unique. Extensive simulations have been conducted to evaluate the performance of the Stackelberg game. The performance evaluation shows some insightful results. For example, when the users have strong interdependency, the profits of the CSSVs become lower.

Targeting at implementing the next generation radio access networks (RANs) with virtualized network components, the open RAN (O-RAN) has been regarded as a novel paradigm towards fully open, virtualized and interoperable RANs. Through particularly introducing RAN intelligent controllers (RICs), machine learning (ML) can be unprecedentedly installed, adapting to various vertical applications and deployment environments without sophisticated planning efforts. However, the O-RAN also suffers two critical challenges of load balancing and frequent handovers in the massive base station (BS) deployment. In this paper, an intelligent user access control scheme with deep reinforcement learning (DRL) is proposed. To optimize the performance of distributed deep Q-networks (DQNs) trained by user equipments (UEs), a federated DRL-based scheme is proposed with a global model server installed in the RIC to update the DQN parameters. To further predictively train a global DQN with acceptable signaling overheads, the upper confidence bound (UCB) algorithm to select the optimal UE set and a dueling structure to decompose the DQN parameters are developed. With the proposed scheme, each UE effectively maximizes the long-term throughput and avoids frequent handovers. The simulation results well justify the outstanding performance of the proposed scheme over the-state-of-the-arts, to serve as references for the O-RAN standardization.

With the help of non-orthogonal multiple access (NOMA), the number of connections of the beamspace multiple-input multiple-output (MIMO) systems can be improved with enhanced sum-rate performance, which constitutes beamspace MIMO-NOMA. Thus, most relevant papers focus on improving the system sum rate, which may inflict unbearable rate loss to weak users. To ensure the achievable rates of weak users, we maximize and analyze the minimal rate of the system in the single-beam case as well as the multi-beam case, where two completely different phenomena are revealed. Particularly, in the single-beam case, the maximized minimal rate of the beamspace MIMO-NOMA always grows rapidly with the signal-to-noise-ratio (SNR), and is larger than that of the beamspace MIMO using orthogonal multiple access (beamspace MIMO-OMA). However, in the multi-beam case, the maximized minimal rate of the beamspace MIMO-NOMA grows slower and slower in the high-SNR region, where it is smaller than that of the beamspace MIMO-OMA. To explain this difference, it is disclosed that the intra-beam interference in the single-beam case is ofsuccessive pattern, which is proved to have no limit on the max-min rate. In contrast, the inter-beam interference in the multi-beam case is ofmutual pattern, which is proved to restrict the max-min rate to a derived upper bound.

Information freshness is essential to industrial wireless networks (IWNs) and can be quantified by the age-of-information (AoI) metric. This paper addresses an AoI-aware spectrum sharing (AgeS) problem in IWNs, where multiple device-to-device (D2D) links opportunistically access the spectrum to satisfy their AoI constraints while maximizing primal links’ throughput. Particularly, we orchestrate the access of D2D links in a distributed manner. Since distributed scheduling results in incomplete observation, D2D links share the spectrum with uncertainty on the transmission environment. Therefore, we propose a distributed scheduling scheme, called D-age, to deal with the transmission uncertainty in the AgeS problem, where an adaptation of actor-critic method is adopted with AoI constraints tackled in the dual domain. To address the non-stationary environment and multi-agent credit assignment issue, cooperative multi-agent reinforcement learning (MARL) approach is developed, where multiple local actors are designed to guide D2D links to make real-time decisions via distributed scheduling policies, which are evaluated by an edge-assisted global critic with action-aware advantage functions. Integrated with graph attention networks (GATs), the critic selectively learns contextual information by assigning different importances to neighboring links, which enables the evaluation of scheduling policies in a scalable and computation-efficient manner. Theoretical guarantee of the time-averaged AoI constraints is provided and the effectiveness of D-age in terms of both AoI violation ratio and the capacity of primal links is demonstrated by simulation.

Network function virtualization is a key enabling technology in future wireless networks for flexible and efficient sharing of network resources. Due to the increasing heterogeneity of network resource providers, a blockchain-based distributed architecture is a promising solution to enable reliable and transparent virtualized network function (VNF) management. However, since on-chain storage and computation are costive, it becomes a challenging task to achieve efficient VNF management with blockchain. In this paper, we first introduce a consortium blockchain for collaborative VNF management among network resource providers. Then, we propose an authenticated VNF dictionary that can be stored as a succinct authenticator on blockchain to support rich VNF query functionalities and efficient verifications of query results. Moreover, we design a dictionary pruning strategy to securely generate a compact authenticator for a given query, which reduces unnecessary memory accesses of the original dictionary when VNF queries are represented as arithmetic circuits. Finally, we conduct extensive experiments with a consortium blockchain network. The experimental results demonstrate that our pruning strategy is efficient for both on-chain and off-chain VNF management.

Securing necessary resources for edge computing processes via effective resource trading becomes a critical technique in supporting computation-intensive mobile applications. Conventional onsite spot trading could facilitate this paradigm with proper incentives, which, however, incurs excessive decision-making latency/energy consumption, and further leads to underutilization of dynamic resources. Motivated by this, a hybrid market unifying futures and spot is proposed to facilitate resource trading among an edge server (seller) and multiple smart devices (buyers) by encouraging some buyers to sign a forward contract with seller in advance, while leaving the remaining buyers to compete for available resources with spot trading. Specifically, overbooking is adopted to achieve substantial utilization and profit advantages owing to dynamic resource demands. By integrating overbooking into futures market, mutually beneficial and risk-tolerable forward contracts with appropriate overbooking rate can be achieved relying on analyzing historical statistics associated with future resource demand and communication quality, which are determined by an alternative optimization-based negotiation scheme. Besides, spot trading problem is studied via considering uniform/differential pricing rules, for which two bilateral negotiation schemes are proposed by addressing both non-convex optimization and knapsack problems. Experimental results demonstrate that the proposed mechanism achieves mutually beneficial player’s utilities, while outperforming baseline methods on critical indicators, e.g., decision-making latency, resource usage, etc.

As a promising solution for massive machine-type communication, grant-free non-orthogonal multiple access (GF-NOMA) has received considerable attention in recent years. However, the multidimensional constellation design (MCD) and multiuser detection (MUD) in GF-NOMA are usually optimized in adivide and conquerway, leading to local optima and performance degradation. To address this issue, we investigate the joint optimization of MCD and MUD for GF-NOMA. The formulated joint optimization is based on variational inference, which is intractable due to the signal superimposition that makes the optimization variables intricately coupled. Then, we resort to end-to-end deep learning (DL) to obtain the optimal solution. Specifically, we propose a DL-based multi-task variational autoencoder (Mul-VAE) that adopts a variational autoencoder network to optimize the distribution of the constellation points. We further derive the loss function of the proposed network and analyze it from an information-theoretic perspective. On this basis, multi-task learning is employed to deal with mutually conflicting yet related detection processes. Besides, taking heterogeneous transmission rates of users into account, a multi-task prioritizing strategy is designed to balance training performance. Simulation results reveal that the proposed method enables significant gains compared to state-of-the-art techniques.

With the aim of integrating over-the-air federated learning (AirFL) and non-orthogonal multiple access (NOMA) into an on-demand universal framework, this paper proposes a reconfigurable intelligent surface (RIS)-aided hybrid network by leveraging the RIS to flexibly adjust the decoding order of heterogeneous data. A new metric of computation rate is defined to measure the performance of AirFL users. Upon this, the objective of this work is to maximize the achievable hybrid rate by jointly optimizing the transmit power, controlling the receive scalar, and designing the reflection coefficients. Since the concurrent transmissions of all computation and communication signals are aided by the discrete phase-shifting elements at the RIS, the formulated problem (P0) is a challenging mixed-integer programming problem. To tackle this intractable issue, we decompose the original problem (P0) into a non-convex problem (P1) and a combinatorial problem (P2), which are characterized by the continuous and discrete variables, respectively. For the transceiver design problem (P1), the power allocation subproblem is first solved by difference-of-convex programming, and then the receive control subproblem is addressed by successive convex approximation, where the closed-form expressions of simplified cases are derived to obtain deep insights. For the reflection design problem (P2), a relaxation-then-quantization method is adopted to find a suboptimal solution for striking a trade-off between complexity and performance. Afterwards, an alternating optimization algorithm is developed to solve the non-linear non-convex problem (P0) iteratively. Finally, simulation results reveal that i) the proposed RIS-aided hybrid network can support on-demand communication and computation efficiently, ii) the system performance can be improved by properly selecting the location of the RIS, and iii) the designed algorithms are also applicable to conventional networks with only AirFL or NOMA users.

The space-air-ground integrated network (SAGIN) has drawn increasing attention for its benefits, such as wide coverage, high throughput for 5G and 6G communications. As one of the links, space-air communications between multiple unmanned aerial vehicles (UAVs) and Ka-band orbiting low earth orbit (LEO) satellites face a crucial challenge in tracking the 3D dynamic channel information. This paper exploits a statistical dynamic channel model called the multi-dimensional Markov model (MD-MM), which investigates the more realistic spatial and temporal correlation in the sparse UAVs-satellite channel. Specifically, the spatial and temporal probabilistic relationships of multi-user (MU) hidden support vector, single-user (SU) joint hidden support vector, and SU hidden value vector are investigated. The specific transition probabilities that connect the SU and MU hidden support vector for both azimuth and elevation directions are defined. Moreover, based on the proposed MD-MM, we derive a novel multi-dimensional dynamic turbo approximate message passing (MD-DTAMP) algorithm for tracking the 3D dynamic channel in multiple UAVs systems. Furthermore, we also develop a gradient update scheme to recursively find the azimuth and elevation offset for 3D off-grid estimation. Numerical results verify that the proposed algorithm shows superior 3D channel tracking performance with smaller pilot overhead and comparable complexity.

The emerging uplink (UL) and downlink (DL) decoupled radio access networks (RAN) has attracted a lot of attention due to the significant gains in network throughput, load balancing and energy consumption, etc. However, due to the diverse vehicular service requirements in different vehicle-to-everything (V2X) applications, how to provide customized cellular V2X services with diversified requirements in the UL/DL decoupled 5G and beyond cellular V2X networks is challenging. To this end, we investigate the feasibility of UL/DL decoupled RAN framework for cellular V2X communications, including the vehicle-to-infrastructure (V2I) communications and relay-assisted cellular vehicle-to-vehicle (RAC-V2V) communications. We propose a two-tier UL/DL decoupled RAN slicing approach. On the first tier, the deep reinforcement learning (DRL) soft actor-critic (SAC) algorithm is leveraged to allocate bandwidth to different base stations. On the second tier, we model the QoS metric of RAC-V2V communications as an absolute-value optimization problem and solve it by the alternative slicing ratio search (ASRS) algorithm with global convergence. The extensive numerical simulations demonstrate that the UL/DL decoupled access can significantly promote load balancing and reduce C-V2X transmit power. Meanwhile, the simulation results show that the proposed solution can significantly improve the network throughput while ensuring the different QoS requirements of cellular V2X.

Small cell network (SCN) is a promising solution to meet the demand for increasing data traffic for the sixth generation and beyond wireless networks. However, the power consumption and two-tier interference issues are two bottlenecks that hinder its further development. In this paper, we propose a novel intelligent reflecting communication (IRC) system in which a reconfigurable intelligent surface (RIS) is used to serve multiple micro users in an SCN while assisting the transmission from a macro base station (MBS) to a macro user. Compared to the conventional SCN, the RIS can achieve significant power reduction as it transmits the information by passively reflecting the incident signals. In addition, the reflected signal can be regarded as a multipath component instead of an interference to the macro user. We are interested in minimizing the total power consumption by jointly designing the phase shift matrix at the RIS and the beamforming vector at the MBS under the user rate constraints and the practical phase shift constraints. The solution is obtained by alternating optimization to iteratively solve two subproblems, one to optimize the phase shift matrix, and the other to optimize the beamforming vector. A TDMA transmission scheme is also proposed as an alternative to serve multiple users. Simulation results demonstrate that the total power consumption can be reduced significantly by deploying the RIS in the SCN when the number of reflecting elements is sufficiently large.

In WSNs, packets are delivered through mostly static shortest paths to their destination. However, static packet delivery makes WSNs highly vulnerable to traffic analysis attacks due to open area deployment. Existing defence proposals fail to achieve a balance between the protection level and the resource constraints. In this paper, we present a proactive SDN-based Route Mutation (SRRM) scheme that enables changing the routes of the multiple flows in WSNs simultaneously to defend against passive and stealthy reconnaissance and sniffer attacks while preserving reliable and energy-aware routing. Multiple routes are ranked for packet flow based on node reliability, energy consumption, link cost, and route overlapping. Our extensive simulation results show that these techniques can effectively provide route obfuscation for software-defined WSNs.

To support the explosive content demands from multifarious services and applications, cache-enabled satellite-terrestrial integrated networks (STINs) are envisioned as a key enabler to reduce the content delivery delay and alleviate the backhaul pressure. In this paper, we investigate the joint optimization of cache placement and content delivery in the STIN to minimize the long-term overall content delivery delay. Considering that cache placement and content delivery are interrelated and affected by network dynamics in terms of satellite movement and random content requests, the joint optimization problem is formulated as a sequential decision making problem by leveraging a Markov decision process. We propose a hierarchical deep Q learning (HDQL) algorithm by leveraging two independent deep neural networks to learn the cache placement and content delivery policies with small action space and low time complexity. Simulation results demonstrate that the proposed HDQL algorithm outperforms the benchmark algorithms in terms of content delivery delay in the STINs.

To realize secure communication and prevent eaves-droppers from detecting the existence of communication activities, covert communication has attracted substantial research interests. In this paper, we propose a dynamic spectrum control (DSC)-assisted scheme to achieve covert and reliable data transmission. Specifically, by constructing time-frequency division channels, the proposed DSC-assisted scheme generates sequences with iterative and orthogonal transformations. Authorized users can orderly occupy different frequency slots in each time slot under the guidance of these sequences, thus achieving simultaneous data transmission without interfering with each other. Then, the covert performance of the proposed transmission scheme is analyzed to provide the closed-form expressions of covert transmission rate and the reliable transmission probability. Simulation results are provided to validate the accuracy of the theoretical analysis and demonstrate that the proposed scheme can achieve better covert and reliable transmission performances when compared with the existing scheme.

Unmanned aerial vehicle (UAV)-assisted wireless powered sensor networks (WPSNs) have emerged as a promising paradigm for charging sensor nodes' batteries in remote areas. However, the sum-throughput of overall sensor nodes can dramatically decrease due to their long-distance transmission to the UAV. In this paper, we propose a joint distributed beamforming and backscatter cooperation (BC) scheme to enhance the sum-throughput of UAV-assisted WPSNs with various types of sensor nodes. In particular, we consider the BC mechanism which leverages other types sensor nodes with constructive multi-path signals to enhance the long-distance transmission of same-type sensor nodes. We maximize the sum-throughput by jointly optimizing the distributed backscattering, distributed beamforming and time allocation. The sum-throughput maximization problem is difficult to be solved directly due to the coupling among optimizing variables. We decompose the problem into a BC subproblem and a time allocation subproblem, and propose a two-step scheme to solve them. First, for the BC subproblem, we derive closed-form low-complexity distributed beamforming solutions and distributed backscattering solutions to maximize the signal-to-noise ratios of the same-type sensor nodes. Second, for the time allocation subproblem, we derive the closed-form solutions according to KKT conditions. Simulation results are provided to demonstrate that the proposed joint distributed beamforming and BC scheme can increase the sum-throughput as compared to conventional distributed beamforming schemes.

Mobile edge computing (MEC) has been widely considered as an effective technology to handle computationally intensive tasks generated by mobile devices. However, the computation resources at an edge node is usually several orders of magnitude smaller than that of a cloud. Thus, it is rather vital to take an investigation into the collaboration between the cloud and the edge. In this paper, to fully exploit the computation power of the cloud server and achieve energy efficient task offloading, we propose an LEO-assisted terrestrial-satellite network (TSN) architecture for cloud-edge collaborative computation offloading. We formulate the collaborative cloud-edge computing problem that minimizes the energy consumption of the whole TSN under the quality-of-service (QoS) constraints. The optimization problem is further decomposed into two subproblems which are solved by deep neural networks (DNN) and successive convex approximation (SCA) algorithm, respectively. Simulation results show the effectiveness of our proposed cloud-edge collaborative computation offloading architecture on achieving a lower energy cost.

The significant advances in wireless communication together with edge intelligent (EI) technology have facilitated the decentralized edge computing paradigm for data-intensive and delay-sensitive solution on massive Internet of Things (IoT) devices. In this paper, a Cybertwin assisted asynchronous federated learning (AFL) mechanism is proposed for realizing efficient edge computing by taking full advantage of local computation capability under heterogeneous wireless environment. First, Cybertwin is introduced as intermediary communication assistant to coordinate individual model aggregation between the users and the cloud server under AFL training process. Second, for the sake of flexible and effective utilization of communication-computation resources for edge computing, Cybertwin plays the role of intelligent agent to jointly take the local computing and up-link transmission into consideration. A resource optimization problem considering the diversified computing power, varied data size, and available communication bandwidth is formulated and we leverage the block coordinate descent (BCD) method to obtain optimal resource management solution. Extensive simulations are conducted to demonstrate the effectiveness of our proposed Cybertwin assisted AFL mechanism, which can shed further light on the application of data-intensive edge computing paradigm over wireless communication network.

Space-ground integrated vehicular networks (SGIVNs) are envisioned as a promising architecture to support multifarious vehicular services with enhanced network flexibility and reliability. Access mode selection (AMS) is of capital importance in the SGIVN for the ingenious cooperation among different network segments to exploit their complementary advantages. In this paper, we investigate the AMS problem for vehicles in the SGIVN by taking distinct features of satellite networks (long propagation delay) and terrestrial networks (frequent handover) into account. In light of the high vehicle/satellite mobility and dynamic data packet arrivals, we formulate a stochastic integer programming problem of sequential AMS to maximize vehicles' long-term data rate. To cope with the time-varying network dynamics, we leverage a Markov decision process framework to model the evolution of vehicle states. For the special case with known stochastic model of data packet arrivals, we transform the problem into a linear programming problem that can be solved with low complexity. For the general case without the data packet arrival model, we propose a reinforcement learning-based algorithm to make adaptive AMS decisions to keep pace with network dynamics. Simulation results demonstrate that the proposed algorithm outperforms benchmark algorithms in terms of data rate under different data packet arrival patterns and vehicle velocities.

In this paper, we investigate a computing task offloading problem in a cloud-based autonomous vehicular network (C-AVN), from the perspective of long-term network wide computation load balancing. To capture the task computation load dynamics over time, we describe the problem as an Markov decision process (MDP) with constraints. Specifically, the objective is to minimize the expectation of a long-term total cost for imbalanced base station (BS) computation load and task offloading decision switching, with per-slot computation capacity and offloading latency constraints. To deal with the unknown state transition probability and large state-action spaces, a multi-agent deep Q-learning (MA-DQL) module is designed, in which all the agents cooperatively learn a joint optimal task offloading policy by training individual deep Q-network (DQN) parameters based on local observations. To stabilize the learning performance, a fingerprint-based method is adopted to describe the observation of each agent by including an abstraction of every other agent’s updated state and policy. Simulation results show the effectiveness of the proposed task offloading framework in achieving long-term computation load balancing with controlled offloading switching times and per-slot QoS guarantee.

The rechargeable battery of a plug-in electric vehicle (PEV) endows the PEV with dual roles in the power grid as power load and mobile energy storage (MES). Owing to the technical advancement of autonomous driving, private PEVs that are parked most of the day can be used as private MESs (PMESs) to autonomously deliver energy for overloaded charging stations (CSs). In this paper, we investigate an energy compensation problem where PMESs are scheduled to deliver energy to overloaded CSs so that the energy balance can be achieved while the energy delivery time can be minimized. Based on the time-variant CS operation status and traffic conditions, we propose a pricing-based scheduling scheme that considers both PMES navigation and incentive price design. First, to navigate PMESs in the energy-capacitated transportation system, a minimum-cost flow problem is formulated to minimize the energy delivery time. Then, the incentive price is determined to encourage PMESs to follow the optimal navigation results for energy delivery. Simulations are conducted based on the traffic data of California highway to validate the effectiveness of the proposed scheduling scheme.

To enhance driving safety and road intelligence for connected vehicles, the transmission of safety messages is critical in vehicular networks. In this paper, we focus on urban vehicular networks with deployed roadside units, and both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) connections can be leveraged for message transmissions. We consider three types of safety messages: periodic messages for vehicular status notification, event-driven messages for urgent situation notification, and messages to achieve collective perception. To support different safety-related services, we develop a multi-dimensional resource allocation scheme to jointly optimize the sensing resource allocation (i.e., selecting vehicles as perception data providers), the V2I/V2V transmission mode selection, and the corresponding communication resource allocation. As the decisions on sensing resource allocation and wireless resource allocation are coupled, an iterative algorithm is proposed to solve the joint optimization problem by taking the differentiated service priorities into consideration. Extensive simulation results are presented to validate the effectiveness of the proposed resource allocation scheme.

In this paper, we investigate an air-ground coordination communication system where ground users (GUs) access suitable UAV base stations (UAV-BSs) to maximize their own throughput and UAV-BSs design their trajectories to maximize the total throughput and keep GU fairness. Note that the action space of GUs is discrete, and UAV-BSs’ action space is continuous. To deal with the hybrid action space, we propose a multi-agent deep reinforcement learning (MADRL) approach, named AG-PMADDPG (air-ground probabilistic multi-agent deep deterministic policy gradient), where GUs transform the discrete actions to continuous action probabilities, and then sample actions according to the probabilities. The proposed method enable the users make decisions based on their local information, which is beneficial for user privacy. Simulation results demonstrate that AG-PMADDPG can outperform the benchmark algorithms in terms of fairness and throughput.

