With the rapid advancement of ubiquitous connectivity, the Internet of Unmanned Agents (IUA) has emerged as a promising paradigm for distributed intelligent perception and decision-making. In such systems, numerous unmanned agents collaboratively collect environmental data to support coordinated tasks. To preserve data privacy, Federated Learning (FL), as a decentralized machine learning framework, enables collaborative training of a global model across agents without directly exchanging raw data. However, FL faces several critical challenges in IUA environments, including high communication overhead under constrained wireless bandwidth, potential privacy leakage from shared model parameters, and agent dropouts caused by unstable connectivity. To address these challenges, we propose PPE-FL, a privacy-preserving and communication-efficient federated learning scheme designed for IUA scenarios. PPE-FL replaces conventional high-dimensional gradient uploads with lightweight ranking-based votes, substantially reducing communication overhead. Then, we design an obfuscation mechanism to protect the privacy of locally generated ranking-based votes, safeguarding both raw data and intermediate parameters. Furthermore, PPE-FL supports mask reconstruction through partial interactions among online unmanned agents, enabling robust aggregation under dynamic network conditions. Experimental results demonstrate that the proposed PPE-FL reduces communication costs by 89.5% compared to VCD-FL and by 87.7% compared to PPML, while maintaining model accuracy and privacy protection.

To address the challenges of reliable and efficient short-packet communication in the Internet of Things (IoT), especially under ultra-reliable low-latency communication (URLLC) constraints, this work optimizes the design of short polar codes tailored for low-power IoT devices. To improve the reliability of short polar codes under successive cancellation list (SCL) decoders, we introduce a novel heuristic optimization algorithm guided by a unified metric. This algorithm carefully balances the tradeoff between the number of minimum-weight codewords (a.k.aerror coefficient) and the reliability of selected information subchannels. Through a guided and deliberate disruption of the partial order property of polar codes, our algorithm reduces the error coefficient to enhance maximum likelihood (ML) decoding performance, while managing the impact on subchannel reliability. Numerical results demonstrate a consistent and significant improvement over the baseline RM-Polar and Gaussian Approximation (GA) constructions across various code parameters. Furthermore, our approach features low offline design complexity, achieving state-of-the-art or highly competitive performance against other advanced schemes, particularly at low code rates. This makes our method highly suitable for URLLC, as the resulting optimized codes can be deployed on existing 5G hardware with zero additional on-device decoding complexity, while the achievable coding gain directly translates into transmission energy savings.

Integrated Sensing, Communication, and Computation (ISCC) has the potential to meet diverse requirements of Internet of Vehicles (IoV), such as high reliability and low power consumption. However, existing works have not fully considered the problems of unreliable communication links and inefficient data processing under resource constraints in non-ideal environments. To address these issues, this paper proposes a predictive Integrated Sensing, Communication and Computation Over-the-Air (ISCCO) approach based on Orthogonal Time Frequency Space (OTFS) modulation. It takes high Doppler shifts, network dynamics, and resource constraints into account. In particular, the Road Side Unit (RSU) performs target tracking while communicating with the downlink users through Space Division Multiplexing (SDM), and receives the transmission results of the uplink. For the downlink, a predictive beamforming approach based on Extended Kalman Filtering (EKF) is employed, while Over-the-Air computation (AirComp) is utilized for the uplink. The transmit power and receive beamformer at the RSU, along with the transmit power of the uplink users, are jointly optimized through two formulated optimization problems: sensing performance maximization and power consumption minimization. To solve these problems, we adopt an Alternating Optimization (AO)-based algorithm for finding the local optimal solution. Simulation results validate the effectiveness of the AO-based algorithm, and the analysis of the trade-offs between multi-dimensional performance of ISCC and power consumption is conducted.

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.