In this paper, we investigate the joint cache placement and content delivery in satellite-terrestrial integrated cloud radio access networks. We consider the scenario where cache-enabled access points (APs), including multiple base stations (BSs) and one low orbit earth satellite, are connected to a central processor via backhaul links and cooperatively serve users via joint beamforming. To minimize the long-term power consumption, we formulate an optimization problem to jointly optimize the cache placement, AP clustering, and multicast beamforming, while satisfying the constraints on the transmission power, quality-of-service, and caching storage. Since the formulated problem has mixed timescales, it is decoupled into two sub-problems. For the short-term delivery sub-problem, we first reformulate it as an equivalent sparse multicast beamforming problem and approximate the non-convex objective function by a concave smooth function, and then solve it with a convex-concave procedure approach. For the long-term cache placement sub-problem, we propose an alternating based method to tackle it iteratively. Simulation results validate the advantages of our proposed method and show the impacts of different cache capacities and file numbers on the long-term power consumption.

As a promising architecture to provide ubiquitous and ultra-reliable network connectivity, space-air-ground integrated network (SAGIN) has attracted substantial research interests. In this paper, we propose a dynamic spectrum access control (DSAC) protocol for the SAGIN. Specifically, the proposed DSAC protocol uses sequences to represent the spectrum access decisions for authorized users at different time slots. Through iterative and orthogonal sequence transformation, the DSAC protocol can generate orthogonalized sequences to guarantee successful spectrum access for authorized users. In addition, the non-collision probability of the data packets accessing the shared spectrum under the guidance of DSAC protocol is analyzed, based on which a closed-form expression of the system throughput is further derived. Simulation results are provided to validate the accuracy of the theoretical analysis and demonstrate that the proposed protocol is effective in access control and throughput improvement in the SAGIN when compared with the existing random access protocol.

Real-time sensing data access is essential for vehicular networks to support safe, efficient, and intelligent road services. Considering the tremendous data volume, the sensing big data search process should be carefully devised to avoid excessive retrieval delay. Edge caching can effectively alleviate the traffic burden and shorten the data downloading route, where the sensing data has to be uploaded to the edge in advance. Given a short life-time of sensing data, the caching scheme is required to be efficient in facilitating both the search process and uplink/downlink transmission, which is challenging due to the coupling of resource allocation decisions. In this paper, an edge-cloud cooperative caching scheme is proposed. Specifically, to enable real-time data search, we first introduce a hierarchical indexing framework for cached data, based on which we then devise a search utility model to quantify the expected data freshness and response delay. Aiming at maximizing the search utility, a Caching-assisted Real-time Search (CRS) problem is formulated. Due to its NP-hardness, we devise a greedy-based algorithm to solve the CRS problem. Simulation results demonstrate that the proposed cooperative caching scheme can significantly improve the data freshness and cache hit ratio comparing to the benchmark schemes.

This paper aims to investigate the joint optimization of multidimensional constellation design (MCD) and multiuser detection (MUD) for grant-free non-orthogonal multiple access (GF-NOMA). We first formulate the joint optimization problem and derive its explicit expression using variational inference. Due to the intractability of the joint optimization problem, we then resort to deep learning (DL) and approximate the optimal solution in an end-to-end manner. Specifically, we develop a novel variational autoencoder based network, such that the distribution of the multidimensional constellations can be accessed and optimized. We also design a multi-task learning architecture on the decoder side to deal with the complex coupling among signal streams, by taking the MUD process as multiple distinctive yet related tasks. The derivation of the loss function for network training is presented, and simulation results are provided to validate the superior performance of the proposed method over conventional approaches.

To support multifarious vehicular services with differentiated quality-of-service (QoS) requirements, space-air-ground integrated vehicular networks (SAGVNs) are envisioned as a promising solution to provide global network connectivity, enhance network flexibility, and improve network reliability. In this paper, we investigate cooperative content delivery in the SAGVN, where vehicular content requests can be simultaneously served by multiple access points (APs) in space, aerial, and terrestrial networks. In specific, a joint optimization problem of vehicle-to-AP association, bandwidth allocation, and content delivery ratio, referred to as the ABC problem, is formulated to minimize the overall content delivery delay while satisfying vehicular QoS requirements. To address the tightly-coupled optimization variables, we propose a load- and mobility-aware ABC (LMA-ABC) scheme to solve the joint optimization problem as follows. We first decompose the ABC problem to optimize the content delivery ratio. Then the impact of bandwidth allocation on the achievable delay performance is analyzed, and an effect of diminishing delay performance gain is revealed. Based on the analysis results, the LMA-ABC scheme is designed with the consideration of user fairness, load balancing, and vehicle mobility. Simulation results demonstrate that the proposed LMA-ABC scheme can significantly reduce the cooperative content delivery delay comparing to the benchmark schemes.

In this paper, we investigate actual cloud-assisted electronic health (eHealth) systems in terms of security, efficiency, and functionality. Specifically, we propose a password-based subsequent-key-locked encryption mechanism to ensure the confidentiality of outsourced electronic health records (EHRs). We also propose a blockchain-based secure EHR provenance mechanism by designing the data structure of the EHR provenance record and deploying a public blockchain and smart contract to secure both EHRs and their provenance records. With the two mechanisms, we develop BESURE (blockchain-based cloud-assisted eHealth system with secure data provenance) to provide a secure EHR storage service with efficient provenance. Security analysis and comprehensive performance evaluation are conducted to demonstrate that BESURE is secure and efficient.

Abstract The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

In this article, we investigate the worker (i.e., vehicle) selection problem in vehicle-based crowdsourcing (VBC), where vehicles in a specific area are recruited by the crowdsourcing platform to collect geographical information in real driving scenarios for autonomous driving. Given a limited recruitment budget, we formulate a cumulative platform utility maximization problem (CMP) to obtain the optimal worker set. The CMP is unsolvable directly as the platform has no prior information of workers at the initial stage (also known as “cold start”) and the cost of collecting all workers' information is prohibitive. To solve the problem, we first conduct a comprehensive data analytics on two real-world vehicle traces and obtain two crucial observations: 1) trajectory of individual vehicle is highly uncertain that it is difficult to make accurate prediction and 2) the overall distribution of vehicular trajectory penetration (measured by collection quantity and coverage) has a diurnal pattern and varies with weekly periodicity. Inspired by the insights, we propose the performance transfer-based online worker selection (POSE) scheme, which works independently from trajectory prediction with two components, i.e., transfer learning-based performance estimation and online worker selection (OWS). Based on the diurnal pattern, the former component collects a short-period trajectory penetration data of vehicles for model fitting, which can output a specific numerical distribution. With the fitting model, we can identify and select vehicles with high trajectory penetration at the initial stage to cope with the “cold start” problem. Then, we map the worker selection problem into a multiarmed bandit problem and develop upper confidence bound-based approach to solve it. Extensive trace-driven simulations are carried out and the results demonstrate the efficiency of POSE in terms of cumulative platform utility.

In the second part of this article, we develop a centralized packet transmission scheduling scheme to pair with the protocol designed in Part I and complete our medium access control (MAC) design for machine-type communications in the industrial Internet of Things. For the networking scenario, fine-grained scheduling that attends to each device becomes necessary, given stringent Quality-of-Service (QoS) requirements and diversified service types, but prohibitively complex for a large number of devices. To address this challenge, we propose a scheduling solution in two steps. First, we develop algorithms for device assignment based on the analytical results from Part I, when parameters of the proposed protocol are given. Then, we train a deep neural network for assisting in the determination of the protocol parameters. The two-step approach ensures the accuracy and granularity necessary for satisfying the QoS requirements and avoids excessive complexity from handling a large number of devices. Integrating the distributed coordination in the protocol design from Part I and the centralized scheduling from this part, the proposed MAC protocol achieves high performance, demonstrated through extensive simulations. For example, the results show that the proposed MAC can support 1000 devices under an aggregated traffic load of 3000 packets per second with a single channel and achieve <; 0.5 ms average delay and <; 1% average collision probability among 50 high priority devices.

In this two-part paper, we propose a novel medium access control (MAC) protocol for machine-type communications in the Industrial Internet of Things. The considered use case features a limited geographical area and a massive number of devices with sporadic data traffic and different priority types. We target supporting the devices while satisfying their Quality-of-Service (QoS) requirements with a single access point and a single channel, which necessitates a customized design that can significantly improve the MAC performance. In Part I of this paper, we present the MAC protocol that comprises a new slot structure, corresponding channel access procedure, and mechanisms for supporting high device density and providing differentiated QoS. A key idea behind this protocol is sensing-based distributed coordination for significantly improving channel utilization. To characterize the proposed protocol, we analyze its delay performance based on the packet arrival rates of devices. The analytical results provide insights and lay the groundwork for the fine-grained scheduling with QoS guarantee as presented in Part II.

Considering the high-density and high-dynamic feature of cooperative unmanned aerial vehicles (UAVs) swarm, also referred to as flocking of flying ad hoc networks (FANETs), reliable medium access control (MAC) protocol design for network connectivity maintaining and network information sharing is a challenging issue. In this article, we propose a self-adaptive carrier sense multiple access with collision avoidance (CSMA/CA)-based MAC protocol for flocking of FANET, namely, FMAC, to provide reliable broadcast information service under density-varying flocking scenarios. To represent the varying trend of UAV density during flocking, we define the collective neighboring potential (CNP) in the FMAC protocol. Specifically, at the beginning of each period, each UAV computes the current CNP based on available neighbors' motion states. Then, the value of CNP at the start of the next period regarding the same neighbors is predicted using UAV's kinetic equation. After that, each UAV can update the contention window (CW) size by comparing the current CNP and the predicted CNP, and CW will be decreased (increased) if the current CNP is larger (smaller) than the predicted one for enough period. The simulation results show that the proposed FMAC protocol can ensure high successful transmission probability under density-varying flocking scenarios and outperforms the typical MAC solutions.

In advanced metering infrastructure, smart meters (SMs) send fine-grained power consumption readings periodically to the utility for load monitoring and energy management. Change and transmit (CAT) is an efficient approach to collect these readings, where the readings are not transmitted when there is no enough change in consumption. However, this approach causes a privacy problem, that is, by analyzing the transmission pattern of an SM, sensitive information on the house dwellers can be inferred. For instance, since the transmission pattern is distinguishable when dwellers are on travel, attackers may analyze the pattern to launch a presence-privacy attack (PPA) to infer whether the dwellers are absent from home. In this article, we propose a scheme, called “STDL,” for efficient collection of power consumption readings in advanced metering infrastructure (AMI) networks while preserving the consumers’ privacy by sending spoofing transmissions using a deep-learning approach. We first use a clustering technique and real power consumption readings to create a data set for transmission patterns using the CAT approach. Then, we train a deep-learning-based attacker model, and our evaluations indicate that the attacker’s success rate is about 91%. Finally, we train a deep-learning-based defense model to send spoofing transmissions efficiently to thwart the PPA. Extensive evaluations are conducted, and the results indicate that our scheme can reduce the attacker’s success rate to 3.15%, while still achieving high efficiency in terms of the number of readings that should be transmitted. Our measurements indicate that the proposed scheme can increase efficiency by about 41% compared to continuously transmitting readings.

Accurate network synchronization is crucial to orchestrate distributed infrastructures in Industrial Internet of Things (IIoT) systems for accomplishing network-wide tight temporal collaboration. Traditional clock synchronization can be achieved with extensive exchanges of explicit timestamps for estimating clock offsets, which becomes impractical due to high overhead with the expansion of the network scale. The performance of conventional synchronization will also be dramatically deteriorated due to various uncertainties of IIoT networks. In this article, we propose a passive network synchronization scheme based on concurrent passive observations to calibrate the distributed clocks in IIoT systems while significantly reducing the explicit interactions and network resource consumption during synchronization. By processing the physical phenomena observed concurrently by a group of selected IIoT devices, the local clock offsets of the passive observing devices can be efficiently estimated according to the common time reference linked to the event observed. Multiple relay nodes are further coordinated by the cloud center to disseminate the reference time information throughout the IIoT system. Simulation results demonstrate that by utilizing a series of concurrent observations with efficient coordination, the proposed scheme can achieve accurate and reliable network synchronization for large-scale IIoT systems with significantly reduced network overhead.

Tight cooperation among distributively connected equipment and infrastructures of an Industrial-Internet-of-Things (IIoT) system hinges on low latency data exchange and accurate time synchronization within sophisticated networks. However, the temperature-induced clock drift in connected industry facilities constitutes a fundamental challenge for conventional synchronization techniques due to dynamic industrial environments. Furthermore, the variation of packet delivery latency in IIoT networks hinders the reliability of time information exchange, leading to deteriorated clock synchronization performance in terms of synchronization accuracy and network resource consumption. In this article, a digital-twin-enabled model-based scheme is proposed to achieve an intelligent clock synchronization for reducing resource consumption associated with distributed synchronization in fast-changing IIoT environments. By leveraging the digital-twin-enabled clock models at remote locations, required interactions among distributed IIoT facilities to achieve synchronization is dramatically reduced. The virtual clock modeling in advance of the clock calibrations helps to characterize each clock so that its behavior under dynamic operating environments is predictable, which is beneficial to avoiding excessive synchronization-related timestamp exchange. An edge-cloud collaborative architecture is also developed to enhance the overall system efficiency during the development of remote digital-twin models. Simulation results demonstrate that the proposed scheme can create an accurate virtual model remotely for each local clock according to the information gathered. Meanwhile, a significant enhancement on the clock accuracy is accomplished with dramatically reduced communication resource consumption in networks with different packet delay variations.

Through network function virtualization (NFV), virtual network functions (VNFs) can be mapped onto substrate networks as service function chains (SFCs) to provide customized services with guaranteed Quality of Service (QoS). In this article, we solve a multi-SFC embedding problem by a game-theoretical approach considering the heterogeneity of NFV nodes, the effect of processing-resource sharing among various VNFs, and the capacity constraints of NFV nodes. Specifically, each SFC is treated as a player whose objective is to minimize the overall latency experienced by the supported service flow, while satisfying the capacity constraints of all NFV nodes. Due to processing-resource sharing, additional delay is incurred and incorporated into the overall latency for each SFC. The capacity constraints of NFV nodes are considered by adding a penalty term into the cost function of each player, and are guaranteed by a prioritized admission control mechanism. We prove that the formulated resource-constrained multi-SFC embedding game (RC-MSEG) is an exact potential game admitting at least one pure Nash equilibrium (NE) and has the finite improvement property (FIP). Two iterative algorithms are developed, namely, the best response (BR) algorithm with fast convergence and the spatial adaptive play (SAP) algorithm with great potential to obtain the best NE. Simulations are conducted to demonstrate the effectiveness of the proposed game-theoretical approach.

To efficiently allocate heterogeneous resources for customized services, in this article, we propose a network virtualization (NV)-based network architecture in cybertwin-enabled 6G core networks. In particular, we investigate how to optimize the virtual network (VN) topology (which consists of several virtual nodes and a set of intermediate virtual links) and determine the resultant VN embedding in a joint way over a cybertwin-enabled substrate network. To this end, we formulate an optimization problem whose objective is to minimize the embedding cost, while ensuring that the end-to-end (E2E) packet delay requirements are satisfied. The queueing network theory is utilized to evaluate each service’s E2E packet delay, which is a function of the resources assigned to the virtual nodes and virtual links for the embedded VN. We reveal that the problem under consideration is formally a mixed-integer nonlinear program (MINLP) and propose an improved brute-force search algorithm to find its optimal solutions. To enhance the algorithm’s scalability and reduce the computational complexity, we further propose an adaptively weighted heuristic algorithm to obtain near-optimal solutions to the problem for large-scale networks. Simulations are conducted to show that the proposed algorithms can effectively improve network performance compared to other benchmark algorithms.

Due to the increasing healthcare costs and the advance of wireless technology, health monitoring systems have been widely adopted recently. In health monitoring systems, a hospital outsources a clinical decision model to a cloud service provider, which receives biomedical data from remote clients and produces clinical decisions based on the outsourced model. Due to critical privacy concerns, both the clinical decision model and biomedical data should be protected. In this article, we propose an efficient and privacy-preserving decision tree (PPDT) classification scheme for health monitoring systems. Specifically, we first transform a decision tree classifier (i.e., the clinical decision model) into the Boolean vectors. Then, we leverage symmetric key encryption to encrypt the Boolean vectors as encrypted indices. The PPDT classification is achieved by searching the encrypted indices with encrypted tokens. We formulate a leakage function and provide the security definition and simulation-based proof for PPDT. The performance analyses demonstrate that PPDT is very efficient in terms of computation, communication, and storage. Experimental evaluations show that PPDT only requires microsecond-level execution time, kilobyte-level communication costs, and kilobyte-level storage costs on the test data set.

In cloud-based health monitoring services, support vector machine (SVM) classification techniques are often utilized by medical institutes to build medical decision models, which can be outsourced to a cloud server for producing medical decisions based on medical features from remote clients. In this article, we propose a verifiable and secure SVM classification scheme ($\mathsf {VSSVMC}$) for cloud-based health monitoring services in a malicious setting, where the cloud server may return invalid decisions. By constructing verifiable indices,$\mathsf {VSSVMC}$ensures the verifiability of medical decisions, which enables clients to detect whether the cloud server returns incorrect or incomplete medical decisions. Symmetric key encryption is leveraged to ensure the confidentiality of the medical decision model and medical data with computational efficiency. We give security and verifiability definitions and provide formal security and verifiability proofs for$\mathsf {VSSVMC}$. Performance analyses show that$\mathsf {VSSVMC}$is extremely efficient in terms of computation, communication, and storage. Experimental evaluations demonstrate that$\mathsf {VSSVMC}$achieves microsecond-level execution time with kilobyte-level communication and storage overheads on the tested data set.

State-of-the-art mechanisms against eavesdropping first encrypt all packet payloads in the application layer and then split the packets into multiple network paths. However, versatile eavesdroppers could simultaneously intercept several paths to intercept all the packets, classify the packets into streams using transport fields, and analyze the streams by brute-force. In this article, we propose a programming protocol-independent packet processors (P4)-based network immune scheme (P4NIS) against the intractable eavesdropping. Specifically, P4NIS is equipped with three lines of defenses to provide a softwarized network immunity. Packets are successively processed by the third, second, and first line of defenses. The third line basically encrypts all packet payloads in the application layer using cryptographic mechanisms. Additionally, the second line re-encrypts all packet headers in the transport layer to distribute the packets from one stream into different streams, and disturbs eavesdroppers to classify the packets correctly. Besides, the second line adopts a programmable design for dynamically changing encryption algorithms. Complementally, the first line uses programmable forwarding policies which could split all the double-encrypted packets into different network paths disorderly. Using a paradigm of programmable data planes-P4, we implement P4NIS and evaluate its performances. Experimental results show that P4NIS can increase difficulties of eavesdropping and transmission throughput effectively compared with state-of-the-art mechanisms. Moreover, if P4NIS and state-of-the-art mechanisms have the same level of defending eavesdropping, P4NIS can decrease the encryption cost by 69.85%-81.24%.

In smart ocean, unmanned surface vehicles (USVs) are deployed to monitor the marine environment in a coordinated manner. The ubiquitous situation awareness of marine environment can be achieved by state estimation with the sensory data collected by USVs. Therefore, the transmission performance in terms of packet loss and delay of sensory data plays an important role in the state estimation of marine IoT systems. However, it is challenging to achieve the high-reliable and low-latency transmission for sensory data due to the path loss, spectrum scarcity and transmit power limitation. In this article, we introduce the Age of Information (AoI) to mathematically characterize the impacts of packet loss and transmission delay on the state estimation error. We first explore the relationship between the state estimation error and the AoI of sensory data. We then investigate the co-design of state estimation and sensory data transmission for marine IoT systems. Specifically, a mother ship (MS)-assisted cooperative transmission scheme is proposed to mitigate the impact of limited resources and path loss on the estimation performance. Then, the MS location, channel allocation, and transmit power are jointly optimized to minimize the mean-square error of state estimation, which is achieved by formulating a constrained minimization problem and solving it with the decomposition method. Simulation results demonstrate that the proposed scheme has superiorities in reducing the estimation error and the power consumption.

Drone cell (DC) is envisioned to enable the dynamic service provisioning for radio access networks (RANs), in response to the spatial and temporal unevenness of user traffic. In this article, we propose a hierarchical deep reinforcement learning (DRL)-based multi-DC trajectory planning and resource allocation (HDRLTPRA) scheme for high-mobility users. The objective is to maximize the accumulative network throughput while satisfying user fairness, DC power consumption, and DC-to-ground link quality constraints. To address the high uncertainties of the environment, we decouple the multi-DC TPRA problem into two hierarchical subproblems, i.e., the higher level global trajectory planning (GTP) subproblem and the lower level local TPRA (LTPRA) subproblem. First, the GTP subproblem is to address trajectory planning for multiple DCs in the RAN over a long time period. To solve the subproblem, we propose a multiagent DRL-based GTP (MARL-GTP) algorithm in which the nonstationary state space caused by the multi-DC environment is addressed by the multiagent fingerprint technique. Second, based on the GTP results, each DC solves the LTPRA subproblem independently to control the movement and transmit power allocation based on the real-time user traffic variations. A deep deterministic policy gradient (DEP)-based LTPRA (DEP-LTPRA) algorithm is then proposed to solve the LTPRA subproblem. With the two algorithms addressing both subproblems at different decision granularities, the multi-DC TPRA problem can be resolved by the HDRLTPRA scheme. Simulation results show that 40% network throughput improvement can be achieved by the proposed HDRLTPRA scheme over the nonlearning-based TPRA scheme.

Recent advancements of Vehicle-to-Everything (V2X) communication combined with artificial intelligence (AI) technologies have shown enormous potentials for improving traffic management efficiency and intelligence. To provide innovative and effective data-driven traffic management solution for the coming automated vehicle era, we present a vehicle-road collaboration-enabled nonsignalized intersection management architecture in this paper. First, by dividing the intersection zone into the central section (CS) and the waiting section (WS), a vehicle regulation scheme involved with communication and computation planes is developed for V2X-enabled nonsignalized intersection management. Specifically, in order to guarantee vehicle safety, the definition of no overlapping occupation time in CS and the fastest crossing time point (FCTP) algorithm are employed for vehicle collision avoidance. Second, considering the relative coordination between adjacent intersections, a multiagent-based deep reinforcement learning scheduling (MA-DRLS) algorithm is proposed to realize cooperative multiple intersection management. Through information exchange with different intersection agents, each agent can obtain an optimal scheduling strategy using independent deep reinforcement learning (DRL) network. The features of fixed Q-targets and experience replay are leveraged to improve the reliability of neural network during the training process. Finally, simulation performances in terms of intersection throughput and vehicle waiting time have been provided to validate the effectiveness and demonstrate the superiority of the proposed nonsignalized intersection management solution.

It is well known that concurrent multipath transfer (CMT) can improve the transmission rate. However, due to multiple heterogeneous paths from users to the access network, a large number of out-of-order packets significantly degrade the overall transmission reliability. Cybertwin provides a potential solution to alleviate the packet out-of-order problem by accurately detecting and perceiving the path state. In this article, we investigate the data scheduling problem and propose a learning-based cybertwin-driven CMT algorithm to obtain the optimal data scheduling policy. In particular, we first formulate the data scheduling problem as an integer linear programming by taking the QoS metrics into account. To cope with the packet out-of-order problem in CMT, we propose a reliable cybertwin-CMT with deep reinforcement learning (CMT-DRL) algorithm to determine the data scheduling decisions. The proposed algorithm takes multipath throughput, end-to-end delay, and packet loss rate into account. Besides, CMT-DRL adopts an asynchronous learning framework to efficiently execute data collection, packet scheduling, and neural network training in sequence by decoupling model training and execution. We conduct extensive experiments in a P4-based programmable network platform. Experimental results indicate that the CMT-DRL outperforms the existing benchmarks in terms of the number of out-of-order packets, round-trip time, and throughput.

Accurate location estimation of Internet-of-Things (IoT) devices within an Area of Interest (AoI) is a challenging issue, especially in a global navigation satellite system (GNSS)-constrained environment. In this article, we present a space-air integrated localization network (SAILN) architecture to exploit the advantages of the unmanned-aerial-vehicle (UAV)-based localization through joint position and power optimization (JPPO) strategies. In SAILN, UAVs can utilize their flexible movement to obtain the line-of-sight (LOS) path with a high probability, thereby providing the potential IoT devices in the AoI with supplementary localization information. The JPPO of UAVs aims to improve the regional localization accuracy for the entire AoI, considering the no-fly-zone (NFZ) and the total energy constraint. We propose the average localization accuracy increment (ALAI) of the sampling points in the AoI as the metric to measure the performance of SAILN compared with that of only satellites, which is regarded as the objective to formulate the JPPO problems for UAV operations in both static and dynamic SAILN. The intractable problems can be resolved by the pure genetic algorithm (PGA) that has a low computational cost and unique features suiting the JPPO of UAVs. Then, by taking advantage of the ALAI convexity to the UAVs' power, we propose a power reallocation-based two-step algorithm (PRTSA) to further explore an improved JPPO solution. Simulation results validate that the proposed PRTSA can obtain a higher localization accuracy for the entire AoI than the PGA and the other straightforward baselines.

Task offloading decision making plays a key role in enabling mobile-edge computing (MEC) technologies in Internet of Things (IoT). However, it meets the significant challenges arising from the stochastic dynamics of task queueing in the application layer and coupled wireless interference in the physical layer in a distributed multiagent network without any centralized communication and computing coordination. In this article, we investigate the distributed task offloading optimization problem with consideration of the upper layer queueing dynamics and the lower-layer coupled wireless interference. We first propose a new optimization model that aims at maximizing the expected offloading rate of multiple agents by optimizing their offloading thresholds. Then, we transform the problem into a game-theoretic formulation, which further leads to the design of a distributed best-response (DBR) iterative optimization framework. The existence of Nash equilibrium strategies in the game-theoretic model has been analyzed. For the individual optimization of each agent's threshold policy, we further propose a programming scheme by transforming a constrained threshold optimization into an unconstrained Lagrangian optimization (ULO). The individual ULO is integrated into the DBR framework to enable agents to cooperate and converge to a global optimum in a distributed manner. Finally, simulation results are provided to validate the proposed method and demonstrate its significant advantage over other existing distributed methods. The numerical results also show that the proposed method can achieve comparable performance to a centralized optimization method.

With the advance of unmanned aerial vehicles (UAVs) and low earth orbit (LEO) satellites, the integration of space, air and ground networks has become a potential solution to the beyond fifth generation (B5G) Internet of remote things (IoRT) networks. However, due to the network heterogeneity and the high mobility of UAVs and LEOs, how to design an efficient UAV-LEO integrated data collection scheme without infrastructure support is very challenging. In this paper, we investigate the resource allocation problem for a two-hop uplink UAV-LEO integrated data collection for the B5G IoRT networks, where numerous UAVs gather data from IoT devices and transmit the IoT data to LEO satellites. In order to maximize the data gathering efficiency in the IoT-UAV data gathering process, we study the bandwidth allocation of IoT devices and the 3-dimensional (3D) trajectory design of UAVs. In the UAV-LEO data transmission process, we jointly optimize the transmit powers of UAVs and the selections of LEO satellites for the total uploaded data amount and the energy consumption of UAVs. Considering the relay role and the cache capacity limitations of UAVs, we merge the optimizations of IoT-UAV data gathering and UAV-LEO data transmission into an integrated optimization problem, which is solved with the aid of the successive convex approximation (SCA) and the block coordinate descent (BCD) techniques. Simulation results demonstrate that the proposed scheme achieves better performance than the benchmark algorithms in terms of both energy consumption and total upload data amount.

In this paper, we investigate the resource and interference management problem in a novel scenario where multiple unmanned aerial vehicle base stations (UAV-BSs) provide cellular services to UAV users (UAV-UEs) by reusing both licensed and unlicensed spectrum. Considering the co-existence of terrestrial cellular, WiFi and UAV-BSs, a joint optimization problem is formulated for both subchannel allocation and power control of UAV-UEs over the licensed/unlicensed spectrum in order to maximize the uplink sum-rate of the multi-cell UAV-cellular network. Since the formulated problem is NP-hard, we decompose it into three sub-problems. Specifically, we first use the convex optimization and the Hungarian algorithm to obtain the global optimal of power and subchannel allocations in the licensed spectrum, respectively. Then, we propose a matching game with externalities and coalition game algorithms to obtain the Nash stable of the subchannel allocation in the unlicensed band. Local optimal power assignment in the unlicensed spectrum is obtained using the successive convex approximation (SCA) method. An iterative algorithm is thereby developed to solve the three sub-problems sequentially till reaching convergence. Simulation results show that the proposed algorithm can improve the network capacity by nearly two times than the Long Term Evolution-Advanced (LTE-A).

In this paper, we investigate multi-dimensional resource management for unmanned aerial vehicles (UAVs) assisted vehicular networks. To efficiently provide on-demand resource access, the macro eNodeB and UAV, both mounted with multi-access edge computing (MEC) servers, cooperatively make association decisions and allocate proper amounts of resources to vehicles. Since there is no central controller, we formulate the resource allocation at the MEC servers as a distributive optimization problem to maximize the number of offloaded tasks while satisfying their heterogeneous quality-of-service (QoS) requirements, and then solve it with a multi-agent deep deterministic policy gradient (MADDPG)-based method. Through centrally training the MADDPG model offline, the MEC servers, acting as learning agents, then can rapidly make vehicle association and resource allocation decisions during the online execution stage. From our simulation results, the MADDPG-based method can converge within 200 training episodes, comparable to the single-agent DDPG (SADDPG)-based one. Moreover, the proposed MADDPG-based resource management scheme can achieve higher delay/QoS satisfaction ratios than the SADDPG-based and random schemes.

With a massive number of Internet-of-Things (IoT) devices connecting with the Internet via 5G or beyond 5G (B5G) wireless networks, how to support massive access for coexisting cellular users and IoT devices with quality-of-service (QoS) guarantees over limited radio spectrum is one of the main challenges. In this paper, we investigate the multi-operator dynamic spectrum sharing problem to support the coexistence of rate guaranteed cellular users and massive IoT devices. For the spectrum sharing among mobile network operators (MNOs), we introduce a wireless spectrum provider (WSP) to make spectrum trading with MNOs through the Stackelberg pricing game. This framework is inspired by the active radio access network (RAN) sharing architecture of 3GPP, which is regarded as a promising solution for MNOs to improve the resource utilization and reduce deployment and operation cost. For the coexistence of cellular users and IoT devices under each MNO, we propose the coexisting access rules to ensure their QoS and the priority of cellular users. In particular, we prove the uniqueness of the Stackelberg equilibrium (SE) solution, which can maximize the payoffs of MNOs and WSP simultaneously. Moreover, we propose an iterative algorithm for the Stackelberg pricing game, which is proved to achieve the unique SE solution. Extensive numerical simulations demonstrate that, the payoffs of WSP and MNOs are maximized and the SE solution can be reached. Meanwhile, the proposed multi-operator dynamic spectrum sharing algorithm can support more than almost 40% IoT devices compared with the existing no-sharing method, and the gap is less than about 10% compared with the exhaustive method.

In this paper, we investigate a radio access network (RAN) slicing problem for Internet of vehicles (IoV) services with different quality of service (QoS) requirements, in which multiple logically-isolated slices are constructed on a common roadside network infrastructure. A dynamic RAN slicing framework is presented to dynamically allocate radio spectrum and computing resource, and distribute computation workloads for the slices. To obtain an optimal RAN slicing policy for accommodating the spatial-temporal dynamics of vehicle traffic density, we first formulate a constrained RAN slicing problem with the objective to minimize long-term system cost. This problem cannot be directly solved by traditional reinforcement learning (RL) algorithms due to complicatedcoupled constraintsamong decisions. Therefore, we decouple the problem into a resource allocation subproblem and a workload distribution subproblem, and propose atwo-layer constrainedRL algorithm, namedResourceAllocation andWorkload diStribution (RAWS) to solve them. Specifically, anouter layerfirst makes the resource allocation decision via an RL algorithm, and then aninner layermakes the workload distribution decision via an optimization subroutine. Extensive trace-driven simulations show that the RAWS effectively reduces the system cost while satisfying QoS requirements with a high probability, as compared with benchmarks.

In this paper, we aim to make the best joint decision of device selection and computing and spectrum resource allocation for optimizing federated learning (FL) performance in distributed industrial Internet of Things (IIoT) networks. To implement efficient FL over geographically dispersed data, we introduce a three-layer collaborative FL architecture to support deep neural network (DNN) training. Specifically, using the data dispersed in IIoT devices, the industrial gateways locally train the DNN model and the local models can be aggregated by their associated edge servers every FL epoch or by a cloud server every a few FL epochs for obtaining the global model. To optimally select participating devices and allocate computing and spectrum resources for training and transmitting the model parameters, we formulate a stochastic optimization problem with the objective of minimizing FL evaluating loss while satisfying delay and long-term energy consumption requirements. Since the objective function of the FL evaluating loss is implicit and the energy consumption is temporally correlated, it is difficult to solve the problem via traditional optimization methods. Thus, we propose a “Reinforcement on Federated” (RoF) scheme, based on deep multi-agent reinforcement learning, to solve the problem. Specifically, the RoF scheme is executed decentralizedly at edge servers, which can cooperatively make the optimal device selection and resource allocation decisions. Moreover, a device refinement subroutine is embedded into the RoF scheme to accelerate convergence while effectively saving the on-device energy. Simulation results demonstrate that the RoF scheme can facilitate efficient FL and achieve better performance compared with state-of-the-art benchmarks.

Millimeter-wave (mmWave) frequency bands, which offer abundant underutilized spectral resources, have been explored and exploited in the past several years to meet the requirements of emerging wireless services highlighted by high data rates, ultrareliability, and ultralow delivery latency. Yet, the unique characteristics of mmWave, e.g., continuous wide bandwidth, large path, and penetration losses, along with hardware constraints, call for innovative technologies for mmWave communication. Recently, an extensive amount of work on mmWave communication has been carried out by researchers and practitioners from both academia and industry, and various technologies have been developed for mmWave communication systems to fulfill the full potential of mmWave frequency bands. In this article, we present a comprehensive survey of the standardization of mmWave communication, the latest progress and outcomes of the research on mmWave communication technologies, and the emerging applications of mmWave communication. In particular, we provide a timely and in-depth summary of the state-of-the-art technology specifications of mmWave communication with an emphasis on the physical (PHY) layer. Then, we elaborate on a number of well-established or promising antenna architectures in mmWave communication systems and investigate the enabling PHY layer transmission technologies. Finally, we show some existing and emerging applications of mmWave communication and discuss the potential open research issues.

Support vector machine (SVM) classification techniques have been widely adopted for building clinical decision models. In cloud-based remote clinical decision services, a healthcare center outsources the clinical decision model to a cloud server, which then provides remote clinical decision services to end users. In this article, we propose a practical and secure SVM classification scheme (${\sf SSVMC}$) for cloud-based remote clinical decision services. Specifically, we first extract SVM decision rules from an SVM classifier. Then, we leverage symmetric key encryption to protect the confidentiality of medical data and prevent the cloud service provider from misusing intellectual property of the outsourced clinical model. Finally, we build encrypted indexes to achieve efficient SVM classification. We define a leakage function, formulate a security definition, and provide a simulation-based security proof for${\sf SSVMC}$. The performance analysis demonstrates that${\sf SSVMC}$achieves linear computational complexity when an SVM classifier (a.k.a., the clinical decision model) is pre-trained. The simulations evaluate the impact of several parameters on time costs. The experimental evaluations show the performance differences between${\sf SSVMC}$and several existing schemes in terms of time costs, storage costs, communication costs, and precisions in a real-world clinical dataset, which demonstrate that${\sf SSVMC}$is computationally efficient with high decision accuracy.

The deployment of cloud storage services has significant benefits in managing data for users. However, it also causes many security concerns, and one of them is data integrity. Public verification techniques can enable a user to employ a third-party auditor to verify the data integrity on behalf of her/him, whereas existing public verification schemes are vulnerable toprocrastinating auditorswho may not perform verifications on time. Furthermore, most of public verification schemes are constructed on the public key infrastructure (PKI), and thereby suffer from certificate management problem. In this paper, we propose acertificatelesspublicverification scheme againstprocrastinatingauditors (CPVPA) by usingblockchain technology. The key idea is to require auditors to record each verification result into a transaction on a blockchain. Because transactions on the blockchain are time-sensitive, the verification can be time-stamped after the transaction is recorded into the blockchain, which enables users to check whether auditors perform the verifications at the prescribed time. Moreover, CPVPA is built on certificateless cryptography, and is free from the certificate management problem. We present rigorous security proofs to demonstrate the security of CPVPA, and conduct a comprehensive performance evaluation to show that CPVPA is efficient.

With proliferation of computation-intensive Internet of Things (IoT) applications, the limited capacity of end devices can deteriorate service performance. To address this issue, computation tasks can be offloaded to the Mobile Edge Computing (MEC) for processing. However, it consumes considerable energy to transmit and process these tasks. In this paper, we study the energy efficient task offloading in MEC. Specifically, we formulate it as a stochastic optimization problem, with the objective of minimizing the energy consumption of task offloading while guaranteeing the average queue length. Solving this offloading optimization problem faces many technical challenges due to the uncertainty and dynamics of wireless channel state and task arrival process, and the large scale of solution space. To tackle these challenges, we apply stochastic optimization techniques to transform the original stochastic problem into a deterministic optimization problem, and propose an energy efficient dynamic offloading algorithm called EEDOA. EEDOA can be implemented in an online manner to make the task offloading decisions with polynomial time complexity. Theoretical analysis is provided to demonstrate that EEDOA can approximate the minimal transmission energy consumption while still bounding the queue length. Experiment results are presented which show the EEDOA’s effectiveness.

As an emerging computing paradigm, mobile edge computing (MEC) can improve users’ service experience by provisioning the cloud resources close to the mobile devices. With MEC, computation-intensive tasks can be processed on the MEC servers, which can greatly decrease the mobile devices’ energy consumption and prolong their battery lifetime. However, the highly dynamic task arrival and wireless channel states pose great challenges on the computation task allocation in MEC. This paper jointly investigates the task allocation and CPU-cycle frequency, to achieve the minimum energy consumption while guaranteeing that the queue length is upper bounded. We formulate it as a stochastic optimization problem, and with the aid of stochastic optimization methods, we decouple the original problem into two deterministic optimization subproblems. An online Task Offloading and Frequency Scaling for Energy Efficiency (TOFFEE) algorithm is proposed to obtain the optimal solutions of these subproblems concurrently. TOFFEE can obtain the close-to-optimal energy consumption while bounding the applications’ queue length. Performance evaluation is conducted which verifies TOFFEE’s effectiveness. Experiment results indicate that TOFFEE can decrease the energy consumption by about 15 percent compared with the RLE algorithm, and by about 38 percent compared with the RME algorithm.

Mobile edge computing is emerging as a new computing paradigm that provides enhanced experience to mobile users via low latency connections and augmented computation capacity. As the amount of user requests is time-varying, while the computation capacity of edge hosts is limited, Cloud Assisted Mobile Edge (CAME) computing framework is introduced to improve the scalability of the edge platform. By outsourcing mobile requests to clouds with various types of instances, the CAME framework can accommodate dynamic mobile requests with diverse quality of service requirements. In order to provide guaranteed services at minimal system cost, the edge resource provisioning and cloud outsourcing of the CAME framework should be carefully designed in a cost-efficient manner. Specifically, two fundamental issues should be answered: (1) what is the optimal edge computation capacity configuration? and (2) what types of cloud instances should be tenanted and what is the amount of each type? To solve these issues, we formulate the resource provisioning in CAME framework as an optimization problem. By exploiting the piecewise convex property of this problem, the Optimal Resource Provisioning (ORP) algorithms with different instances are proposed, so as to optimize the computation capacity of edge hosts and meanwhile dynamically adjust the cloud tenancy strategy. The proposed algorithms are proved to be with polynomial computational complexity. To evaluate the performance of the ORP algorithms, extensive simulations and experiments are conducted based on both the widely-used traffic models and the Google cluster usage tracelogs, respectively. It is shown that the proposed ORP algorithms outperform the local-first and cloud-first benchmark algorithms in system flexibility and cost-efficiency.

Cloud storage enables users to outsource data to storage servers and retrieve target data efficiently. Some of the outsourced data are very sensitive and should be prevented for any leakage. Generally, if users conventionally encrypt the data, searching is impeded. Public-key encryption with keyword search (PEKS) resolves this tension. Whereas, it is vulnerable to keyword guessing attacks (KGA), since keywords are low-entropy. In this paper, we present a secure PEKS scheme called SEPSE against KGA, where users encrypt keywords with the aid of dedicated key servers via a threshold and oblivious way. SEPSE supports key renewal to periodically replace an existing key with a new one on each key server to thwart the key compromise. Furthermore, SEPSE can efficiently resist online KGA, where each keyword request made by a user is integrated into a transaction on a public blockchain (e.g., Ethereum), which allows key servers to learn the number of keyword requests made by the user without requiring a synchronization between them for per-user rate limiting. Security analysis and performance evaluation demonstrate that SEPSE provides a stronger security guarantee compared with existing schemes, at the expense of acceptable computational costs.

In this article, we propose an accountable and efficient data sharing scheme for industrial IoT (IIoT), named an accountable and data sharing scheme (ADS), in which a data owner can pursue the responsibility of a data receiver if the latter leaks some sensitive shared data to the public for profits while without permission (i.e., accountability). Specifically, ADS is built upon an adaptive decentralized oblivious transfer protocol together with a zero-knowledge proof technique, which enables the data receiver's private key to be hidden from the data owner and yet correctly embedded into the shared data during the process of data sharing. Once data breaches occur, the private key can be automatically revealed to the data owner so as to achieve the accountability. In addition, with ADS, a group of sharing providers can also assist IIoT devices in handling heavy computational tasks via the secret sharing technique without sacrificing the security. Extensive performance evaluations are conducted, and the simulation results demonstrate that ADS has high computational efficiency, making it well fit for IIoT.

Industrial wireless networks (IWNs) have attracted significant attention for providing time-critical delivery services, which can benefit from device-to-device (D2D) communication for low transmission delay. In this article, a distributed scheduling problem is investigated for D2D-enabled IWNs, where D2D links have various age-of-information (AoI) constraints for information freshness. This problem is formulated as a constrained optimization problem to optimize D2D packet delivery over limited spectrum resources, which is intractable since D2D users have no prior knowledge of the operating environment. To tackle this problem, in this article, an AoI-aware scheduling scheme is proposed based on primal-dual optimization and actor--critic reinforcement learning. In specific, multiple local actors for D2D devices learn AoI-aware scheduling policies to make on-site decisions with their stochastic AoI constraints addressed in the dual domain. An edge-based critic estimates the performance of all actors' decision-making policies from a global view, which can effectively address the nonstationary environment caused by concurrent learning of multiple local actors. Theoretical analysis on the convergence of learning is provided and simulation results demonstrate the effectiveness of the proposed scheme.

Collaboration among industrial Internet of Things (IoT) devices and edge networks is essential to support computation-intensive deep neural network (DNN) inference services, which require low delay and high accuracy. Sampling rate adaption, which dynamically configures the sampling rates of industrial IoT devices according to network conditions, is the key in minimizing the service delay. In this article, we investigate the collaborative DNN inference problem in industrial IoT networks. To capture the channel variation and task arrival randomness, we formulate the problem as a constrained Markov decision process (CMDP). Specifically, sampling rate adaption, inference task offloading, and edge computing resource allocation are jointly considered to minimize the average service delay while guaranteeing the long-term accuracy requirements of different inference services. Since CMDP cannot be directly solved by general reinforcement learning (RL) algorithms due to the intractable long-term constraints, we first transform the CMDP into an MDP by leveraging the Lyapunov optimization technique. Then, a deep RL-based algorithm is proposed to solve the MDP. To expedite the training process, an optimization subroutine is embedded in the proposed algorithm to directly obtain the optimal edge computing resource allocation. Extensive simulation results are provided to demonstrate that the proposed RL-based algorithm can significantly reduce the average service delay while preserving long-term inference accuracy with a high probability.