Certificate management is needed for securing certificates which has been widely deployed in satellite networks to support security-related services. However, directly utilizing existing certificate management mechanisms in satellite networks would cause critical issues in terms of security, privacy, and practicality. Typically, the trustworthiness of certificate revocation checking (CRC) cannot be guaranteed in the presence of active adversaries; The certificate to be checked contains the satellite’s identity, which is sensitive in some applications but could be exposed during CRC; CRC cannot be trivially launched when the satellite is under constrained networks (e.g., it enters dead zones where direct communication with base stations fails).Worse still, compromising the secret key of a single certificate authority (CA) leads to certificate forgery. In this paper, we propose a forward-secure and privacy-preserving certificate management scheme, dubbed SNCM, for satellite networks, where a forward-secure signature algorithm is used to issue certificates. We utilize a neighboring-assisted forwarding paradigm in SNCM to support CRC in constrained networks. SNCM is secure against adversaries who invalidate CRC results or violate related sensitive information about the satellite, which is achieved by utilizing authenticated encryption with associated data (AEAD). Furthermore, SNCM utilizes a 2-layer revocation checking protocol to perform lightweight CRC, where the CA and base stations handle CRC tasks from satellites in a cooperative way, which frees the CA from heavy costs and reduces CRC delay significantly. We analyze the security of SNCM, implement an SNCM prototype, and conduct a comprehensive performance evaluation, which demonstrates its security, efficiency, and practicality.

Exchange-assisted cloud-based data trading (ECDT) is a promising paradigm in current marketplaces, where an exchange provides underlying trading services while the cloud serves as a fundamental base for data sellers, brokers, and data buyers to enable them to benefit from data trading. However, directly integrating existing commercial cloud services into an exchange system suffers from practicality issues. In existing ECDT systems, the data outsourced to the cloud generally follows an “encrypt-then-outsource” paradigm, and the encrypted database makes it impractical for brokers to generate and deliver on-demand data products to the buyer, thereby hindering subscription-based data trading. In this paper, we propose a secure and efficient data delivery scheme, dubbed ESECDT, for subscription-based ECDT. ESECDT consists of data entrustment and data delivery and supports continuous data entrustment and customized data delivery while freeing the broker from heavy costs in terms of computation and communication. We formally define and prove the security of ESECDT in the random oracle model. We also implement an ESECDT prototype and conduct a comprehensive performance evaluation, which demonstrates the efficiency and practicality of ESECDT.

Satellite communications have been considered a key part of global connectivity, effectively supporting diverse applications such as the Internet of Things (IoT) and real-time communication services. However, security-sensitive devices face significant challenges due to the threat of eavesdropping satellites, which compromise data confidentiality. Existing approaches often rely on deterministic models and fail to account for the stochastic nature of eavesdropping threats and the dynamic demands of satellite networks, limiting their applicability in practical scenarios. To address these challenges, this work proposes a novel secure access strategy for satellite mobile edge computing (SatMEC) systems, integrating a stochastic risk assessment model and an evolutionary game-theoretic framework. The proposed solution leverages a probabilistic model to evaluate the spatial distribution of eavesdropping satellites, quantifies the eavesdropping risk via the concept of eavesdropping capacity, and incorporates a dynamic service selection strategy that balances secrecy capacity and queuing delay.Furthermore, a distributed algorithm is developed to enable IoT devices to select service satellites based on real-time utility optimization adaptively. Extensive simulation experiments validate the effectiveness of the proposed strategy, demonstrating its ability to improve system security, balance the network load, and enhance overall performance in large-scale and dynamic satellite network environments. The results highlight the reliability and scalability of the proposed solution, making it a practical approach for secure and efficient access in LEO satellite networks.

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.