Connected vehicles have been considered as an effective solution to enhance driving safety as they can be well aware of nearby environments by exchanging safety beacons periodically. However, under dynamic traffic conditions, especially for dense-vehicle scenarios, the naive beaconing scheme where vehicles broadcast beacons at a fixed rate with a fixed transmission power can cause severe channel congestion and thus degrade the beaconing reliability. In this paper, by considering the kinematic status and beaconing rate together, we study the rear-end collision risk and define a danger coefficient ρ to capture the danger threat of each vehicle being in the rear-end collision. In specific, we propose a fully distributed adaptive beacon control scheme, called ABC, which makes each vehicle actively adopt a minimal but sufficient beaconing rate to avoid the rear-end collision in dense scenarios based on individually estimated ρ. With ABC, vehicles can broadcast at the maximum beaconing rate when the channel medium resource is enough and meanwhile keep identifying whether the channel is congested. Once a congestion event is detected, an NP-hard distributed beacon rate adaptation (DBRA) problem is solved with a greedy heuristic algorithm, in which a vehicle with a higher ρ is assigned with a higher beaconing rate while keeping the total required beaconing demand lower than the channel capacity. We prove the heuristic algorithm's close proximity to the optimal result and thoroughly analyze the communication overhead of ABC scheme. By using Simulation of Urban MObility (SUMO)-generated vehicular traces, we conduct extensive simulations to demonstrate the efficacy of our proposed ABC scheme. Simulation results show that vehicles can adapt beaconing rates according to the driving safety demand, and the beaconing reliability can be guaranteed even under high-dense vehicle scenarios.

In this paper, we design dynamic probabilistic caching for the scenario when the instantaneous content popularity may vary with time while it is possible to predict the average content popularity over a time window. Based on the average content popularity, optimal content caching probabilities can be found, e.g., from solving optimization problems, and existing results in the literature can implement the optimal caching probabilities via static content placement. The objective of this work is to design dynamic probabilistic caching that: i) converge (in distribution) to the optimal content caching probabilities under time-invariant content popularity, and ii) adapt to the time-varying instantaneous content popularity under time-varying content popularity. Achieving the above objective requires a novel design of dynamic content replacement because static caching cannot adapt to varying content popularity while classic dynamic replacement policies, such as LRU, cannot converge to target caching probabilities (as they do not exploit any content popularity information). We model the design of dynamic probabilistic replacement policy as the problem of finding the state transition probability matrix of a Markov chain and propose a method to generate and refine the transition probability matrix. Extensive numerical results are provided to validate the effectiveness of the proposed design.

It is widely recognized that connected vehicles have the potential to further improve the road safety, transportation intelligence and enhance the in-vehicle entertainment. By leveraging the 5G enabled Vehicular Ad hoc NETworks (VANET) technology, which is referred to as 5G-VANET, a flexible software-defined communication can be achieved with ultra-high reliability, low latency, and high capacity. Many enabling applications in 5G-VANET rely on sharing mobile data among vehicles, which is still a challenging issue due to the extremely large data volume and the prohibitive cost of transmitting such data using 5G cellular networks. This article focuses on efficient cooperative data sharing in edge computing assisted 5G-VANET. First, to enable efficient cooperation between cellular communication and Dedicated Short-Range Communication (DSRC), we first propose a software-defined cooperative data sharing architecture in 5G-VANET. The cellular link allows the communications between OpenFlow enabled vehicles and the Controller to collect contextual information, while the DSRC serves as the data plane, enabling cooperative data sharing among adjacent vehicles. Second, we propose a graph theory based algorithm to efficiently solve the data sharing problem, which is formulated as a maximum weighted independent set problem on the constructed conflict graph. Specifically, considering the continuous data sharing, we propose a balanced greedy algorithm, which can make the content distribution more balanced. Furthermore, due to the fixed amount of computing resources allocated to this software-defined cooperative data sharing service, we propose an integer linear programming based decomposition algorithm to make full use of the computing resources. Extensive simulations in NS3 and SUMO demonstrate the superiority and scalability of the proposed software-defined architecture and cooperative data sharing algorithms.

Widespread and large-scale WiFi systems have been deployed in many corporate locations, while the backhual capacity becomes the bottleneck in providing high-rate data services to a tremendous number of WiFi users. Mobile edge caching is a promising solution to relieve backhaul pressure and deliver quality services by proactively pushing contents to access points (APs). However, how to deploy cache in large-scale WiFi system is not well studied yet quite challenging since numerous APs can have heterogeneous traffic characteristics, and future traffic conditions are unknown ahead. In this paper, given the cache storage budget, we explore the cache deployment in a large-scale WiFi system, which contains 8,000 APs and serves more than 40,000 active users, to maximize the long-term caching gain. Specifically, we first collect two-month user association records and conduct intensive spatio-temporal analytics on WiFi traffic consumption, gaining two major observations. First, per AP traffic consumption varies in a rather wide range and the proportion of AP distributes evenly within the range, indicating that the cache size should be heterogeneously allocated in accordance to the underlying traffic demands. Second, compared to a single AP, the traffic consumption of a group of APs (clustered by physical locations) is more stable, which means that the short-term traffic statistics can be used to infer the future long-term traffic conditions. We then propose our cache deployment strategy, named LeaD (i.e., Large-scale WiFi Edge cAche Deployment), in which we first cluster large-scale APs into well-sized edge nodes, then conduct the stationary testing on edge level traffic consumption and sample sufficient traffic statistics in order to precisely characterize long-term traffic conditions, and finally devise the TEG (Traffic-wEighted Greedy) algorithm to solve the long-term caching gain maximization problem. Extensive trace-driven experiments are carried out, and the results demonstrate that LeaD is able to achieve the near-optimal caching performance and can outperform other benchmark strategies significantly.

Powering cellular networks with hybrid energy supplies is not only environment-friendly but can also reduce the on-grid energy consumption, thus being emerging as a promising solution for green networking. Intelligent management of spectrum and power can increase the network utility in cellular networks with hybrid energy supplies, usually at the cost of higher energy consumption. Unlike prior studies on either the network utility maximization or on-grid energy cost minimization, this paper studies the joint spectrum and power allocation problem that maximizes the system revenue in a heterogeneous small-cell network with hybrid energy supplies. Specifically, the system revenue is considered as the difference between the network utility and on-grid energy cost. By developing the convexity of the optimization problem through transformation and reparameterization, we propose a joint spectrum and power allocation algorithm based on the primal-dual arguments to obtain the optimal solution by iteratively solving the primal and dual sub-problems of the convex optimization problem. To solve the primal sub-problem, we further propose the Lagrangian maximization based on the alternating direction method of multipliers (ADMM), and derive the optimal solution in the closed-form expression at each iteration. It is shown that the proposed joint spectrum and power allocation algorithm approaches the global optimality at the rate of 1=n with n being the number of iterations. Also, the proposed ADMM-based Lagrangian maximization algorithm approaches the primal optimal solution with the time complexity of O(1=εr) iterations with εrbeing the termination parameter. Simulation results show that in comparison with the power control with equal frequency allocation algorithm and frequency allocation with equal power allocation algorithms the proposed algorithm increases the system revenue by over 20 and 60 percent without consuming more on-grid energy when the proportional fairness utility and the weighted sum rate utility are considered with the approximate system parameter settings, respectively. Meanwhile, in comparison with the full frequency reuse case, the proposed algorithm increases the system revenue by 20 percent at least in terms of the weighted sum rate utility, although it achieves the similar system revenue when considering the proportional fairness utility. Simulation results also show that our proposed algorithm can perform well under the realistic fast fading channel conditions.

Mobile Edge Computing (MEC) can facilitate various important image retrieval applications for mobile users by offloading partial computation tasks from resource-limited mobile devices to edge servers. However, existing related works suffer from two major limitations. (i) High network bandwidth cost: they need to extract numerous features from the image and upload these feature data to the cloud server. (ii) Lowretrieval accuracy: they separate the feature extraction processes from the image data set in the cloud server, thus unable to provide effective features for accurate image retrieval. In this paper, we propose a cloud-guided feature extraction approach for mobile image retrieval. In the proposed approach, the cloud server first leverages the relationships among labeled images in the data set to learn a projection matrix P. Then, it uses the matrix P to extract discriminative features from the image data set and form a low-dimensional feature data set. Following that, the cloud server sends the matrix P to the edge server and uses it to multiply the image χ. The result PTχ, i.e., image features, is uploaded to the cloud server to find the label of the image with the most similar multiplying result. The label is regarded as the retrieval result and returned to the mobile user. In the cloud-guided feature extraction approach, the matrix P can extract a small number of effective image features, which not only reduces network traffic but also improves retrieval accuracy. We have implemented a prototype system to validate the proposed approach and evaluate its performance by conducting extensive experiments using a real MEC environment and data set. The experimental results show that the proposed approach reduces the network traffic by nearly 93 percent and improves the retrieval accuracy by nearly 6.9 percent compared with the state-of-the-art image retrieval approaches in MEC.

As an emerging service architecture, microservice enables decomposition of a monolithic web service into a set of independent lightweight services which can be executed independently. With mobile edge computing, microservices can be further deployed in edge clouds dynamically, launched quickly, and migrated across edge clouds easily, providing better services for users in proximity. However, the user mobility can result in frequent switch of nearby edge clouds, which increases the service delay when users move away from their serving edge clouds. To address this issue, this article investigates microservice coordination among edge clouds to enable seamless and real-time responses to service requests from mobile users. The objective of this work is to devise the optimal microservice coordination scheme which can reduce the overall service delay with low costs. To this end, we first propose a dynamic programming-based offline microservice coordination algorithm, that can achieve the globally optimal performance. However, the offline algorithm heavily relies on the availability of the prior information such as computation request arrivals, time-varying channel conditions and edge cloud's computation capabilities required, which is hard to be obtained. Therefore, we reformulate the microservice coordination problem using Markov decision process framework and then propose a reinforcement learning-based online microservice coordination algorithm to learn the optimal strategy. Theoretical analysis proves that the offline algorithm can find the optimal solution while the online algorithm can achieve near-optimal performance. Furthermore, based on two real-world datasets, i.e., the Telecom's base station dataset and Taxi Track dataset from Shanghai, experiments are conducted. The experimental results demonstrate that the proposed online algorithm outperforms existing algorithms in terms of service delay and migration costs, and the achieved performance is close to the optimal performance obtained by the offline algorithm.

Password-based single-sign-on authentication has been widely applied in mobile environments. It enables an identity server to issue authentication tokens to mobile users holding correct passwords. With an authentication token, one can request mobile services from related service providers without multiple registrations. However, if an adversary compromises the identity server, he can retrieve users' passwords by performing dictionary guessing attacks (DGA) and can overissue authentication tokens to break the security. In this paper, we propose a password-based threshold single-sign-on authentication scheme dubbed PROTECT that thwarts adversaries who can compromise identity server(s), where multiple identity servers are introduced to authenticate mobile users and issue authentication tokens in a threshold way. PROTECT supports key renewal that periodically updates the secret on each identity server to resist perpetual leakage of the secret. Furthermore, PROTECT is secure against off-line DGA: a credential used to authenticate a user is computed from the password and a server-side key. PROTECT is also resistant to online DGA and password testing attacks in an efficient way. We conduct a comprehensive performance evaluation of PROTECT, which demonstrates the high efficiency on the user side in terms of computation and communication and proves that it can be easily deployed on mobile devices.

Named Data Networking (NDN) is considered as a prominent architecture towards future Wireless Local Area Networks (WLAN), and multicast plays an important role in data delivery such as media streaming, multipoint videoconferencing, etc. However, to achieve high-efficiency multicast in NDN WLAN is challenging for two significant reasons. First, without feedback mechanism in IEEE 802.11 standards, to guarantee reliability, the current multicast scheme transmits the multicast data with the basic rate (e.g., 1 Mbps for IEEE 802.11b), which inevitably increases the transmission delay for high-speed consumers. Second, as a NDN multicast group is constituted by consumers who are requesting the same content, multicast groups are easy to form and evolve rapidly, where a data rate adaptation scheme is requisite to accommodate differential multicast groups. In this paper, we propose a multi-stage multicast rate adaptation scheme for NDN WLAN, namedNDN-MMRA, to minimize the total transmission time with reliability guarantee for multicast group members. InNDN-MMRA, by checking the Pending Interest Table (PIT) status information, the number of consumers in each multicast group as well as their receiving capabilities are known ahead; with the available data rates in a specific 802.11 standard,NDN-MMRAdetermines: 1) how many transmission stages are required; and 2) in each stage, which data rate should be adopted. The merit is that with multi-stage transmissions, the data rate can be adapted in descending order to accommodate high-speed consumers with delay minimized, and low-speed consumers with reliability guaranteed. We implementNDN-MMRAin NS-3 by adopting the ndnSIM module, and conduct extensive experiments to demonstrate its efficacy under different IEEE 802.11 standards and various underlying WLAN topologies.

In this paper, we propose a secure computationoffloading scheme (SCOS) in intelligently connected vehicle (ICV) networks, aiming to minimize overall latency of computing via offloading part of computational tasks to nearby servers in small cell base stations (SBSs), while securing the information delivered during offloading and feedback phases via physical layer security. Existing computation offloading schemes usually neglected time-varying characteristics of channels and their corresponding secrecy rates, resulting in an inappropriate task partition ratio and a large secrecy outage probability. To address these issues, we utilize an ergodic secrecy rate to determine how many tasks are offloaded to the edge, where ergodic secrecy rate represents the average secrecy rate over all realizations in a time-varying wireless channel. Adaptive wiretap code rates are proposed with a secrecy outage constraint to match time-varying wireless channels. In addition, the proposed secure beamforming and artificial noise (AN) schemes can improve the ergodic secrecy rates of uplink and downlink channels even without eavesdropper channel state information (CSI). Numerical results demonstrate that the proposed schemes have a shorter system delay than the strategies neglecting time-varying characteristics.

Multi-Unmanned Aerial Vehicle (UAV) control is one of the major research interests in UAV-based networks. Yet few existing works focus on how the network should optimally react when the UAV lineup and user distribution change. In this work, proactive self-regulation (PSR) of UAV-based networks is investigated when one or more UAVs are about to quit or join the network, with considering dynamic user distribution. We target at an optimal UAV trajectory control policy which proactively relocates the UAVs whenever the UAV lineupis about tochange, rather than passively dispatches the UAVsafterthe change. Specifically, a deep reinforcement learning (DRL)-based self-regulation approach is developed to maximize the accumulated user satisfaction (US) score for a certain period within which at least one UAV will quit or join the network. To handle the changed dimension of the state-action space before and after the lineup changes, the state transition is deliberately designed. To accommodate continuous state and action space, an actor-critic based DRL, i.e., deep deterministic policy gradient (DDPG), is applied with better convergence stability. To effectively promote learning exploration around the timing of lineup change, an asynchronous parallel computing (APC) learning structure is proposed. Referred to as PSR-APC, the developed approach is then extended to the case of dynamic user distribution by incorporating time as one of the agent states. Finally, numerical results are presented to demonstrate the convergence and superiority of PSR-APC over a passive reaction method, and its capability in jointly handling the dynamics of both UAV lineup and user distribution.

In this article, we investigate a computing task scheduling problem in space-air-ground integrated network (SAGIN) for delay-oriented Internet of Things (IoT) services. In the considered scenario, an unmanned aerial vehicle (UAV) collects computing tasks from IoT devices and then makes online offloading decisions, in which the tasks can be processed at the UAV or offloaded to the nearby base station or the remote satellite. Our objective is to design a task scheduling policy that minimizes offloading and computing delay of all tasks given the UAV energy capacity constraint. To this end, we first formulate the online scheduling problem as an energy-constrained Markov decision process (MDP). Then, considering the task arrival dynamics, we develop a novel deep risk-sensitive reinforcement learning algorithm. Specifically, the algorithm evaluates the risk, which measures the energy consumption that exceeds the constraint, for each state and searches the optimal parameter weighing the minimization of delay and risk while learning the optimal policy. Extensive simulation results demonstrate that the proposed algorithm can reduce the task processing delay by up to 30% compared to probabilistic configuration methods while satisfying the UAV energy capacity constraint.

Unmanned Aerial Vehicle (UAV)-assisted communication is a promising technique for future communication. In this paper, the UAV serves as base station (BS) to provide energy-efficient and fair communication service for ground users (GUs). We first derive the energy consumption model of a quad-rotor UAV as a function of UAV's 3D movement. Then we formulate the problem where UAV aims to maximize the defined fair throughput within limited on-board energy through 3D trajectory and frequency band allocation. The formulated problem is hard to deal with for the GUs' movement and complicated nonconvex objective function. Then we propose a deep reinforcement learning (DRL) based method to transform the original problem into maximizing accumulative reward. Simulation results demonstrate that the proposed method outperforms two baselines in terms of fairness and total throughput.

With the explosive growth of the Internet of Things (IoT), a massive number of IoT devices are deployed so as to realize a variety of advanced applications, i.e., environmental monitoring and smart traffic. There are two main characteristics in these typical applications, namely the massive connectivity and the sporadic transmission. The massive connectivity usually leads the IoT system to be spatially undersampled, since the number of devices is much larger than the number of receiver antennas, which brings difficulties and challenges to symbol detection. Fortunately, the sporadic transmission in IoT communication introduces sparsity into transmitted symbols, thanks to which we are able to perform symbol detection even in a spatially undersampled scenario. In this paper, we attempt to incorporate deep learning (DL) into the symbol detection of spatially undersampled IoT with sporadically transmitting devices. Specifically, we propose a novel DL-based detector named DeepIoT that employs a variant autoencoder network to recover both the indices of active devices and their transmitting symbols by using only the received signal. Simulation results show that the DeepIoT can outperform various existing methods and has only a 1.5- 2dB signal-to-noise ratio (SNR) loss compared to the optimal maximum likelihood (ML) detector.

With the proliferation of cloud services, cloud-based image retrieval services enable large-scale image outsourcing and ubiquitous image searching. While enjoying the benefits of the cloud-based image retrieval services, critical privacy concerns may arise in such services since they may contain sensitive personal information. In this paper, we propose an efficient and Privacy-Preserving Image Retrieval scheme with Key Switching Technique (PPIRS). PPIRS utilizes the inner product encryption for measuring Euclidean distances between image feature vectors and query vectors in a privacy-preserving manner. Due to the high dimension of the image feature vectors and the large scale of the image databases, traditional secure Euclidean distance comparison methods provide insufficient search efficiency. To prune the search space of image retrieval, PPIRS tailors key switching technique (KST) for reducing the dimension of the encrypted image feature vectors and further achieves low communication overhead. Meanwhile, by introducing locality sensitive hashing (LSH), PPIRS builds efficient searchable indexes for image retrieval by organizing similar images into a bucket. Security analysis shows that the privacy of both outsourced images and queries are guaranteed. Extensive experiments on a real-world dataset demonstrate that PPIRS achieves efficient image retrieval in terms of computational cost.

The significant enhancement of vehicular communications together with artificial intelligence (AI) have opened up new horizons for innovative data-driven traffic management solution within intelligent transportation system (ITS). In this paper, to alleviate progressively worse urban traffic, we propose an efficient vehicle-to-everything (V2X) communications enabled non-signalized intersection management framework for automated vehicles. First, a resource reservation model involved with different functional planes has been developed for vehicle collision avoidance in V2X enabled non-signalized intersections. Second, reinforcement learning (RL) solution is leveraged to enhance management efficiency of non-signalized scheduling. Furthermore, considering the dimensionality disaster problem of vehicle state caused by complicated traffic environment, we propose a function approximation based non-signalized intersection control (FA-NIC) algorithm to obtain optimal scheduling strategy. Simulation results are provided to demonstrate the effectiveness of our proposed non-signalized intersection management solution.

The Uplink (UL) and Downlink (DL) decoupled cellular access through flexible cell association has attracted a lot of attention due to numerous benefits such as higher network throughput, better load balancing, and lower energy consumption, etc. In this paper, we introduce a novel reinforcement learning aided decoupled RAN access framework for Cellular Vehicle-to-Everything (V2X) communications, and propose a two-step RAN slicing approach to dynamically allocate the radio resource to V2X services in different time granularity. We derive an innovative QoS metric of V2V cellular mode by taking consideration of the bidirectional nature of V2V cellular communications. Moreover, we maximize the sum utility considering the proposed QoS metric by leveraging the Deep Deterministic Policy Gradient (DDPG) enabled RAN slicing method. Simulation results are provided to demonstrate the advance of the proposed reinforcement learning aided decoupled RAN slicing framework in achieving load balancing, maximizing total network utility and satisfying the QoS metric of Cellular V2X communications.

In this paper, we investigate the joint task assignment and resource allocation for deep neural network (DNN) inference in the device-edge-cloud based industrial Internet of things (IIoT) networks. To efficiently orchestrate the limited spectrum and computing resources in IIoT networks for massive DNN inference tasks, a resource management problem is formulated with the objective of maximizing the average inference accuracy while satisfying the quality-of-service of DNN inference tasks. Considering the strict delay requirements of inference tasks, we transform the formulated problem into a Markov decision process, and propose a deep deterministic policy gradient based learning algorithm to obtain the solution rapidly. Simulation results show that the proposed algorithm can achieve high average inference accuracy.

Truth discovery is one of the key technologies to extract truthful information from unreliable sensory data collected by different mobile devices in mobile crowdsensing, but the sensory data and the outputs of truth discovery (i.e., truths and mobile devices' weights) may contain sensitive information and cause serious privacy concerns. In this paper, we propose an efficient and privacy-preServing non-interActive Truth discovEry scheme (SATE) in mobile crowdsensing. Specifically, SATE is designed based on a two-cloud model. First, the sensory data is encoded into two parts (i.e., perturbed data and noises) at the mobile device, which are maintained by two clouds separately. Second, by utilizing an adapted distributed public key homomorphic cryptosystem, two clouds can co-operatively exchange the intermediate weights and truths in a privacy preserving manner and thus achieve privacy-preserving truth discovery without the participation of the mobile devices. Security analysis demonstrates that SATE can provide full privacy protection for sensory data, weights, and truths. Performance evaluation also shows that SATE can achieve high computational efficiency and low communication overhead on the mobile devices, since there is no time-consuming cryptographic operation involved.

Mobile edge computing (MEC) has been recognized as a promising technology to support various emerging services in vehicular networks. With MEC, vehicle users can offload their computation-intensive applications (e.g., intelligent path planning and safety applications) to edge computing servers located at roadside units. In this paper, an efficient computing offloading and server collaboration approach is proposed to reduce computing service delay and improve service reliability for vehicle users. Task partition is adopted, whereby the computation load offloaded by a vehicle can be divided and distributed to multiple edge servers. By the proposed approach, the computation delay can be reduced by parallel computing, and the failure in computing results delivery can also be alleviated via cooperation among edges. The offloading and computing decision-making is formulated as a long-term planning problem, and a deep reinforcement learning technique, i.e., deep deterministic policy gradient, is adopted to achieve the optimal solution of the complex stochastic nonlinear integer optimization problem. Simulation results show that our collaborative computing approach can adapt to different service environments and outperform the greedy offloading approach.

Accurate cellular traffic load prediction is a pre-requisite for efficient and automatic network planning and management. Considering diverse users' activities at different locations and times, it is technically challenging to characterize the network resource demands at different time scales via traditional prediction methods. In this paper, we propose to combine the long short-term memory (LSTM) and Gaussian process regression (GPR) to achieve accurate single-cell level cellular traffic prediction, using the open Milan cellular traffic dataset provided by Telecom Italia. Firstly, the dominant periodic components of the cellular data are extracted, and then the small components are fed to the LSTM network. To further improve the prediction accuracy, GPR is used to recover the residual components. Extensive experiments are conducted based on the dataset, and it is shown that the proposed LSTM-GPR scheme outperforms the benchmark schemes, especially for a relatively long time and burst traffic prediction.

Decision trees are prevalent machine learning models used for data classification. Cloud servers can build their decision tree models and provide users with many classification services, such as remote medical diagnosis. Moreover, users would also like to share the classification results with third-party applications for customized services. For example, the medical diagnosis results can be further utilized by a nutrition application to provide users with dietary recommendations. However, as stringent privacy regulations of personal data, such as GDPR, takes effect, the decision tree evaluation must comply with the following requirements. First, the model parameters and user data (input and output) should be protected from public disclosure. Second, different applications should obtain the classification results with users' consent in the context of user-customised services. In this paper, we propose a consent-based privacy-preserving decision tree evaluation scheme, named CPDE. Specifically, to achieve model parameter privacy and user data privacy, the original decision tree evaluation is conducted in a private manner in CPDE. As a result, all operations can be performed in the encrypted domain using an additively homomorphic encryption primitive and a secure comparison protocol. In addition, by integrating a proxy re-encryption technique, CDPE enables user-authorized applications to obtain the user's classification results even if the user is offline. The security analysis shows that CPDE achieves the desirable security properties and performance evaluation demonstrates CPDE is efficient and is suitable for real-world implementations.

In this paper, we propose a multiobjective optimization framework to jointly optimize power and position of full-duplex (FD) receiver in the covert communication. By introducing a legitimate FD receiver (Bob) with random transmit power, the signal of a legitimate transmitter (Alice) can be transmitted covertly since an eavesdropper (Willie) is confronted with interference uncertainty and makes an incorrect decision for signal detection. Therefore, we optimize the position and the transmit power range of Bob in order to maximize the achievable transmission rate from Alice to Bob and average covert probability at Willie simultaneously. Due to the presence of multiple optimization objectives, the nondominated sorting genetic algorithm II (NSGA-II) is utilized to explore the Pareto front and to give a set of solutions that reveal different tradeoffs between the two conflicting objectives. Simulation results are provided to reveal the Pareto front and to illustrate the effect of transmit power of Alice and Bob on the Pareto front.

Cloud-based deep learning (DL) solutions have been widely used in applications ranging from image recognition to speech recognition. Meanwhile, as commercial software and services, such solutions have raised the need for intellectual property rights protection of the underlying DL models. Watermarking is the mainstream of existing solutions to address this concern, by primarily embedding pre-defined secrets in a model's training process. However, existing efforts almost exclusively focus on detecting whether a target model is pirated, without considering traitor tracing. In this paper, we present SecureMark_DL, which enables a model owner to embed a unique fingerprint for every customer within parameters of a DL model, extract and verify the fingerprint from a pirated model, and hence trace the rogue customer who illegally distributed his model for profits. We demonstrate that SecureMark_DL is robust against various attacks including fingerprints collusion and network transformation (e.g., model compression and model fine-tuning). Extensive experiments conducted on MNIST and CIFAR10 datasets, as well as various types of deep neural network show the superiority of SecureMark_DL in terms of training accuracy and robustness against various types of attacks.

Large-scale WiFi system is gaining an increasing momentum rapidly in most corporate places. Enabling edge functions on the system is imperative to support unprecedented edge applications. However, building edge functionalities at each AP may incur frequent service migrations, low resource utilization, and inflexible resource provisioning. It is thus prospective to federate suitable APs to create a resource-pooled edge system such that users in association with federated APs can share the pooled resource. In this paper, we propose a novel architecture, named SoSA, to Socialize Static APs via user association transition activities for edge resource pooling. A reference implementation of SoSA is developed under an operating large-scale WiFi system in a campus area of 3.0925 km2. The novelty and contribution of SoSA lie in its three-layer design. In transition data feeding layer, we collect and process 25,074,733 association records of 55,809 users from 7,404 APs in a real WiFi system. In sociality construction and characterization layer, we construct an AP contact graph based on user transition statistics, under which we empirically study the sociality of APs and explore their evolving patterns. In edge resource pooling layer, by harnessing the AP sociality, we are able to customize resource pooling strategy to improve service provisioning performance. With adopting SoSA, we systematically investigate the performance of AP federation strategy in reducing service migration when users frequently transit among APs. Extensive data-driven experiments corroborate the efficacy of SoSA.

In this paper, we propose an online control frame-work to dynamically slice the network resource for isolated service provisioning in Space-Air-Ground integrated Vehicular Network (SAGVN). In particular, at a given time slot, the system makes online decisions on the request admission and scheduling, UAV dispatching, and resource slicing for different services. To characterize the impact of those parameters, we construct a time-averaged queue stability criteria by taking queue backlogs of all services into consideration, and formulate a system revenue function which incorporates the time-averaged system throughput and UAV dispatching cost. The objective is to maximize the system revenue while stabilizing the time-averaged queue, which can be achieved via the Lyapunov optimization theory. By bounding the drift-plus-penalty, the problem then can be decoupled into four independent subproblems, which are readily solved. The merits of our control framework are three-fold: 1) the system can admit and process as many requests as possible; 2) the time-averaged UAV dispatching cost is minimized; and 3) service queues can be stabilized over time. Extensive simulations are carried out, and the results demonstrate that the control framework can effectively achieve the system revenue maximization and queueing stabilization. Moreover, it can balance the trade-off among system throughput, UAV dispatching cost, and queueing states via parameter tuning.

In this paper, we investigate joint vehicle association and multi-dimensional resource management in a vehicular network assisted by multi-access edge computing (MEC) and unmanned aerial vehicle (UAV). To efficiently manage the available spectrum, computing, and caching resources for the MEC-mounted base station and UAVs, a resource optimization problem is formulated and carried out at a central controller. Considering the overlong solving time of the formulated problem and the sensitive delay requirements of vehicular applications, we transform the optimization problem using reinforcement learning and then design a deep deterministic policy gradient (DDPG)-based solution. Through training the DDPG-based resource management model offline, optimal vehicle association and resource allocation decisions can be obtained rapidly. Simulation results demonstrate that the DDPG-based resource management scheme can converge within 200 episodes and achieve higher delay/quality-of-service satisfaction ratios than the random scheme.

For the massive access of Internet-of-Things (IoT) devices in 5G or beyond 5G (B5G) wireless networks, how to support the coexisting of cellular users and massive IoT devices with quality-of-service (QoS) guarantees over limited spectrum is challenging. In this paper, inspired by the active radio access network (RAN) sharing standard of 3GPP, we present a heterogeneous multi-operator spectrum sharing architecture by leveraging the spectrum trading to support the coexistence of QoS-guaranteed cellular users and massive IoT devices with non-orthogonal multiple access (NOMA). In the architecture, we formulate the spectrum trading between the wireless spectrum provider (WSP) and mobile network operators (MNOs) as a Stackelberg pricing game. Meanwhile, for each MNO, the cellular users and massive IoT devices can be both serviced in the uplink NOMA networks with QoS guarantees. For the pricing game, we prove the uniqueness of the equilibrium solution, which can maximize the payoffs of MNOs and WSP simultaneously. Moreover, we propose an iterative pricing algorithm to achieve the equilibrium solution with theoretical optimality guarantees. Simulation results demonstrate that our framework can support more IoT devices and reach the optimal spectral bandwidth price.

Mobile edge computing (MEC) has recently become a promising paradigm to meet the increasing computing requirement of mobile devices, and hybrid energy supply has been considered as an effective approach for saving the energy consumption of the MEC system and making it environmentally friendly. In particular, the joint task scheduling and energy management (TSEM) scheme plays a crucial role in reaping the benefits of MEC with hybrid energy supply. In this article, we focus on jointly optimizing the TSEM decisions to maximize the utility of the MEC system which accounts for both the computation throughput and the fairness among different cells, by formulating a stochastic optimization problem subject to the constraints of queue stability and energy budget. We transform the formulated problem into a deterministic problem and then decouple it into four independent subproblems, which can be solved in a distributed manner without future system statistical information. An online TSEM algorithm is developed to derive the optimal solutions to these subproblems. Mathematical analysis shows that TSEM can achieve a close-to-optimal system utility and realize the utility-queue tradeoff. The experimental results validate the advantages of TSEM in improving the system utility and stabilizing the queue length.

Network provenance is essential for Internet-of-Things (IoT) network administrators to conduct the network diagnostics and identify root causes of network errors. However, the distributed nature of the IoT network results in the management of the provenance data at different trust domains, which poses concerns on the security and trustworthiness of the cross-domain network diagnostics. In this article, we propose a blockchain-based architecture for secure and efficient distributed network provenance (SEDNP) in the IoT. Instead of directly storing and querying the whole provenance data on the blockchain with prohibitive implementation cost, we introduce a unified provenance query model and develop a provenance digest strategy that: 1) enables compact (constant size) on-blockchain digests of provenance data and a multilevel index regardless of provenance data volume and 2) ensures the correctness and integrity of provenance query results through the verification of the on-blockchain digests. We formally define the security requirements as Archiving Security along with thorough security analysis. Moreover, we conduct extensive experiments with the integration of a verifiable computation (VC) framework and a blockchain testing network. The experimental results are provided as performance benchmarks to demonstrate the application feasibility of SEDNP.

The k-nearest neighbors (kNN) classification has been widely used for defective product identification and anomaly detection in the Industrial Internet of Things (IIoT). In this article, we propose a secure and efficient distributed kNN classification algorithm (SEED-kNN) to prevent information and control flow exposure while supporting large-scale data classification on distributed servers. Specifically, we first design a secure and efficient vector homomorphic encryption (VHE) scheme by constructing a key-switching matrix and a noise matrix for data encryption. Based on the designed VHE, SEEDkNN is proposed to efficiently achieve the confidentiality of data flow, kNN query, and class label, while enabling homomorphic operations on the encrypted data. Moreover, by leveraging the Map/Reduce architecture, SEED-kNN enables the kNN classification over the large-scale encrypted data on distributed servers for industrial control systems. Finally, we demonstrate that SEEDkNN achieves semantic security and high classification accuracy, and is applicable in IIoT due to its high efficiency.

The paradigm of network function virtualization (NFV) with the support of software defined networking (SDN) emerges as a promising approach for customizing network services in fifth generation (5G) networks. In this paper, a multicast service orchestration framework is presented, where joint traffic routing and virtual network function (NF) placement are studied for accommodating multicast services over an NFV-enabled physical substrate network. First, we investigate a joint routing and NF placement problem for a single multicast request accommodated over a physical substrate network, with both single-path and multipath traffic routing. The joint problem is formulated as a mixed integer linear programming (MILP) problem to minimize the function and link provisioning costs, under the physical network resource constraints, flow conservation constraints, and NF placement rules; Second, we develop an MILP formulation that jointly handles the static embedding of multiple service requests over the physical substrate network, where we determine the optimal combination of multiple services for embedding and their joint routing and placement configurations, such that the aggregate throughput of the physical substrate is maximized, while the function and link provisioning costs are minimized. Since the presented problem formulations are NP-hard, low complexity heuristic algorithms are proposed to find an efficient solution for both single-path and multipath routing scenarios. Simulation results are presented to demonstrate the effectiveness and accuracy of the proposed heuristic algorithms.

Plug-in Electric Vehicles (PEVs) keep on penetrating the automobile market. However, uncoordinated PEV charging can impair the reliability of power grid. In this paper, an interesting problem of PEV charging power allocation is investigated, in which both power distribution and transportation constraints are considered. A novel approach for PEV charging management based on optimal power flow (OPF) analysis is proposed to optimize PEV charging energy in a power distribution system. Firstly, spatial and temporal PEV demand scheduling is introduced to maximize PEV charging service capacity while considering the maximum traveling distance of PEVs. Secondly, to ensure the scalability of the OPF analysis, a distributed optimization technique, i.e., proximal Jacobian alternating direction multiplier method, is applied to attain the optimal power allocation in a decentralized manner. The resulting PEV charging service capacity in the power distribution system is improved without violating power distribution and transportation constraints. Furthermore, kernel density estimation method is adopted to identify the PEV range anxiety constraint without the PEV battery information. Simulation results are presented to validate the effectiveness of our approach with high PEV penetration.

Space-air-ground integrated networks (SAGIN) extend the capability of wireless networks and will be the essential building block for many advanced applications, like autonomous driving, earth monitoring, and etc. However, coordinating heterogeneous physical resources is very challenging in such a large-scale dynamic network. In this paper, we propose a reconfigurable service provisioning framework based on service function chaining (SFC) for SAGIN. In SFC, the network functions are virtualized and the service data needs to flow through specific network functions in a predefined sequence. The inherent issue is how to plan the service function chains over large-scale heterogeneous networks, subject to the resource limitations of both communication and computation. Specifically, we must jointly consider the virtual network functions (VNFs) embedding and service data routing. We formulate the SFC planning problem as an integer non-linear programming problem, which is NP-hard. Then, a heuristic greedy algorithm is proposed, which concentrates on leveraging different features of aerial and ground nodes and balancing the resource consumptions. Furthermore, a new metric, aggregation ratio (AR) is proposed to elaborate the communication-computation tradeoff. Extensive simulations shows that our proposed algorithm achieves near-optimal performance. We also find that the SAGIN significantly reduces the service blockage probability and improves the efficiency of resource utilization. Finally, a case study on multiple intersection traffic scheduling is provided to demonstrate the effectiveness of our proposed SFC-based service provisioning framework.

In this article, we investigate the UAV-aided edge caching to assist terrestrial vehicular networks in delivering high-bandwidth content files. Aiming at maximizing the overall network throughput, we formulate a joint caching and trajectory optimization (JCTO) problem to make decisions on content placement, content delivery, and UAV trajectory simultaneously. As the decisions interact with each other and the UAV energy is limited, the formulated JCTO problem is intractable directly and timely. To this end, we propose a deep supervised learning scheme to enable intelligent edge for real-time decision-making in the highly dynamic vehicular networks. In specific, we first propose a clustering-based two-layered (CBTL) algorithm to solve the JCTO problem offline. With a given content placement strategy, we devise a time-based graph decomposition method to jointly optimize the content delivery and trajectory design, with which we then leverage the particle swarm optimization (PSO) algorithm to further optimize the content placement. We then design a deep supervised learning architecture of the convolutional neural network (CNN) to make fast decisions online. The network density and content request distribution with spatio-temporal dimensions are labeled as channeled images and input to the CNN-based model, and the results achieved by the CBTL algorithm are labeled as model outputs. With the CNN-based model, a function which maps the input network information to the output decision can be intelligently learnt to make timely inference and facilitate online decisions. We conduct extensive trace-driven experiments, and our results demonstrate both the efficiency of CBTL in solving the JCTO problem and the superior learning performance with the CNN-based model.

The space-air-ground integrated network (SAGIN) aims to provide seamless wide-area connections, high throughput and strong resilience for 5G and beyond communications. Acting as a crucial link segment of the SAGIN, unmanned aerial vehicle (UAV)-satellite communication has drawn much attention. However, it is a key challenge to track dynamic channel information due to the low earth orbit (LEO) satellite orbiting and three-dimensional (3D) UAV trajectory. In this paper, we explore the 3D channel tracking for a Ka-band UAV-satellite communication system. We firstly propose a statistical dynamic channel model called 3D two-dimensional Markov model (3D-2D-MM) for the UAV-satellite communication system by exploiting the probabilistic insight relationship of both hidden value vector and joint hidden support vector. Specifically, for the joint hidden support vector, we consider a more realistic 3D support vector in both azimuth and elevation direction. Moreover, the spatial sparsity structure and the time-varying probabilistic relationship between degree patterns named the spatial and temporal correlation, respectively, are studied for each direction. Furthermore, we derive a novel 3D dynamic turbo approximate message passing (3D-DTAMP) algorithm to recursively track the dynamic channel with the 3D-2D-MM priors. Numerical results show that our proposed algorithm achieves superior channel tracking performance to the state-of-the-art algorithms with lower pilot overhead and comparable complexity.

Vehicular communication networks have emerged to enable numerous vehicular data services and applications. Conventional vehicularad hocnetworks (VANETs) are often operated in thead hocmode and mainly focus on road safety applications based on the connection between vehicles and roadside units (RSUs). To support vehicular communications, dedicated shortrange communication (DSRC) and car-to-car communication consortium (C2C-CC) have been initiated in the United States and Europe, respectively. With the new era of the Internet of Things (IoT), the conventional VANETs have evolved to the Internet of Vehicles (IoV). In IoV, each vehicle is envisioned as an intelligent object, equipped with sensing platforms, computing facilities, control units, and storages and is connected to any entity (other vehicles, RSUs, charging/gas stations, cloud, and so on) via vehicle-to-everything (V2X) communications. Intelligent vehicles can take different roles, i.e., being both a client and a server, taking and providing big data services, leading to numerous new IoV applications, from assisted/autonomous driving and platooning, secure information sharing and learning to traffic control and optimization.

Software defined networking (SDN) and network function virtualization (NFV) are key enabling technologies in fifth generation (5G) communication networks for embedding service-level customized network slices in a network infrastructure, based on statistical resource demands to satisfy long-term quality of service (QoS) requirements. However, traffic loads in different slices are subject to changes over time, resulting in challenges for consistent QoS provisioning. In this paper, a dynamic flow migration problem for embedded services is studied, to meet end-to-end (E2E) delay requirements with time-varying traffic. A multi-objective mixed integer optimization problem is formulated, addressing the trade-off between load balancing and reconfiguration overhead. The problem is transformed to a tractable mixed integer quadratically constrained programming (MIQCP) problem. It is proved that there is no optimality gap between the two problems; hence, we can obtain the optimum of the original problem by solving the MIQCP problem with some post-processing. To reduce time complexity, a heuristic algorithm based on redistribution of hop delay bounds is proposed to find an efficient solution. Numerical results are presented to demonstrate the aforementioned trade-off, the benefit from flow migration in terms of E2E delay guarantee, as well as the effectiveness and efficiency of the heuristic solution.

In this paper, we investigate physical (PHY) layer message authentication to combat adversaries with infinite computational capacity. Specifically, a PHY-layer authentication framework over a wiretap channel (W1; W2) is proposed to achieve information theoretic security with the same key. We develop a theorem to reveal the requirements/conditions for the authentication framework to be information-theoretic secure for authenticating a polynomial number of messages in terms of n. Based on this theorem, we design an authentication protocol that can guarantee the security requirements, and prove its authentication rate can approach infinity when n goes to infinity. Furthermore, we design and implement a feasible and efficient message authentication protocol over binary symmetric wiretap channel (BSWC) by using Linear Feedback Shifting Register based (LFSR-based) hash functions and strong secure polar code. Through extensive simulations, it is demonstrated that the proposed protocol can achieve high authentication rate, with low time cost and authentication error rate.

Though representing a promising approach for personalization, targeting, and recommendation, aggregation of user profiles from multiple social networks will inevitably incur a serious privacy leakage issue. In this paper, we propose a Novel Heterogeneous De-anonymization Scheme (NHDS) aiming at de-anonymizing heterogeneous social networks. NHDS first leverages the network graph structure to significantly reduce the size of candidate set, then exploits user profile information to identify the correct mapping users with a high confidence. Performance evaluation on real-world social network datasets shows that NHDS significantly outperforms the prior schemes. Finally, we perform an empirical study on privacy leakage arising from cross-network aggregation based on four real-world social network datasets. Our findings show that 39.9 percent more information is disclosed through de-anonymization and the de-anonymized ratio is 84 percent. The detailed privacy leakage of user demographics and interests is also examined, which demonstrates the practicality of the identified privacy leakage issue.

Mobile crowdsensing enables a crowd of individuals to cooperatively collect data for special interest customers using their mobile devices. The success of mobile crowdsensing largely depends on the participating mobile users. The broader participation, the more sensing data are collected; nevertheless, the more replicate data may be generated, thereby bringing unnecessary heavy communication overhead. Hence it is critical to eliminate duplicate data to improve communication efficiency, a.k.a., data deduplication. Unfortunately, sensing data is usually protected, making its deduplication challenging. In this paper, we propose a fog-assisted mobile crowdsensing framework, enabling fog nodes to allocate tasks based on user mobility for improving the accuracy of task assignment. Further, a fog-assisted secure data deduplication scheme (Fo-SDD) is introduced to improve communication efficiency while guaranteeing data confidentiality. Specifically, a BLS-oblivious pseudo-random function is designed to enable fog nodes to detect and remove replicate data in sensing reports without exposing the content of reports. To protect the privacy of mobile users, we further extend the Fo-SDD to hide users' identities during data collection. In doing so, Chameleon hash function is leveraged to achieve contribution claim and reward retrieval for anonymous mobile users. Finally, we demonstrate that both schemes achieve secure, efficient data deduplication.