This paper presents a mobile computing-based framework for distributed computing and cooperative control of connected and automated vehicles (CAVs) in ramp merging scenarios under intelligent transportation systems (ITS). A centralized trajectory planning problem is first formulated to optimize merging efficiency and safety. To eliminate reliance on a central controller, a distributed solution is developed using ADMM algorithm based on V2X communication, enabling CAVs to collaboratively compute trajectories in parallel by leveraging their onboard computing power. Building on this, a multi-vehicle model predictive control (MPC) problem is proposed to enhance system stability under strict constraints. To solve it efficiently, a Distributed Cooperative Iterative MPC (DCIMPC) method is introduced, which decomposes and reformulates the problem for real-time distributed execution across CAVs. Together, these methods form a mobile edge computing-driven control framework. Simulations and experiments demonstrate significant improvements in computational efficiency and system performance, highlighting the potential of mobile computing in cooperative CAV control.

This paper investigates a multi-Unmanned Aerial Vehicle (UAV) joint base station-assisted Internet of Vehicles (IoV) task offloading system in dense urban environments. To minimize system delay and energy consumption under strict coupling constraints, the complex non-convex optimization problem is decoupled into a hierarchical execution framework. First, a sequential distributed optimization algorithm based on Second-Order Cone Programming (SOCP) is proposed to optimize the 3D flight trajectory of each UAV, ensuring adaptive network coverage. Second, a novel hybrid resource scheduling paradigm synergizing Deep Reinforcement Learning (DRL) and Large Language Models (LLMs) is developed. Within this framework, the DRL agent dictates the initial resource allocation, while the LLM acts as a semantic macro-scheduler to rectify long-tail allocation imbalances for failed and surplus tasks. Crucially, a reward decoupling mechanism is introduced to isolate DRL training from external LLM interventions, thereby ensuring policy convergence. Finally, the task offloading ratios are precisely determined via Linear Programming (LP) within an alternating optimization loop. Simulation results demonstrate that the proposed method significantly outperforms traditional multi-agent reinforcement learning baselines in terms of task success rate and system efficiency.

Collaborative perception can significantly enhance the perceptual capabilities of autonomous vehicles by sharing sensing information through vehicular communications. However, large-scale sharing of sensing information often results in unsustainable network loads, making it challenging to maximize perception performance with limited communication resources in complex environments. To address this challenge, we propose FullPerception, an innovative cooperative perception framework that jointly orchestrates sensing information sharing and communication resource allocation at the network level. FullPerception advocates for the sharing of semantic information (neural network features) within critical areas, i.e., blind spots. With limited communication resources, FullPerception strategically eliminates these blind spots to maximize the accumulated perception performance. We formulate this strategy as a weighted optimization problem and prove its NP-hardness. We propose a simple yet effective algorithm, Proactive Conflict-free Scheduling (PCS), which guarantees a good performance ratio by considering broader contexts. PCS is meticulously combined with recursive structure, accounting for both the overall and future contexts to determine link scheduling and resource allocation. We demonstrate that FullPerception improves perception accuracy by 20% relative to single-vehicle systems and by 10% compared to existing scheduling methods through large-scale comprehensive joint simulation experiments.

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.

In this paper, we propose a general digital twin edge computing network comprising multiple vehicles and a server. Each vehicle generates multiple computing tasks within a time slot, leading to queuing challenges when offloading tasks to the server. The study investigates task offloading strategies, queue stability, and resource allocation. Lyapunov optimization is employed to transform long-term constraints into tractable short-term decisions. To solve the resulting problem, an in-context learning approach based on large language model (LLM) is adopted, replacing the conventional multi-agent reinforcement learning (MARL) framework. Experimental results demonstrate that the LLM-based method achieves comparable or even superior performance to MARL.

In this paper, we address the problem of fair access and Age of Information (AoI) optimization in 5G New Radio (NR) Vehicle to Everything (V2X) Mode 2. Specifically, vehicles need to exchange information with the road side unit (RSU). However, due to the varying vehicle speeds leading to different communication durations, the amount of data exchanged between different vehicles and the RSU may vary. This may poses significant safety risks in high-speed environments. To address this, we define a fairness index through tuning the selection window of different vehicles and consider the image semantic communication system to reduce latency. However, adjusting the selection window may affect the communication time, thereby impacting the AoI. Moreover, considering the re-evaluation mechanism in 5G NR, which helps reduce resource collisions, it may lead to an increase in AoI. We analyze the AoI using Stochastic Hybrid System (SHS) and construct a multi-objective optimization problem to achieve fair access and AoI optimization. Sequential Convex Approximation (SCA) is employed to transform the non-convex problem into a convex one, and solve it using convex optimization. We also provide a large language model (LLM) based algorithm. The scheme's effectiveness is validated through numerical simulations.

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.

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.

Integrated sensing and communication (ISAC) has emerged as a promising paradigm for next-generation mobile wireless communication systems. In this paper, we propose a novel framework for full-duplex ISAC systems that jointly incorporates dynamic tracking and robust secure beamforming. Specifically, a dual-functional radar-communication base station employs an extended Kalman filter to dynamically estimate the trajectories of mobile downlink (DL) users. To mitigate the impact of imperfect channel state information from multiple eavesdroppers, a robust beamforming strategy is devised by quantifying angular uncertainty via the Cramér–Rao bound (CRB). A total transmit power minimization problem is formulated under secrecy rate constraints for both DL and uplink (UL) communications, while simultaneously ensuring sensing accuracy through CRB and beam tracking mean squared error metrics. The optimization jointly considers the beamforming matrices, artificial noise covariance matrix, and UL power allocation strategy. To address the formulated non-convex problem, the S-procedure is employed to transform semi-infinite constraints into linear matrix inequalities. Successive convex approximation technique is then iteratively applied to obtain high-quality solutions. Extensive simulations verify the effectiveness of the proposed framework, demonstrating superiority in secrecy rate and power efficiency compared to benchmark schemes. Moreover, the results highlight the inherent trade-offs between secure communications performance and sensing accuracy, thereby offering insights into the design of future full-duplex ISAC systems.

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.

Large artificial intelligence (AI) models have demonstrated impressive performance in a wide range of fields. Despite their versatility, adapting large AI models to specific downstream applications often requires fine-tuning on decentralized and privacy-sensitive data, posing significant challenges in resource-constrained wireless networks. In this paper, we propose a novel zeroth-order federated fine-tuning framework for efficient fine-tuning large AI models to alleviate the computation, communication, and memory bottleneck issues. Specifically, to address the computation limitation on edge devices, we adopt the split learning architecture, hosting the most computation-intensive component of the large AI model on the edge server. Besides, we employ a memory-efficient zeroth-order fine-tuning algorithm to further reduce the GPU memory consumption. Furthermore, we conduct a rigorous convergence analysis to illustrate how device scheduling influences the learning performance. Based on the analysis, we formulate a global loss minimization problem that jointly optimizes device scheduling, transmit power, and receive beamforming under the average transmission latency constraint. To tackle this complex mixed-integer nonlinear programming problem, we apply Lyapunov theory to break down the long-term optimization problem into multiple subproblems, followed by designing an effective online algorithm. Simulation results show that the proposed framework can lower the GPU memory consumption by up to 84% and GPU hours by 39% compared to the baselines, while achieving comparable accuracy.

Sixth generation (6G) systems require environment-aware communication, driven by native artificial intelligence (AI) and integrated sensing and communication (ISAC). Radio maps (RMs), providing spatially continuous channel information, are key enablers. However, generating high-fidelity RM ground truth via electromagnetic (EM) simulations is computationally intensive, motivating machine learning (ML)-based RM construction. The effectiveness of these data-driven methods depends on large-scale, high-quality training data. Current public datasets often focus on single-input single-output (SISO) and limited information, such as path loss, which is insufficient for advanced multi-input multi-output (MIMO) systems requiring detailed channel state information (CSI). To address this gap, this paper presents UrbanMIMOMap, a novel large-scale urban MIMO CSI dataset generated using high-precision ray tracing. UrbanMIMOMap offers comprehensive complex CSI matrices across a dense spatial grid, going beyond traditional path loss data. This rich CSI is vital for constructing high-fidelity RMs and serves as a fundamental resource for data-driven RM generation, including deep learning. We demonstrate the dataset’s utility through baseline performance evaluations of representative ML methods for RM construction. This work provides a crucial dataset and reference for research in high-precision RM generation, MIMO spatial performance, and ML for 6G environment awareness. The code and data for this work are available at: https://github.com/UNIC-Lab/UrbanMIMOMap.