Driven by the explosive growth in computation-intensive applications in future 5G networks and industries, mobile edge computing (MEC), which enables smart terminals (STs) to offload their computation workloads to nearby edge servers (ESs) in radio access networks, has attracted increasing attention. In this article, we investigate the energy-efficient multitask multiaccess MEC via nonorthogonal multiple access (NOMA). Exploiting NOMA, an ST with multiple tasks can offload the respective computation workloads of different tasks to different ESs simultaneously. To study this problem, we adopt a two-step approach. Specifically, we first consider a given task-ES assignment and formulate a joint optimization of the tasks' computation offloading, local computation-resource allocation, and the NOMA-transmission duration, with the objective of minimizing the ST's total energy consumption for completing all tasks. Next, based on the optimal offloading solution for the given task-ES assignment, we further investigate how to properly assign different tasks to the ESs for further minimizing the ST's total energy consumption. For both the formulated problems, we propose efficient algorithms to compute the respective solutions. Numerical results are provided to validate the effectiveness of our proposed algorithms. The results also show that our proposed NOMA-enabled multitask multiaccess computation offloading can outperform conventional orthogonal multiple access based offloading scheme, especially when the tasks have heavy computation-workload requirements and stringent delay limits.

Industrial mortgage enables companies to acquire loan for business venture or investment purposes by pledging their industrial assets to financial institutions. To prevent double-mortgage fraud of borrowers, information exchange among different financial institutions is necessary. On the other hand, it results in the privacy leakage of borrowers. In this article, we construct a blockchain-based accountable and privacy-preserving industrial mortgage scheme (BAPIM). BAPIM enables financial institutions to share the mortgage data of borrowers in an efficient and secure manner, that achieves the borrower identity privacy and accountability at the same time. Specifically, borrower identity is concealed on the blockchain by anonymous identity credential, while financial institutions can still uncover the identity of a misbehaving borrower if he pledges the same asset for multiple mortgages. We demonstrate that BAPIM achieves the desirable security properties and has high computational efficiency, so as to be suitable for the industrial mortgage management.

To develop smart city and intelligent manufacturing, video cameras are being increasingly deployed. In order to achieve fast and accurate response to live video queries (e.g., license plate recording and object tracking), the real-time high-volume video streams should be delivered and analyzed efficiently. In this article, we introduce an end-edge-cloud coordination framework for low-latency and accurate live video analytics. Considering the locality of video queries, edge platform is designated as the system coordinator. It accepts live video queries and configures the related end cameras to generate video frames that meet quality requirements. By taking into account the latency constraint, edge computing resources are subtly distributed to process the live video frames from different sources such that the analytic accuracy of the accepted video queries can be maximized. Since the amount of required edge computing resource and video quality to accurately address different video queries are unknown in advance, we propose an online video quality and computing resource configuration algorithm to gradually learn the optimal configuration strategy. Extensive simulation results show that as compared to other benchmarks, the proposed configuration algorithm can effectively improve the analytic accuracy, while providing low-latency response.

The IEEE 802.11p-based dedicated short range communication (DSRC) is essential to enhance driving safety and improve road efficiency by enabling rapid cooperative message exchanging. However, there is a lack of good understanding on the DSRC performance in urban environments for vehicle-to-vehicle (V2V) communications, which impedes its reliable and efficient application. In this paper, we first conduct intensive data analytics on V2V performance, based on a large amount of real-world DSRC communications trace collected in Shanghai city, and obtain several key insights as follows. First, among many context factors, the non-line-of-sight (NLoS) link condition is the major factor degrading V2V performance. Second, the durations of line-of-sight (LoS) and NLoS transmission conditions follow power law distributions, which indicate that the probability of experiencing long LoS/NLoS conditions both could be high. Third, the packet inter-reception (PIR) time distribution follows an exponential distribution in the LoS conditions but a power law in the NLoS conditions, which means that the consecutive packet reception failures rarely appear in the LoS conditions but can constantly appear in the NLoS conditions. Based on these findings, we propose a context-aware reliable beaconing scheme, called CoBe, to enhance the broadcast reliability for safety applications. The CoBe is a fully distributed scheme, in which a vehicle first detects the link condition with each of its neighbors by machine learning algorithms, then exchanges such link condition information with its neighbors, and finally selects the minimal number of helper vehicles to rebroadcast its beacons to those neighbors in bad link condition. To analyze and evaluate the CoBe performance, a two-state Markov chain is devised to model beaconing behaviors. The extensive trace-driven simulations are conducted to demonstrate the efficacy of CoBe.

In this paper, a dynamic spectrum management framework is proposed to improve spectrum resource utilization in a multi-access edge computing (MEC) in autonomous vehicular network (AVNET). To support the increasing communication data traffic and guarantee quality-of-service (QoS), spectrum slicing, spectrum allocating, and transmit power controlling are jointly considered. Accordingly, three non-convex network utility maximization problems are formulated to slice spectrum among base stations (BSs), allocate spectrum among autonomous vehicles (AVs) associated with a BS, and control transmit powers of BSs, respectively. Through linear programming relaxation and first-order Taylor series approximation, these problems are transformed into tractable forms and then are jointly solved through an alternate concave search (ACS) algorithm. As a result, the optimal spectrum slicing ratios among BSs, optimal BS-vehicle association patterns, optimal fractions of spectrum resources allocated to AVs, and optimal transmit powers of BSs are obtained. Based on our simulation, a high aggregate network utility is achieved by the proposed spectrum management scheme compared with two existing schemes.

Mobile crowdsensing engages a crowd of individuals to use their mobile devices to cooperatively collect data about social events and phenomena for customers with common interest. It can reduce the cost on sensor deployment and improve data quality with human intelligence. To enhance data trustworthiness, it is critical for the service provider to recruit mobile users based on their personal features, e.g., mobility pattern and reputation, but it leads to the privacy leakage of mobile users. Therefore, how to resolve the contradiction between user privacy and task allocation is challenging in mobile crowdsensing. In this paper, we propose SPOON, a strong privacy-preserving mobile crowdsensing scheme supporting accurate task allocation based on geographic information and credit points of mobile users. In SPOON, the service provider enables to recruit mobile users based on their locations, and select proper sensing reports according to their trust levels without invading user privacy. By utilizing proxy re-encryption and BBS+ signature, sensing tasks are protected and reports are anonymized to prevent privacy leakage. In addition, a privacy-preserving credit management mechanism is introduced to achieve decentralized trust management and secure credit proof for mobile users. Finally, we show the security properties of SPOON and demonstrate its efficiency in terms of computation and communication.

We propose Chronos+, an accurate blockchain-based time-stamping scheme for outsourced data, where both the storage and time-stamping services are provided by cloud service providers. Specifically, Chronos+integrates a file into a transaction on a blockchain once the file is created, which guarantees the file's latest creation time to be the time when the block containing the transaction is appended to the blockchain. A sufficient number of consecutive blocks that are latest confirmed on the blockchain is embedded into the file at the creation time. These blocks serve as a time-dependent random seed to prove the earliest creation time, due to blockchains' chain quality property. Chronos+makes the file's timestamp corresponding to a time interval formed by the earliest and latest creation times which are derived from the heights of the corresponding blocks. Due to blockchains' chain growth property, such a height-derived timestamp can ensure that the time intervals' range is within a few minutes so as to guarantee the accuracy. We also point out potential threats towards outsourced time-sensitive files and present security analyses to prove that Chronos+is secure against these threats. Comprehensive performance evaluations demonstrate the efficiency and practicality of Chronos+.

In this paper, we propose a low-complexity user selection algorithm for an uplink multiuser transmission in millimeter wave (mmWave) WLAN. We first formulate the user selection problem, taking hybrid beamforming (HBF), an NP-hard problem, into consideration. We then develop a three-step HBF algorithm that incorporates user selection. Specifically, users can be selected based on semi-orthogonality instead of collecting perfect channel state information (CSI) from all potential users. We optimize the digital beamforming to mitigate residual interference among the selected users. Furthermore, we provide analytical validation for the proposed user selection algorithm and study the impact of angle correlation, analog beam pattern, and beamwidth on the achievable rate of the selected users. Extensive simulations validate the performance of the proposed overall HBF algorithm when compared with existing solutions.

Unmanned Aerial Vehicle (UAV)-assisted communication has drawn increasing attention recently. In this paper, we investigate 3D UAV trajectory design and band allocation problem considering both the UAV's energy consumption and the fairness among the ground users (GUs). Specifically, we first formulate the energy consumption model of a quad-rotor UAV as a function of the UAV's 3D movement. Then, based on the fairness and the total throughput, the fair throughput is defined and maximized within limited energy. We propose a deep reinforcement learning (DRL)-based algorithm, named as EEFC-TDBA (energy-efficient fair communication through trajectory design and band allocation) that chooses the state-of-the-art DRL algorithm, deep deterministic policy gradient (DDPG), as its basis. EEFC-TDBA allows the UAV to: 1) adjust the flight speed and direction so as to enhance the energy efficiency and reach the destination before the energy is exhausted; and 2) allocate frequency band to achieve fair communication service. Simulation results are provided to demonstrate that EEFC-TDBA outperforms the baseline methods in terms of the fairness, the total throughput, as well as the minimum throughput.

Recent developments in industrial wireless sensor networks (IWSNs) have revolutionized industrial automation systems. However, harsh industrial environment poses great challenges to a time-critical and reliable wireless communication. For instance, effects of multipath fading, noise and co-channel interference can have unpredictable and time-varying impacts on the propagation channel, leading to the failure of on-time packet delivery. To address this problem, in this paper we propose a channel-based Optimal Back-off Delay Control (OBDC) scheme which can minimize the total time a packet spends in the sensor node (TSN) by assessing the features of a generic wireless channel. Specifically, we first explore the channel impairments by investigating the probability density function (PDF) of the level crossing rate (LCR) of the received signal in the industrial wireless environment. Then, with the obtained channel assessment results, we develop a phase-type semi-Markov model to investigate the probability distribution of the back-off delay of a packet in the sensor node (SN). The probability distribution of the back-off delay can be further substituted with TSN according to the queuing theory. The proposed OBDC scheme examines the Kullback-Leibler (KL) divergence between the obtained distribution of TSN and the packet arrival rate, and reduces the TSN according to an objective function which is constantly renewed in every transmission round with regard to a delay constraint. The simulation results show that the OBDC scheme can reduce TSN and guarantee to keep the TSN in an acceptable range even though the wireless channel is impaired by interference effects. It also shows that the OBDC scheme can reduce the proportion of packets meeting their deadline to the total packets in transmission when the number of SN and LCR changes

Energy harvesting with combination of multiple cooperating radio access technologies (multi-RAT) is regarded as a promising network paradigm to improve the energy efficiency of 5G networks. In this paper, we propose a resource-on-demand energy scheduling strategy for multi-RAT wireless networks, where the varying energy demand of the network can be satisfied by both grid power and harvested energy. Due to the high sensitivity to uncertainties of energy harvesting, a dynamic network energy queue model is designed first considering the inherently stochastic and intermittent nature of the harvested energy. Then, to minimize time-averaged grid power consumption and make effective utilization of harvested energy, the energy scheduling is formulated as a stochastic optimization problem subject to data queue stability and harvested energy availability, considering the high dynamics of wireless channel states and renewable energy sources. Following the Lyapunov optimization framework, the stochastic grid power minimization problem is decomposed into a network flow control subproblem, a network energy management subproblem, and a network resource allocation subproblem, respectively. In order to solve these subproblems, we develop a dynamic adaptive resource-on-demand (DAROD) algorithm to effectively reduce the grid power consumption cost by allocating the resource efficiently based on the dynamic demands of multi-RAT networks. Finally, the tradeoff between grid power consumption cost and network delay is achieved, in which the increase of network delay is approximately linear with the network control parameter V and the decrease of grid power consumption cost is at the speed of 1/V. Extensive simulations are conducted to verify the theoretical analysis and show the effectiveness of our proposed algorithm.

In mobile edge computing (MEC), the computation offloading of massive users could cause the task uploading congestion to deteriorate the users' offloading delay. The non-orthogonal multiple access (NOMA) enabled MEC is envisioned to address this issue by allowing multiple users to simultaneously upload their tasks on one subchannel. However, the differentiated uploading delay of users may make task uploading completion inconsistent with NOMA decoding order, which complicates the co-channel interference and restricts NOMA to reducing the uploading delay. In this paper, we characterize the interaction between the differentiated uploading delay and co-channel interference for a pair of NOMA users. Furthermore, we propose a computation offloading scheme to reduce the users' average offloading delay by jointly optimizing offloading decision and resource allocation. Specifically, the proposed scheme first obtains the optimal power allocation based on the characterized interaction and the closed-form solution of computation resource allocation by convex programming. Then, the NOMA user pairing and offloading decision are iteratively determined by semidefinite relaxation and convex-concave procedure. Simulation results show that the proposed scheme effectively mitigates co-channel interference under differentiated uploading delay of users and outperforms in reducing the users' average offloading delay and increasing the number of users to offload tasks.

In this paper, we propose a joint spatial division and multiplexing (JSDM) beamforming based on a neighbor scheme for frequency division duplex (FDD) massive multi-input multi-output (MIMO) systems. The neighbor-based JSDM (N-JSDM) can fully utilize signal space, leading to higher spectral efficiency over the conventional JSDMs. The reason is that for the neighbor scheme, neighbors and non-neighbors are classified adaptively by the angles of departure (AoD), and the prebeamformer is designed to mitigate the non-neighbors' interference by the statistical channel state information. The effective channel matrix after the prebeamformer then becomes a band matrix, from which the downlink training length (DTL) and the channel feedback length are much smaller than the number of antennas. Moreover, an optimal prebeamformer which is proved to be able to achieve the same system capacity as the full CSI system is proposed, followed by a suboptimal prebeamformer with constrained DTL, and a DFT-based prebeamformer. On the other hand, the neighbors' interference is mitigated using the banded channel state information. Simulation results validate the good performance of the proposed N-JSDM.

To enable ever-increasing vehicular applications, heterogeneous vehicular networks (HetVNets) are recently emerged to provide enhanced and cost-effective wireless network access. Meanwhile, edge caching is imperative to future vehicular content delivery to reduce the delivery delay and alleviate the unprecedented backhaul pressure. This work investigates content caching in HetVNets where Wi-Fi roadside units (RSUs), TV white space (TVWS) stations, and cellular base stations are considered to cache contents and provide content delivery. Particularly, to characterize the intermittent network connection provided by Wi-Fi RSUs and TVWS stations, we establish an on-off model with service interruptions to describe the content delivery process. Content coding then is leveraged to resist the impact of unstable network connections with optimized coding parameters. By jointly considering file characteristics and network conditions, we minimize the average delivery delay by optimizing the content placement, which is formulated as an integer linear programming (ILP) problem. Adopting the idea of student admission model, the ILP problem is then transformed into a many-to-one matching problem and solved by our proposed stable-matching-based caching scheme. Simulation results demonstrate that the proposed scheme can achieve near-optimal performances in terms of delivery delay and offloading ratio with low complexity.

Vehicular networks are expected to support diverse content applications with multi-dimensional quality of service (QoS) requirements, which cannot be realized by the conventional one-fit-all network management method. In this paper, a service-oriented hierarchical soft slicing framework is proposed for the cache-enabled vehicular networks, where each slice supports one service and the resources are logically isolated but opportunistically reused to exploit the multiplexing gain. The performance of the proposed framework is studied in an analytical way considering two typical on-road content services, i.e., the time-critical driving related context information service (CIS) and the bandwidth-consuming infotainment service (IS). Two network slices are constructed to support the CIS and IS, respectively, where the resource is opportunistic reused at both intra- and inter-slice levels. Specifically, the throughput of the IS slice, the content freshness (i.e., age of information) and delay performances of the CIS slice are analyzed theoretically, whereby the multiplexing gain of soft slicing is obtained. Extensive simulations are conducted on the OMNeT++ and MATLAB platforms to validate the analytical results. Numerical results show that the proposed soft slicing method can enhance the IS throughput by 30% while guaranteeing the same level of CIS content freshness and service delay.

In this paper, we propose covert localization to improve the security of wireless localization networks, which can prevent the legitimate transmission of localization signals between anchors and agent from being detected by the illegitimate warden. Specifically, we first establish a framework of covert localization and demonstrate its feasibility when the warden suffers noise uncertainty. Then, with two specific noise uncertainty distributions, we derive the fundamental limit of localization accuracy, i.e., covert squared position error bound (CSPEB), which is the achievable localization accuracy for the agent while ensuring covertness for the warden. Theoretical analysis of CSPEB demonstrates the impact of different factors on the localization accuracy. Besides, in an energy-constrained scenario, we formulate a power allocation problem to refine anchors' power to minimize the CSPEB for a given total power budget and develop an algorithm based on the semidefinite program (SDP). Simulation results verify our theoretical analysis by evaluating the effect of several representative factors on the CSPEB and show the superiority of the SDP-based power allocation algorithm to the other baseline methods.

In this paper, we investigate service offloading in an integrated terrestrial-satellite (T-S) system. We consider the terrestrial base station (TBS) and satellites to be service providers, all user equipment (UE) to be service requesters, and the service can be content delivery, computation, etc. While offloading services to the satellites can prevent the TBS from being overloaded, the quality of service (QoS), e.g., content delivery latency, may degrade, necessitating a balance between the user preference and the utilities of the TBS and satellites. From the perspective of network management, we propose an abstract model that incorporates the utilities of the TBS, the satellites, and the UE, as well as the service capacity, service load, and service cost at the TBS and the satellites. While finding the optimal offloading decision, a problem of integer programming, is NP-hard, we develop two algorithms with low complexity for finding sub-optimal solutions of the offloading decision problem in the scenarios of one satellite and multiple satellites, respectively. Moreover, we prove that the solution found by the first algorithm is guaranteed to be optimal under the condition that the tasks for service from all UE have an identical size. Numerical results demonstrate the performance of the proposed algorithms compared to that of the optimal offloading by exhaustive search and the offloading by the greedy algorithm.

Vehicle-based crowdsourcing is expected to be an economic yet efficient solution to build and maintain an accurate, fine-grained, and up-to-date environment map (i.e., high-definition map) for autonomous vehicles, which is an essential building block for safe and intelligent autonomous driving. However, how to select crowdsourcing workers with performance maximization is prudent and quite challenging since vehicles are highly dynamic and have unpredictable routes. In this paper, we study the worker selection problem for crowdsourced on-route map collection where the trade- off between the real-time worker exploration and exploitation is the main focus. Specifically, by adopting the multi-armed bandit model, we formulate a cumulative platform utility maximization problem. To solve this problem, we propose an Online Worker Selection (OWS) scheme, to learn drivers' performance and make worker selection decisions in real time. Essentially, two key designs are integrated in OWS: 1) performance transfer. If a new driver joins the crowdsourcing, we will initialize the new driver's performance based on the knowledge transferred from the existing drivers' records; and 2) marginal utility. Particularly, we carefully incorporate the platform utility to embody the marginal effect, i.e., repeated coverage by multiple vehicles on a certain road will undermine the utility. Based on the real-world vehicular GPS trace, we conduct extensive trace- driven simulations, and results demonstrate that our scheme can effectively obtain high-quality environment map, with on average 40.5% crowdsourcing utility gain over other benchmark schemes.

Supported by the technical development of electric battery and charging facilities, plug-in electric vehicle (PEV) has the potential to be mobile energy storage (MES) for energy delivery from resourceful charging stations (RCSs) to limited-capacity charging stations (LCSs). In this paper, we study the problem of using on-road PEVs as MESs for energy compensation service to compensate charging station (CS) overload. A price-incentive scheme is proposed for power system operator (PSO) to stimulate on-road MESs fulfilling energy compensation tasks. The price-service interaction between the PSO and MESs is characterized as a one-leader, multiple-follower Stackelberg game. The PSO acts as a leader to schedule on-road MESs by posting service price and on-road MESs respond to the price by choosing their service amount. The existence and uniqueness of the Stackelberg equilibrium are validated, and an algorithm is developed to find the equilibrium. Simulation results show the effectiveness of the proposed scheme in utility optimization and overload mitigation.

Non-orthogonal multiple access mobile edge computing (NOMA-MEC) is proposed to enhance the connectivity between the edge node and users for low- latency computation offloading. However, it is asynchronous for users to complete the task uploading, which complicates the co-channel interference between NOMA users to affect overall offloading delay. In this paper, we first characterize the impact of this asynchronism in task uploading on interference management in NOMA enabled computation offloading. The optimal power allocation is proposed to coordinate the co-channel interference between both NOMA users. Then, we propose a multi- user offloading scheme to jointly optimize offloading decision and NOMA user pairing, aiming to minimize the users' average delay on executing their tasks. The proposed offloading scheme is designed by formulating a binary nonlinear problem, which is solved by the proposed relaxation method and heuristic algorithm. Simulation results demonstrate that compared with other NOMA based schemes, our proposed scheme can effectively reduce the average delay of users and increase the number of users to perform computation offloading.

In this paper, we investigate the edge caching and content delivery problem for both high-speed train (HST) passengers and low-mobility cellular users. Under multi-dimensional resources constraints, we formulate an optimization problem to minimize the content retrieval delay of HST passengers and meanwhile guarantee the delay requirements of cellular users. As the formulated problem is a mixed-integer nonconvex optimization problem, which is intractable directly, we propose an efficient iterative algorithm that optimizes the three decision variables (i.e., content placement, subchannel allocation, and transmission power allocation) alternately. In specific, Lagrangian multiplier is introduced to convert the constrained optimization, which transforms the content caching problem into a Lagrangian relaxed knapsack problem. Afterwards, the subchannel assignment problem is solved by the Hungarian algorithm with polynomial time complexity, and the power allocation strategy is obtained by the bisection method. Extensive simulations are carried out and results demonstrate that our proposed caching strategy can reduce the content retrieval delay by up to 25% in comparison with the benchmark strategy.