This paper investigates the secrecy performance optimization of an uplink satellite communication system exposed to dual-domain eavesdropping threats. Specifically, a cooperative relaying architecture is considered, where a ground user (GU) transmits confidential information to a target satellite (TS), assisted by an amplify-and-forward (AF) relay satellite (RS). Simultaneously, a ground-based malicious node acts as an AF relay to enhance the interception capability of a satellite eavesdropper (SE). To improve secure transmission, a secrecy rate maximization problem is formulated by jointly optimizing the transmit powers of the GU and RS, subject to quality-of-service constraints at the TS. The resulting non-convex problem is solved efficiently using a successive convex approximation algorithm. Simulation results demonstrate that the proposed cooperative relaying scheme significantly enhances secrecy performance compared to traditional non-cooperative baselines. These findings highlight the potential of cooperative multi-satellite relaying as an effective approach to securing next-generation satellite communication networks against sophisticated eavesdropping threats.

Multipath transmission offers bandwidth aggregation capabilities for mobile networks. However, path heterogeneity and user mobility often lead to increased packet out-oforder (OFO) rate, causing buffer blocking, reduced throughput, and degraded transmission quality. To mitigate the OFO effect in multipath transmission, this paper proposes LooM, a learning-based multipath scheduler. LooM is designed to optimize throughput and OFO rate, employing a learning-based scheduling strategy to achieve the optimal packet scheduling under fluctuating paths. Particularly, LooM employs singleround scheduling as its basic unit, calculating the number of OFO packets across scheduling units to dynamically set path blocking delay, thereby adjusting packet transmission order to ensure in-order delivery. Simulation results show that, compared to traditional scheduling algorithms, LooM reduces the OFO rate by 16% while maintaining high throughput and achieving a lower packet loss rate.

Certificate revocation checking (CRC) is a fundamental component for securing certificates which has been widely deployed in satellite networks to support security-related services. However, directly utilizing existing CRC mechanisms in satellite networks would cause critical issues in terms of security, privacy, and practicality. Typically, the trustworthiness of checking results cannot be guaranteed in the presence of active adversaries; the certificate to be checked contains the satellite’s identity, which is sensitive in some applications but could be exposed during CRC; CRC cannot be trivially launched when the satellite is being under constrained networks (e.g., it enters dead zones where direct communication with base stations fails). In this paper, we propose a privacy-preserving and lightweight CRC scheme, dubbed SNCRC, for satellite networks, where a neighboring-assisted forwarding paradigm is utilized to support CRC in constrained networks. SNCRC is secure against adversaries who invalidate checking results or violate related sensitive information about the satellite, which is achieved by utilizing authenticated encryption with associated data (AEAD). Furthermore, SNCRC utilizes a 2-layer revocation checking protocol to perform lightweight CRC, where the certificate authority (CA) and base stations handle CRC tasks from satellites in a cooperative way, which frees CA from heavy costs and reduces CRC delay significantly. We analyze the security of SNCRC, implement an SNCRC prototype, and conduct a comprehensive performance evaluation, which demonstrates its security, efficiency, and practicality.

We introduce a novel Multi-modal Guided Real-World Face Restoration (MGFR) technique designed to improve the quality of facial image restoration from low-quality inputs. Leveraging a blend of attribute text prompts, high-quality reference images, and identity information, MGFR can mitigate the generation of false facial attributes and identities often associated with generative face restoration methods. By incorporating a dual-control adapter and a two-stage training strategy, our method effectively utilizes multi-modal prior information for targeted restoration tasks. We also present the Reface-HQ dataset, comprising over 21,000 high-resolution facial images across 4800 identities, to address the need for reference face training images. Our approach achieves superior visual quality in restoring facial details under severe degradation and allows for controlled restoration processes, enhancing the accuracy of identity preservation and attribute correction. Including negative quality samples and attribute prompts in the training further refines the model's ability to generate detailed and perceptually accurate images.

Fine-tuning large language models (LLMs) poses significant computational burdens, especially in federated learning (FL) settings. We introduce Layer-wise Efficient Federated Fine-tuning (LEFF), a novel method designed to enhance the efficiency of FL fine-tuning while preserving model performance and minimizing client-side computational overhead. LEFF strategically selects layers for fine-tuning based on client computational capacity, thereby mitigating the straggler effect prevalent in heterogeneous environments. Furthermore, LEFF incorporates an importance-driven layer sampling mechanism, prioritizing layers with greater influence on model performance. Theoretical analysis demonstrates that LEFF achieves a convergence rate of $\mathcal{O}(1/\sqrt{T})$. Extensive experiments on diverse datasets demonstrate that LEFF attains superior computational efficiency and model performance compared to existing federated fine-tuning methods, particularly under heterogeneous conditions.

Overcoming the limitations of individual-vehicle line-of-sight (LOS) sensing holds significant importance for guaranteeing the safety of driving. With a wider perception field, vehicle-infrastructure (VI) collaborative perception can provide vehicles with more comprehensive perception assistance, which has received widespread attention in recent years. However, the perception data fusion between infrastructure and vehicles is still impeded by issues such as large data volume and complex processing procedures, constituting a threat to driving safety. To deal with these issues, this paper proposes a VI collaborative environment perception approach based on sparse bird's eye view (BEV) features. By leveraging the representation of sparse BEV, features can be fused within a unified perspective in a lightweight manner, thereby enhancing the efficiency of feature fusion and reducing redundancy. Additionally, we present a solution for processing the overlapping features between the EGO-vehicle and road side unit (RSU) by taking the union of the coordinate points. Finally, the applicable vehicle and RSU datasets are collected through Carla. The experimental results demonstrate that the proposed approach can effectively mitigate the limitations of individual-vehicle perception by compensating for occluded information and provide a more comprehensive perception field.

Currently, end-to-end autonomous driving systems that employ imitation learning effectively learn and optimize driving strategies by integrating the driving behaviors of human experts with modern deep learning techniques. However, challenges such as scene diversity, data security, and training time must still be addressed to develop a model that is applicable across various traffic scenarios while maintaining high accuracy. To tackle these issues, this paper proposes a personalized feder-ated imitation learning algorithm that aggregates locally trained imitation learning decision models from vehicles operating in different scenarios through a distributed training approach, thereby enhancing training efficiency and model accuracy. Specifically, considering the variability in communication link quality and potential disconnections caused by the mobility of vehicles in a connected vehicle network, we introduce a personalized user selection algorithm. Building on this foundation, we employ a federated imitation learning method to efficiently and rapidly train a driving decision model with comparable performance while safeguarding data privacy. Extensive simulation results confirm the superiority of the proposed algorithm in terms of training speed and model accuracy.