Identity anonymization is believed to be a common mechanism to protect users' privacy in a public review platform, as each user's unique identifier is removed to ensure pseudonymity and unlinkability. However, the usefulness of the mechanism is not explicit, i.e., whether it is possible for an adversary to link anonymous reviews from the same user has not been well studied. In this paper, we attempt to explore this issue by means of machine learning techniques. Specifically, we first extract major features from anonymous reviews and propose some adaptive metrics to measure their effectiveness. Then, we exploit these features and several machine learning methods to link the anonymous reviews created by the same user in a real- world dataset. Considering that different adversaries has different background knowledge, both supervised and unsupervised methods, such as random forest and hierarchical agglomerative clustering, are designed and utilized to perform linkability attacks. The simulation results demonstrate that the supervised methods have a good performance, i.e., almost 40% anonymous reviews can be accurately linked to users if an adversary has the background knowledge. The unsupervised methods, compared with supervised methods, has a bad performance, i.e., it is difficult for an adversary without the background knowledge to link anonymous reviews with a high probability.

In this paper, we study a multi-beam millimeter- wave beamspace multiple-input multiple-output (MIMO) system with non-orthogonal multiple access (NOMA) to simultaneously accommodate multiple users in a single beam. To improve the data rate while maintaining user fairness, we analyze the max-min rate of the system via power allocation. The challenge is that the existence of both the intra- beam and inter-beam interference makes the power- allocation problem non-convex. To address this issue, we devise a bisection approach to calculate the max-min rate and the corresponding power allocation. We prove that the max-min rate can be achieved when all the users are assigned the same rate. Furthermore, our endeavors reveal that beamspace MIMO-NOMA outperforms the traditional beamspace MIMO in terms of the minimal rate when the power or the number of users is relatively small. When the power or the number of users is relatively large, traditional beamspace MIMO can outperform beamspace MIMO-NOMA since the former is free of inter-beam interference, which has been verified by simulation results.

Software-defined networking (SDN) and network function virtualization (NFV) together form a promising paradigm that enables the slicing of heterogeneous network resources for agile and efficient service customization. Among other techniques, virtual network function (VNF) mapping and scheduling are crucial to the deployment of SDN/NFV-enabled network services. In this paper, to enhance the performance of service provisioning, dynamic VNF mapping and scheduling are jointly investigated. Specifically, to achieve load balancing with QoS guarantee, we first formulate the VNF mapping and scheduling problem as a mixed integer linear programming (MILP). We then propose a two-stage online algorithm to address the NP-hardness of the MILP. In particular, when new service arrives, we map and schedule the VNFs on a service function chain (SFC) by greedily minimizing the waiting time of VNFs. If the delay requirement cannot be satisfied after the first stage, a delay-aware rescheduling scheme is triggered, in which selected existing VNFs are remapped and rescheduled. The proposed dynamic approach achieves flexible function placement and increases service acceptance ratio. Simulation results are provided to validate the effectiveness of the proposed algorithm.

Space-air-ground integrated network (SAGIN) brings great potentials to extend the terrestrial networks and satisfy the diverse service demands from many emerging applications. The major challenge is the coordination of large-scale networks with heterogeneous communication and computation resources. In this paper, flexible and reconfigurable service provisioning based on service function chaining (SFC) is exploited to address the challenge, where the traffic flow of the network services need to pass through specified virtual network functions (VNFs) in a given order. Our main target is to optimize the planning of the service function chains under limited heterogeneous resources and to map them on physical networks, considering the balance of resource utilization of both communication and computation. The SFC planning problem is formulated as an integer non-linear programming problem, which is NP-hard. Then, we propose a heuristic SFC planning algorithm (HSP) to reduce the computational complexity. Moreover, we propose a new metric, aggregation ratio (AR), to observe the tradeoff between communication and computation resource consumptions. The simulations results demonstrate that the HSP achieves near-optimal performance and the communication and computation resources can be well tradeoffed via tuning AR. The service blockage probability is significantly decreased and the efficiency of resource utilization is improved by integrating SAGIN based on SFC.

In this paper, we study the secrecy provisioning downlink transmission in an aerial-assisted network, where the unmanned aerial vehicle (UAV) serves as an aerial platform to provide secure transmission for the mobile users (MUs) with coexist of Internet of Things (IoT) nodes (INs). Specifically, secure transmission is required for MUs to combat eavesdropping attacks and a desired successful transmission probability should be ensured for INs to receive the public instruction massages. To improve the secrecy rates (SRs) for MUs, we consider an eavesdropper-free area, i.e., protected zone, surrounding the UAV. With non-orthogonal multiple access (NOMA) for MUs, the power allocation to each MU is optimized to maximize the minimum secrecy rate of MUs within the protected zone, under the constraints of successful receiving probability requirements for INs. To solve this problem, we first prove that the max-min SR can be obtained when SRs of all users are equal, and then a dichotomy-based successive power allocation policy is proposed. Numerical results show that higher max-min secrecy rate can be achieved by our proposed power allocation policy than the traditional policy.

Message-locked encryption (MLE) is a special type of symmetric encryption enabling deduplication over ciphertexts. Since an MLE key is extracted from the message itself, it is vulnerable to brute-force attacks. Existing schemes employ an independent key server to help in generating MLE keys, where the MLE key is extracted from the message and a server-side secret to thwart brute-force attacks. Whereas, the security of these schemes depends on the reliability of the key server, which causes the single-point-of- failure problem. In this paper, we propose DECKS, an encrypted data \underline{de}duplication scheme against the \underline{c}ompromised \underline{k}ey \underline{s}erver. DECKS employs multiple key servers to assist users in generating MLE keys using an oblivious and threshold-based protocol, such that compromising any key server would not break the security. To free DECKS from trusting a specific group of key servers during the lifetime of protected data, the key servers are periodically replaced by new ones to renew the security protection. Provable security and high efficiency of DECKS are demonstrated by comprehensive analyses and experimental evaluations.

Unmanned aerial vehicles (UAV) deployment is of pivotal importance in the promising space-air integrated localization network (SAILN), which is a typical partially controllable network and supports 3- dimensional (3D) localization. To improve the localization accuracy for specific area or user, several UAVs need to be deployed. This paper proposes an iterative UAV deployment strategy for SAILN, which can minimize the localization error by determining accurate 3D coordinate information (elevation and azimuth angles, distance) for all the supplementary UAVs. Specifically, based on the analysis of accuracy increment when a new UAV is added into SAILN, the genetic algorithm (GA) is leveraged to find its optimal geometric position in a constrained area that can maximize the accuracy increment. Then, UAVs are iteratively added until the desired number, i.e., the quantity budget of deployed UAVs, is achieved. Simulation results demonstrate that the proposed UAV deployment strategy provides considerably better localization accuracy compared with uniform angular arrays (UAA) and random deployment (RD).

Due to the versatile networking capability, space- air-ground integrated network (SAGIN) becomes a prominent future architecture to support the ever- increasing Internet of Things (IoT) applications. In this paper, we investigate the IoT task offloading under an SAGIN scenario where multiple IoT devices generate computing tasks to be processed. We adopt an unmanned aerial vehicle (UAV) to fly along a given trajectory to collect the tasks of IoT devices within the coverage area, and then makes the online offloading decision, i.e., processing locally, or offloading to the nearby base station or the far-away satellite. However, due to the constrained energy resources committed by UAV and the uncertainty of the system dynamics, designing an efficient computation task offloading algorithm is challenging. This dynamic scheduling problem is formulated as a constrained Markov decision process (CMDP), considering the stochastic channel conditions, UAV coverage, energy consumption, and task queue backlogs. By exploiting the stationary stochastic feature of the CMDP, the problem can be solved by the linear programming to find a stochastic policy. Simulation results demonstrate that the proposed computation offloading scheme can significantly reduce IoT task processing delay as compared to other benchmarks.

In this paper, a comprehensive software-defined networking (SDN) based transmission protocol (SDTP) is presented for fifth generation (5G) communication networks, where an SDN controller gathers network state information from the physical network to improve data transmission efficiency between end hosts, with in-path packet retransmission. In the SDTP, we first develop a new two-way handshake mechanism for connection establishment between a pair of end host. With the aid of SDN control module, signaling exchanges for establishing E2E connections are migrated to the control plane to improve resource utilization in the data plane. A new SDTP packet header format is designed to support efficient data transmission with in-path packet caching and packet retransmission. Based on the new data packet format, a novel in-path receiver-based packet loss detection and caching-based packet retransmission scheme is proposed to achieve in-path fast recovery of lost packets. Extensive simulation results are presented to validate the effectiveness of the proposed protocol in terms of low connection establishment delay and low end-to-end packet transmission delay.

Multiuser multiple input multiple output (MU-MIMO) millimeter wave (mmWave) communication is considered as a key technology to provide multi-gigabit train-to-ground wireless connections in the high-speed railway (HSR) system. Considering the power consumption and hardware constraint, the hybrid beamforming (BF), which combines analog BF and digital BF, is widely adopted in the MU-MIMO mmWave systems. In this paper, we target on an efficient hybrid BF structure design in HSR scenario with taking the practical HSR mmWave channel model into consideration. Specifically, the hybrid BF design aims at maximizing the overall throughput and is formulated as an optimization problem which is proved to be nonconvex and NP-hard. Therefore, a suboptimal yet efficient two-stage solution is proposed, where a weighted minimum mean square error (WMMSE) based beamforming strategy is exploited to devise the hybrid beamformer at the base station (BS) at the first stage, and the orthogonal matching pursuit (OMP) approach is leveraged to decouple the digital BF and analog BF at BS at the second stage. Simulation results demonstrate that higher overall throughput can be achieved by the proposed hybrid BF scheme compared to other state-of-the-art benchmarks.

Numerous crowdsourcing applications have emerged in our daily lives, which enable customers to outsource their complicated tasks to a crowd of workers. However, the information of task tags and worker profiles is explicitly obtained by the crowdsourcing server to recommend tasks effectively, which violates the privacy of both customers and workers. Moreover, the worker's ability to do the task should also be verified in a privacy-preserving way. To address these issues, we propose a privacy-preserving interest-ability based task allocation scheme in crowdsourcing, which protects both task and worker privacy and enables the crowdsourcing server to allocate tasks in a fine-grained way. Specifically, by utilizing attribute-based encryption (ABE) and proxy re-encryption based searchable encryption (PRE-SE) on the task content and task tags respectively, customers are able to enforce fine-grained ability requirements on their tasks, and workers can specify flexible interests to choose their desired tasks. Additionally, ElGamal signature enables workers to prove their abilities to the crowdsourcing server without revealing the task content. Numerical analysis and experiment results demonstrate that our proposed scheme is efficient in terms of computation and storage overhead and is practical to be implemented in crowdsourcing.

Online advertising is a popular business model where advertisers can deliver promotional marketing messages to their potential consumers via Ad brokers. However, as the proxy between advertisers and customers, a malicious Ad broker could arbitrarily fabricate the advertising rates to overcharge advertisers, which causes unnecessary financial loss. To deal with this issue, we propose a publicly verifiable and fair online advertising scheme. Specifically, a proof-of-downloading (PoD) protocol is first designed based on the zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK), to help the customer generate a unique acknowledgment for downloading the Ad; the acknowledgment will then be published to both the advertiser and the Ad broker such that anyone can verify the acknowledgment to guarantee the fairness and transparency of online advertising. Moreover, as long as the customer's private key is not leaked, our scheme can resist the collusion attack, i.e., the Ad broker and the customer collude with each other to deceive the advertiser, which has not been addressed in previous works. Finally, we evaluate the performance of the proposed scheme to demonstrate its computational efficiency.

As the number of Plug-in Electric Vehicles (PEVs) is increasing in recent years, there has been a growing interest in terms of improving the charging service for on-the-move PEVs. In this paper, a task time allocation and reward scheme for advertising PEV charging station information is proposed. PEVs passing by a charging station are assigned a period of time to spread the charging station information within an interest area. To stimulate PEVs behaving cooperatively, two incentive policies provided by the charging station are studied in the task time allocation: the pre-determined reward policy and the optimal reward policy. For the former one, a fairness task time allocation scheme is developed to maximize the utility of the recruited PEVs. For the latter one, a Stackelberg game based optimization problem is formulated to obtain the optimal reward according to the utility of the charging station. An optimization tool, Geometric Water-filling, is utilized to analyze both problems efficiently. Simulation results are provided to validate the optimality of proposed schemes.

Data classification has become an important and prevailing technique for big data analytics. Typically, a data classifier is designed and outsourced to a public cloud. A service provider then can easily provide various services and handle frequent and massive classification requests from users. With privacy concerns as well as Intellectual Property(IP) protection issues, the valuable classifier and the sensitive user data cannot be directly exposed to the public cloud. In this paper, we focus on the Support Vector Machine (SVM), one of the most popular classifiers, and propose an efficient and privacy-preserving outsourcing scheme for SVM classification in public clouds. Specifically, the service provider is allowed to transform the traditional SVM classifier to fixed hyper-rectangles and the order-preserving encryption is utilized to encrypt these hyper-rectangles as the encrypted classifier. Afterwards, the encrypted classifier is outsourced to the public cloud, and a user can submit an encrypted range query to the cloud and obtain the classification results back. Security analysis and extensive experimental evaluation demonstrate that our scheme can protect the confidentiality of classifier and users' data and achieves efficient SVM classification in terms of computational cost.

In the secure mobile advertising, mobile users privately select advertisements of interest for displaying without exposing their preferences to the ad network. However, the strong privacy guarantee has uncovered limitations on gathering aggregated ad impression statistics for the ad network to enforce correct billing on the merchants who run their ad campaigns. Early efforts integrated cryptographic voting mechanism to address this challenge, which introduces additional bandwidth overhead on mobile devices due to the construction of the ballot proof. In this paper, we propose a private and efficient ad impression aggregation scheme in mobile advertising to protect the individual ad impression statistics while preventing the ad-fraud attack. The main idea of the proposed scheme is the design of an efficient cryptographic voting mechanism based on the compact hamming weight proof technique and additive homomorphic encryption. The proposed scheme has better bandwidth efficiency by reducing the ballot proof size from O(logN) to O(1), where N denotes the dimension of the ballot. Security analysis demonstrates the confidentiality of the individual impression statistics and the verifiability of the ballot proof under standard cryptographic assumptions. Experimental results consolidate that the proposed scheme is feasible for real-world implementations on mobile devices.

Large-scale WiFi systems have been widely deployed in an increasing number of corporate places such as universities, big malls and companies, to provide fast Internet experience to users. However, user association patterns in such large-scale systems have not been well investigated, which is crucial for performance enhancement and intelligent system management. In this paper, we provide the analytics of a large-scale campus WiFi dataset, which includes more than 8,000 access points (APs) and 40,000 active users in the area of 3.0925 km2. By conducting extensive analysis on association patterns, we achieve several key insights as follows. First, user associations are highly dynamic as short association durations and frequent AP transitions prevail throughout the whole trace. Second, even though users may associate to many APs, they generally have a small preferable AP set in which they spend most of their WiFi connection time for data traffic; in addition, each user has distinct yet relatively fixed AP transition route, indicating that given its current associated AP, its next association AP is highly predictable. Third, diurnal association patterns are observed not only at single AP level, but also at the building and the system level, where the number of associated users and the data traffic vary periodically on a daily basis. These insights can provide valuable guidelines to numerous intelligent service provisions such as proactive service migration, edge content distribution, efficient network management.

Ensuring network QoS for Internet of vehicles (IoVs) is crucial for safe and intelligent transportation system, while the vehicle density variation seems invincible for stationary base station (BS) networks. In this paper, we study IoV's downlink throughput guarantee, in which, in addition to the cellular BS resource, UAVs (equipped with WiFi interfaces) can be dynamically sent out to provide additional wireless connections. To cope with the dynamic IoV density, we propose an Online UAV Scheduling scheme, referred to as OUS, to online schedule and manage UAVs to guarantee seamless connections with reliable throughput performance. In OUS, we first use the complementary cumulative distribution function (CCDF) of IoV throughput to calculate the likelihood of a channel resource shortage. If a shortage condition is imminent and then minimal UAVs will be sent out to their optimal hovering positions. In particular, we revealed the marginal effect for the optimal hovering position acquisition, i.e., the further the UAV is away from the BS, the larger throughput gain can be achieved by the system. We conduct extensive simulations to evaluate the performance of our OUS scheme, and results demonstrate that it can well react to the throughput QoS demand by intelligently sending out minimal UAVs, and its hovering position acquisition method can fully utilize the efficacy of UAVs.

Service-oriented virtual network deployment is based on statistical resource demands of different services, while data traffic from each service fluctuates over time. In this paper, a delay-aware flow migration problem for embedded services is studied to meet end-to-end (E2E) delay requirement with time-varying traffic. A non-convex multi-objective mixed integer optimization problem is formulated, addressing the trade-off between maximum load balancing and minimum reconfiguration overhead due to flow migrations, under processing and transmission resource constraints and QoS requirement constraints. Since the original problem is non-solvable in optimization solvers due to unsupported types of quadratic constraints, it is transformed to a tractable mixed integer quadratically constrained programming (MIQCP) problem. The optimality gap between the two problems is proved to be zero, so we can obtain the optimum of the original problem through solving the MIQCP problem with some post-processing. Numerical results are presented to demonstrate the aforementioned trade-off, as well as the benefit from flow migration in terms of E2E delay performance guarantee.

With the increasing network traffic volume in the big data era, enterprises have paid significant attentions on outsourcing middlebox services to the public cloud. While offering appealing benefits, including network resource scalability and management cost reduction, it also raises severe privacy and security issues, such as the exposure of packet payload and middlebox rules. Since the traffic is redirected to cloud server, the exposure of packet payload and middlebox rule becomes inevitable. Simply encrypting the traffic can mitigate this problem at the cost of sacrificing data utility, which poses great challenges on deep packet inspection. In this paper, an efficient and secure Deep Packet Inspection (DPI) scheme is proposed based on non-collusion two cloud servers to enable data utility, while protecting the packet payload and middlebox rules. We leverage encrypted Matryoshka filter and T-set to process DPI. Since both the middlebox rule and packet payload are encrypted, cloud server cannot breach the confidentiality of them. We also build a secure hash chain to prevent the leakage of token order information. Extensive experiments demonstrate that proposed scheme performances better in terms of packet processing, rule preparation and rule matching.

Drone cell (DC) is an emerging technique to offer flexible and cost-effective wireless connections to collect Internet-of-things (IoT) data in uncovered areas of terrestrial networks. The flying trajectory of DC significantly impacts the data collection performance. However, designing the trajectory is a challenging issue due to the complicated 3D mobility of DC, unique DC-to-ground (D2G) channel features, limited DC-to-BS (D2B) backhaul link quality, etc. In this paper, we propose a 3D DC trajectory design for the DC-assisted IoT data collection where multiple DCs periodically fly over IoT devices and relay the IoT data to the base stations (BSs). The trajectory design is formulated as a mixed integer non-linear programming (MINLP) problem to minimize the average user-to-DC (U2D) pathloss, considering the state-of-the-art practical D2G channel model. We decouple the MINLP problem into multiple quasi-convex or integer linear programming (ILP) sub-problems, which optimizes the user association, user scheduling, horizontal trajectories and DC flying altitudes of DCs, respectively. Then, a 3D multi-DC trajectory design algorithm is developed to solve the MINLP problem, in which the sub-problems are optimized iteratively through the block coordinate descent (BCD) method. Compared with the static DC deployment, the proposed trajectory design can lower the average U2D pathloss by 10-15 dB, and reduce the standard deviation of U2D pathloss by 56%, which indicates the improvements in both link quality and user fairness.

In this paper, we propose a cooperation framework in heterogeneous cloud radio access networks (H-CRANs) to mitigate inter-tier interference. Specifically, small cell remote radio head (S-RRH) acts as the cognitive relay for multiple macrocell users (MUEs) which are primary users, and obtains a fraction of time slot from multiple MUEs as a reward. Through the cooperation, the S-RRHs can obtain extra spectrum resource for serving secondary users-small cell users (SUEs), while the MUEs can improve their transmission rates. Moreover, the inter-tier interference between macrocell networks and small cell networks can be mitigated via cooperation. The cooperation problem is formulated as a binary integer programming problem which is NP-hard. To solve this problem, we transform it to an equivalent many-to-one matching problem. Then, we achieve the near optimal solution by proposing a two-sided cooperator selection algorithm, which takes the benefits of both S-RRHs and MUEs into consideration. Simulation results show that the performance of the macrocell networks as well as small cell networks can be improved by adopting the proposed scheme, and the cooperator selection result is stable and close to the optimal solution.

To combat the pilot spoofing attack in non-orthogonal multiple access (NOMA) systems, we propose a double channel training scheme in this paper. Specifically, we consider two users in each cluster and both users send the training sequence in the first uplink training phase, while one of them keeps silent in the second phase. By exploiting channel estimation results in the two phases, more accurate legitimate channel estimation can be obtained by removing the contamination from the eavesdropping channel. Thus, the pilot spoofing attack can be mitigated effectively. We then analyze the achievable downlink secrecy rate with matched filter precoding scheme. Simulation results demonstrate that the achievable secrecy rate can be improved dramatically with the proposed scheme even under very strong pilot attack power.

The energy consumption for wireless interface is important for the power-constraint mobile and sensor devices. To improve energy efficiency in WLAN (such as Wi-Fi), power saving mode (PSM) is proposed, with an attempt to manage the time spent in idle listening (IL) state. The challenge is that the receiver has no knowledge about when the pending data will arrival under end-to-end communication protocols (TCP/IP); therefore each station has to spend more time in IL to wait for the pending data. To address this problem, we propose NDN-PSM, in which NDN communication architecture is leveraged to cut down unnecessary IL time. In particular, we introduce two new power states in NDN-PSM, i.e., light doze and deep doze. As stations can check pending interest table (PIT) information to predict data arrival precisely, they can switch to deep doze or light doze intelligently. The inherent receiver-driven patterns of NDN can make each station effectively go to deep doze state for power saving. We have implemented NDN-PSM in NS-3 through ndnSIM and the simulation results demonstrate that NDN-PSM can effectively reduce IL time as well as total power consumption and meanwhile retain low transmission delay. Specifically, compared to the PSM mechanism, NDN-PSM can reduce the average power consumption up to 56%.

Mobile edge computing extends computing and storage resources to the proximity of mobile users, facilitating a number of innovative mobile applications. Particularly, video streaming is the most prevailing one that consumes substantial edge resources. In this paper, we investigate the multi-dimensional resource allocation for video service provisioning, with the objective of ensuring satisfied streaming experience at high resource utilization. Considering the diversified and constantly changing user preferences on the quality of video contents, the edge resource allocation process is modeled as a long-term utility maximization problem. To address this problem, we propose an online learning algorithm that actively estimates user preferences according to regression analysis on user feedback. This algorithm requires no training phase, and hence is adaptive to dynamic user interests and available edge resources. Both theoretical analysis and numerical results demonstrate that the performance of the proposed algorithm asymptotically approaches the hindsight optimal resource allocation strategy.

It is common to certify when a file was created in digital investigations, e.g., determining first inventors for patentable ideas in intellectual property systems to resolve disputes. Secure time-stamping schemes can be derived from blockchain-based storage to protect files from backdating/forward-dating, where a file is integrated into a transaction on a blockchain and the timestamp of the corresponding block reflects the latest time the file was created. Nevertheless, blocks' timestamps in blockchains suffer from time errors, which causes the inaccuracy of files' timestamps. In this paper, we propose an accurate blockchain-based time-stamping scheme called Chronos. In Chronos, when a file is created, the file and a sufficient number of successive blocks that are latest confirmed on blockchain are integrated into a transaction. Due to chain quality, it is computationally infeasible to pre-compute these blocks. The time when the last block was chained to the blockchain serves as the earliest creation time of the file. The time when the block including the transaction was chained indicates the latest creation time of the file. Therefore, Chronos makes the file's creation time corresponding to this time interval. Based on chain growth, Chronos derives the time when these two blocks were chained from their heights on the blockchain, which ensures the accuracy of the file's timestamp. The security and performance of Chronos are demonstrated by a comprehensive evaluation.

By exploiting the storage resources of end devices, local caching becomes a promising approach to reduce the latency and improve the throughput of content delivery. Considering battery constraints at end devices, this paper investigates energy-aware caching policy to minimize the power consumed in content delivery, taking into account the heterogeneity of interests and sharing willingness of different MSs. Specifically, MSs are divided into disjoint groups based on the preferences and sharing willingness, and each group customizes the caching policy accordingly. Both the non-coordinated and coordinated caching scenarios are considered. For the non-coordinated case, the problem is formulated as an optimization problem, which is proved to be concave and solved by Lagrange methods. For the coordinated caching case, a water-filling based iterative algorithm is proposed to get the optimal caching policies. Numerical results demonstrate that the designed caching policies can reduce the system energy consumption by around 10% - 20% comparing to the conventional ones without considering the heterogeneity of users.

How to deploy cache in large-scale WiFi system is not well studied yet quite challenging since numerous Aps turn to be heterogeneous in terms of traffic consumption, and future traffic conditions are unknown ahead. In this paper, given the cache storage budge, we explore the cache deployment in a large-scale WiFi system which contains 8,000 APs and serves more than 40,000 active users, to maximize the long-term caching gain, i.e., the total reduced backhaul traffic. Specifically, we first collect enormous user association records and conduct intensive statistical analysis on the collected data, gaining two major observations. First, per AP traffic consumption varies in a rather wide range and the AP proportion distributes evenly within the range, which indicates that the cache size should be heterogeneously allocated in accordance to the underlying traffic demands. Second, compared to a single AP, the traffic consumption of a group of APs (clustered by physical locations) is more stable, which means that the short-term traffic statistics can be used to infer the future long-term traffic conditions. We then propose our cache deployment strategy, named LEAD (i.e., Large-scale wifi Edge cAche Deployment), in which we first cluster large-scale APs into well-sized edge nodes, then conduct the stationary testing on edge level traffic consumption and sample sufficient traffic statistics in order to precisely characterize future traffic conditions, and finally devise the TEG (Traffic-wEighted Greedy) algorithm to solve the long-term caching gain maximization problem. Extensive trace-driven simulations are carried out and simulation results demonstrate the efficacy of LEAD.

Secure task-driven data sharing can improve the sensing data usage and protect data confidentiality in mobile crowdsensing (MCS). However, the existing data sharing schemes lack efficient support of forward secrecy, i.e., if the secret key of a data requester is compromised, all the historically shared data will be leaked. In this paper, we propose a forward secure and fine-grained data sharing scheme in mobile crowdsensing to provide a strong security guarantee and flexible access control over the sensing data. Specifically, by incorporating puncturable encryption and attribute based encryption, a shared symmetric key for data sharing can be encrypted by an access structure over the attributes of data requesters and the introduced Bloom filter attributes. Moreover, the shared key establishment between the MCS server and data requesters can be done jointly with the both sides authentication. By utilizing the structure of the Bloom filter, the update of a private key which is used to achieve forward secrecy only needs several deletion operations and no communication with the key distributor is involved. The security proof shows our scheme is provably secure under the security model. Experiment results demonstrate the practicability of the scheme.

Vehicular platooning communication has been widely recognized as a promising traffic management technique for connected vehicles to improve the traffic capacity, on-road safety, and energy efficiency, etc. However, due to the high mobility and dense vehicular communication scenario, how to improve the radio resource efficiency of vehicular platooning communications is a challenge issue. In addition, considering the broadcast nature of vehicular communications, vulnerable vehicular platooning communications could be exposed to potential eavesdroppers, which would be a hidden danger for connected vehicle applications. In this paper, we investigate the secure radio resource sharing problem in device-to-device (D2D) enabled multiplatooning vehicular communications. Based on the physical layer security theory, we propose a security aware joint channel and power allocation (SA-JCPA) scheme, in which the closed-form expression for power control is derived through an algebraic method and the channel sharing strategy is obtained by leveraging a maximum weight bipartite graph matching approach. Through extensive simulations, it is demonstrated that our proposed SA- JCPA scheme can achieve efficient and secure radio resource utilization.

Spectrally efficient frequency division multiplexing (SEFDM) is a promising non-orthogonal multi-carrier technique to improve spectral efficiency, by compressing the inter-carrier interval relative to orthogonal frequency division multiplexing (OFDM) systems. However, by breaking the orthogonality among subcarriers, the self- introduced inter-carrier interference (ICI) severely restrains the achievable transmission rate and poses great challenges in designing the receiver with ICI cancellation. In this paper, we first characterize the statistical distribution of ICI and then derive a closed-form expression of the signal-to- interference-plus-noise ratio (SINR). After that, an efficient time-domain ICI mitigation approach is proposed to improve the achievable SINR and the spectral efficiency of the SEFDM system. Numerical results verify the analytical expressions for the cumulative distribution function (CDF) of ICI and the achievable SINR. In addition, it is shown that the spectral efficiency can be significantly improved by adopting our proposed ICI mitigation approach.

Internet of Things (IoT) computing offloading is a challenging issue, especially in remote areas where common edge/cloud infrastructure is unavailable. In this paper, we present a space-air-ground integrated network (SAGIN) edge/cloud computing architecture for offloading the computation-intensive applications considering remote energy and computation constraints, where flying unmanned aerial vehicles (UAVs) provide near-user edge computing and satellites provide access to the cloud computing. First, for UAV edge servers, we propose a joint resource allocation and task scheduling approach to efficiently allocate the computing resources to virtual machines (VMs) and schedule the offloaded tasks. Second, we investigate the computing offloading problem in SAGIN and propose a learning-based approach to learn the optimal offloading policy from the dynamic SAGIN environments. Specifically, we formulate the offloading decision making as a Markov decision process where the system state considers the network dynamics. To cope with the system dynamics and complexity, we propose a deep reinforcement learning-based computing offloading approach to learn the optimal offloading policy on-the-fly, where we adopt the policy gradient method to handle the large action space and actor-critic method to accelerate the learning process. Simulation results show that the proposed edge VM allocation and task scheduling approach can achieve near-optimal performance with very low complexity and the proposed learning-based computing offloading algorithm not only converges fast but also achieves a lower total cost compared with other offloading approaches.

Recently, edge caching and multicasting arise as two promising technologies to support high-data-rate and low-latency delivery in wireless communication networks. In this paper, we design three transmission schemes aiming to minimize the delivery latency for cache-enabled multigroup multicasting networks. In particular, full caching bulk transmission scheme is first designed as a performance benchmark for the ideal situation where the caching capability of each enhanced remote radio head (eRRH) is sufficient large to cache all files. For the practical situation where the caching capability of each eRRH is limited, we further design two transmission schemes, namely partial caching bulk transmission (PCBT) and partial caching pipelined transmission (PCPT) schemes. In the PCBT scheme, eRRHs first fetch the uncached requested files from the baseband unit (BBU) and then all requested files are simultaneously transmitted to the users. In the PCPT scheme, eRRHs first transmit the cached requested files while fetching the uncached requested files from the BBU. Then, the remaining cached requested files and fetched uncached requested files are simultaneously transmitted to the users. The design goal of the three transmission schemes is to minimize the delivery latency, subject to some practical constraints. Efficient algorithms are developed for the low-latency cloud-edge coordinated transmission strategies. Numerical results are provided to evaluate the performance of the proposed transmission schemes and show that the PCPT scheme outperforms the PCBT scheme in terms of the delivery latency criterion.

Service composition is popular for composing a set of existing services to provide complex services. With the increasing number of services deployed in cloud computing environments, many service providers have started to offer candidate services with equivalent functionality but different Quality of Service (QoS) levels. Therefore, QoS-aware service composition has drawn extensive attention. Most existing approaches for QoS-aware service composition assume a service's QoS values are not correlated to those of other services. However, QoS dependency exists in real life, and impacts the overall QoS values of the composite services. In this article, we study QoS dependency-aware service composition considering multiple QoS attributes. Based on the Pareto set model, we focus on searching for a set of Pareto optimal solutions. A candidate pruning algorithm for removing the unpromising candidates is proposed, and a service composition algorithm using Vector Ordinal Optimization techniques is designed. Simulation experiments are conducted to validate the efficiency and effectiveness of our algorithms. We are the first to take advantage of Vector Ordinal Optimization techniques to search for Pareto optimal composition solutions with QoS dependency involved. The capturing of QoS dependency enables us to find truly desirable solutions.

The emerging ad hoc clouds form a new cloud computing paradigm by leveraging untapped local computation and storage resources. An important application of ad hoc clouds is to outsource computational intensive problems to nearby cloud agents. Specifically, for the problem of solving a linear algebraic equation (LAE), an outsourcing client assigns each cloud agent a subproblem, and then all involved agents apply a consensus-based algorithm to obtain the correct solution of the LAE in an iterative and distributed manner. However, such a distributed collaboration paradigm suffers from cyber security threats that undermine the confidentiality of the outsourced problem and the integrity of the returned results. In this paper, we identify a number of such security threats in this process, and propose a secure outsourcing scheme which not only preserves the privacy of the LAE parameters and the final solution from the participating agents, but also guarantees the correctness of the final solution. We prove that the proposed scheme has low computation complexity at each agent, and is robust against the identified security attacks. Numerical and simulation results are presented to demonstrate the effectiveness of the proposed method.

Industrial Internet of Things (IIoT) is revolutionizing the retail industry for manufacturers, suppliers, and retailers to improve operational efficiency and consumer experience. In IIoT-enabled retail marketing, reputation systems play a critical role to boost mutual trust among industrial entities and build consumer confidence. In this paper, we focus on reputation management in the consumer–retailer channel, where retailers can accumulate reputations from consumer feedbacks. To encourage consumers to post feedbacks without worrying about being tracked or retaliated, we propose an anonymous reputation system that preserves consumer identities and individual review confidentialities. To increase system transparency and reliability, we further exploit the tamper-proof nature and the distributed consensus mechanism of the blockchain technology. With system designs based on various cryptographic primitives and a Proof-of-Stake consensus protocol, our blockchain-based reputation system is more efficient to offer high levels of privacy guarantees compared with existing ones. Finally, we explore the implementation challenges of the blockchain-based architecture and present a proof-of-concept prototype system by Parity Ethereum. We measure the on/off -chain performance with the scalability discussion to demonstrate the feasibility of the proposed system.

Pervasive data collected from e-healthcare devices possess significant medical value through data sharing with professional healthcare service providers. However, health data sharing poses several security issues, such as access control and privacy leakage, as well as faces critical challenges to obtain efficient data analysis and services. In this article, we propose an efficient and privacy-preserving fog-assisted health data sharing (PFHDS) scheme for e-healthcare systems. Specifically, we integrate the fog node to classify the shared data into different categories according to disease risks for efficient health data analysis. Meanwhile, we design an enhanced attribute-based encryption method through combination of a personal access policy on patients and a professional access policy on the fog node for effective medical service provision. Furthermore, we achieve significant encryption consumption reduction for patients by offloading a portion of the computation and storage burden from patients to the fog node. Security discussions show that PFHDS realizes data confidentiality and fine-grained access control with collusion resistance. Performance evaluations demonstrate cost-efficient encryption computation, storage and energy consumption.

In vehicular sensor networks, vehicles can act as mobile sensors to monitor the dynamic features of the physical world such as traffic flow, air quality, and temperature. However, the conventional full-coverage sensing approach is neither realizable nor cost-effective since the sensor-equipped vehicles are unevenly distributed and the environmental data are spatio-temporally correlated. To this end, we propose a cost-effective urban environment sensing solution (CESense), that exploits the sensing data correlations to improve the sensing accuracy and efficiency. CESense gathers data only at some specific areas of the whole sensing space and reliably infers the status of unsensed areas. Particularly, CESense uses a probabilistic matrix factorization model to reveal the latent features that impact the environmental status. Then, an appropriate set of sensing areas can be selected by fully taking advantage of these latent features and the sensing resource distribution patterns. In addition, to be adaptive to the dynamic environment, a checkpoint mechanism is designed to supervise the data gathering progress. Extensive experiments, which are based on the real taxicab mobility traces and air quality data collected in Beijing city, demonstrate that CESense can significantly improve the accuracy and efficiency of vehicular sensing.

In Body Area Networks (BANs), bio-sensors can collect personal health information and cooperate with each other to provide intelligent health care services for medical users. Since personal health information is highly privacy-sensitive, the flourish of BANs still faces critical security challenges, especially secure communication between bio-sensors. In this paper, we propose a flexible and efficient authenticated key agreement scheme (PBAKA) to provide secure communication for BANs. Specifically, we employ a control unit (e.g., smart phone) to launch authentication based on physiological features collected from BANs, and integrate bilinear pairings to negotiate session keys for bio-sensors. Since physiological features can be collected from various kinds of bio-sensors in real time, PBAKA is flexible for adding new bio-sensors without pre-distributed keys. Meanwhile, PBAKA is computationally efficient by offloading authentication burden from resource-limited bio-sensors to the control unit. Security analysis demonstrates that PBAKA is provably secure under the decisional bilinear Diffie-Hellman assumption. Extensive experimental results validate efficient communication, computation and energy consumption of our scheme when compared with several existing solutions.

Crowdsourcing is a promising platform, whereby massive tasks are broadcasted to a crowd of semi-skilled workers by the requester for reliable solutions. In this paper, we consider four key evaluation indices of a crowdsourcing community (i.e., quality, cost, latency, and platform improvement), and demonstrate that these indices involve the interests of the three stakeholders, namely the requester, worker, and crowdsourcing platform. Since the incentives among these three stakeholders always conflict with each other, to elevate the long-term development of the crowdsourcing community, we take the perspective of the whole crowdsourcing community, and design a crowdsourcing mechanism to align incentives of stakeholders together. Specifically, we give workers reward or penalty according to their reporting solutions instead of only nonnegative payment. Furthermore, we find a series of proper reward-penalty function pairs and compute workers personal order values, which can provide different amounts of reward and penalty according to both the workers reporting beliefs and their individual history performances, and keep the incentive of workers at the same time. The proposed mechanism can help latency control, promote quality and platform evolution of crowdsourcing community, and improve the aforementioned four key evaluation indices. Theoretical analysis and experimental results are provided to validate and evaluate the proposed mechanism, respectively.

Driven by the growing popularity of mobile applications, mobile cloud computing has been envisioned as a promising approach to enhance computation capability of mobile devices and reduce the energy consumptions. In this paper, we investigate the problem of multi-user computation offloading for mobile cloud computing under dynamic environment, wherein mobile users become active or inactive dynamically, and the wireless channels for mobile users to offload computation vary randomly. As mobile users are self-interested and selfish in offloading computation tasks to the mobile cloud, we formulate the mobile users' offloading decision process under dynamic environment as a stochastic game. We prove that the formulated stochastic game is equivalent to a weighted potential game which has at least one Nash Equilibrium (NE). We quantify the efficiency of the NE, and further propose a multi-agent stochastic learning algorithm to reach the NE with a guaranteed convergence rate (which is also analytically derived). Finally, we conduct simulations to validate the effectiveness of the proposed algorithm and evaluate its performance under dynamic environment.

Mobile edge caching aims to enable content delivery within the radio access network, which effectively alleviates the backhaul burden and reduces response time. To fully exploit edge storage resources, the most popular contents should be identified and cached. Observing that user demands on certain contents vary greatly at different locations, this paper devises location-customized caching schemes to maximize the total content hit rate. Specifically, a linear model is used to estimate the future content hit rate. For the case with zero-mean noise, a ridge regression-based online algorithm with positive perturbation is proposed. Regret analysis indicates that the hit rate achieved by the proposed algorithm asymptotically approaches that of the optimal caching strategy in the long run. When the noise structure is unknown, an$H_{\infty }$filter-based online algorithm is devised by taking a prescribed threshold as input, which guarantees prediction accuracy even under the worst-case noise process. Both online algorithms require no training phases and, hence, are robust to the time-varying user demands. The estimation errors of both algorithms are numerically analyzed. Moreover, extensive experiments using real-world datasets are conducted to validate the applicability of the proposed algorithms. It is demonstrated that those algorithms can be applied to scenarios with different noise features, and are able to make adaptive caching decisions, achieving a content hit rate that is comparable to that via the hindsight optimal strategy.

This two-part paper investigates cache replacement schemes with the objective of developing a general model to unify the analysis of various replacement schemes and illustrate their features. To achieve this goal, we study the dynamic process of caching in the vector space and introduce the concept of state transition field (STF) to model and characterize replacement schemes. In the first part of this work, we consider the case of time-invariant content popularity based on the independent reference model (IRM). In such case, we demonstrate that the resulting STFs are static, and each replacement scheme leads to a unique STF. The STF determines the expected trace of the dynamic change in the cache state distribution, as a result of content requests and replacements, from any initial point. Moreover, given the replacement scheme, the STF is only determined by the content popularity. Using four example schemes including random replacement (RR) and least recently used (LRU), we show that the STF can be used to analyze replacement schemes such as finding their steady states, highlighting their differences, and revealing insights regarding the impact of knowledge of content popularity. Based on the above results, STF is shown to be useful for characterizing and illustrating replacement schemes. Extensive numeric results are presented to demonstrate analytical STFs and STFs from simulations for the considered example replacement schemes.

In the second part of this two-part paper, we extend the study of dynamic caching via state transition field (STF) to the case of time-varying content popularity. The objective of this part is to investigate the impact of time-varying content popularity on the STF and how such impact accumulates to affect the performance of a replacement scheme. Unlike the case in the first part, the STF is no longer static over time, and we introduce instantaneous STF to model it. Moreover, we demonstrate that many metrics, such as instantaneous state caching probability and average cache hit probability over an arbitrary sequence of requests, can be found using the instantaneous STF. As a steady state may not exist under time-varying content popularity, we characterize the performance of replacement schemes based on how the instantaneous STF of a replacement scheme after a content request impacts on its cache hit probability at the next request. From this characterization, insights regarding the relations between the pattern of change in the content popularity, the knowledge of content popularity exploited by the replacement schemes, and the effectiveness of these schemes under time-varying popularity are revealed. In the simulations, different patterns of time-varying popularity, including the shot noise model, are experimented. The effectiveness of example replacement schemes under time-varying popularity is demonstrated, and the numerical results support the observations from the analytic results.

To satisfy the high requirements on operation efficiency in the 5G network, self-organizing network (SON) is envisioned to reduce the network operating complexity and costs by providing SON functions, which can optimize the network autonomously. However, different SON functions have different time scales and inconsistent objectives, which leads to conflicting operations and network performance degradation, raising the needs for SON coordination solutions. In this paper, we devise a multiple time-scale coordination management scheme (MTCS) for densely deployed SONs, considering the specific time scales of different SON functions. Specifically, we propose a novel analytical model named M time-scale Markov decision process, where SON decisions made in each time-scale consider the impacts of SON decisions in other M - 1 time scales on the network. Furthermore, in order to manage the network more autonomously and efficiently, a Q-learning algorithm for SON functions in the proposed MTCS scheme is proposed to achieve a stable control policy by learning from history experience. To improve energy efficiency, we then evaluate the proposed MTCS scheme with two functions of mobility load balancing and energy saving management with designed network utility. The simulation results show that the proposed SON coordination scheme significantly improves the network utility with different quality of experience requirements while guaranteeing stable operations in wireless networks.

Beam alignment (BA) is to ensure the transmitter and receiver beams are accurately aligned to establish a reliable communication link in millimeter-wave (mmwave) systems. Existing BA methods search the entire beam space to identify the optimal transmit-receive beam pair, which incurs significant BA latency on the order of seconds in the worst case. In this paper, we develop a learning algorithm to reduce BA latency, namely Hierarchical Beam Alignment (HBA) algorithm. We first formulate the BA problem as a stochastic multi-armed bandit problem with the objective to maximize the cumulative received signal strength within a certain period. The proposed algorithm takes advantage of the correlation structure among beams such that the information from nearby beams is extracted to identify the optimal beam, instead of searching the entire beam space. Furthermore, the prior knowledge on the channel fluctuation is incorporated in the proposed algorithm to further accelerate the BA process. Theoretical analysis indicates that the proposed algorithm is asymptotically optimal. Extensive simulation results demonstrate that the proposed algorithm can identify the optimal beam with a high probability and reduce the BA latency from hundreds of milliseconds to a few milliseconds in the multipath channel, as compared to the existing BA method in IEEE 802.11ad.