The perception of a single vehicle has limitations. In contrast, vehicle-infrastructure cooperative perception can share perception information among nodes, overcoming view limitations and occlusion for more comprehensive environmental awareness. However, vehicle-infrastructure cooperative perception encounters two main challenges: the trade-off between perception performance and communication bandwidth, and the negative impact of communication delay on perception. To address these challenges, this paper devises a cooperative data transmission algorithm with delay compensation that innovatively combines feature selection and delay compensation to reduce data volume and offset delays. The algorithm exploits spatial value differences for dynamic feature selection, reducing data transmission while maintaining key performance, and has a receiving-end designment to synchronize the asynchronous features. Simulation results show that the proposed a Sparse BEV Feature Transmission Algorithm with Delay Compensation (DC-SBEVTx) significantly reduces data transmission volume in bandwidth-limited scenarios while maintaining acceptable Average Precision (AP). It also maintains high AP under communication delays, outperforming baseline methods in terms of AP across various conditions, such as different average delay and position noise levels.

Federated Learning (FL) enables the collaborative training of models by users distributed across various locations, transforming traditional data sharing into model sharing. This paradigm holds the promise of facilitating the development of safe, reliable, and accurate driving models within Internet of Vehicles (IoV), with its performance contingent upon the stability of the training process. However, traditional FL relies on a central server for aggregation, which is susceptible to malicious attacks. Moreover, limited communication resources prevent the inclusion of all users in the training process. To resolve issues related to reliability and resource utilization, this paper proposes a reliable Rotating Server Federated Learning (RSFL) algorithm to enhance the security and efficiency of FL. Specifically, we first consider the vehicular topology and participation in FL during their transition, and introduce a server rotation algorithm that incorporates a weighted sum of multiple factors including model training activity, vehicle credibility, speed stability, and distance to augment system security. Additionally, addressing the limitation of server channel resources that can impede FL efficiency, this paper proposes a method to select high-quality users for channel resource allocation by comprehensively considering participation latency, contribution, energy, and channel state during the FL process. This optimizes resource usage at the FL server side and constructs an efficiency-maximization problem for FL to improve the convergence rate. Simulation results confirm that the proposed RSFL algorithm can significantly enhance the security and system efficiency of FL.

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.

Federated learning (FL), as a typical distributed machine learning framework, has been effectively applied to traffic flow optimization, driving behavior analysis, and other areas. However, the stability and efficiency of FL systems heavily rely on the quality and cooperation of participants. If participants find no profit in the FL process, they may reduce their willingness to participate due to energy consumption and limited resources. To bridge these gaps, this article proposes a demand-balanced incentive mechanism for multiple FL tasks. First, considering the time-varying channel characteristics in the Internet of Vehicles (IoV) scenario, two optimization problems are constructed: 1) maximizing task-matching satisfaction and 2) minimizing communication energy consumption. Second, these problems are transformed into a distributed incentive mechanism based on a multileader-multifollower (MLMF) Stackelberg game, and a bi-level alternating direction method of multipliers (ADMM) algorithm is proposed to solve for the optimal resource allocation and reward schemes that balance the demands of all parties. Furthermore, this article designs a multiagent deep reinforcement learning-based method to solve the incentive problem, thereby avoiding the impact of information asymmetry. Simulation results verify that the proposed scheme not only balances the demands of all parties but also enhances user participation without being affected by the number of participants, making it suitable for IoV environments.

In the Internet of Vehicles (IoV), centralized transmission of vehicle data poses significant privacy risks. Applying federated learning (FL) to vehicular networks enables collaborative model training while preserving data privacy. However, due to heterogeneous computational capabilities across devices, some users may drop out due to insufficient computing resources, resulting in slow convergence and degraded model accuracy. Furthermore, the traditional sequential transmission of model parameters creates communication bottlenecks and increases training latency, particularly when dealing with a large number of participating vehicles. Over-the-air computation (AirComp) provides an innovative solution by exploiting the natural super-position property of wireless channels to enable simultaneous transmission and aggregation of signals from multiple users, significantly reducing communication rounds and system latency compared to conventional sequential approaches. In order to solve the above problems, this paper combines AirComp with FL to form an adaptive OA-FL algorithm that addresses computing resource heterogeneity and improves communication efficiency through parallel signal processing. Specifically, we first adaptively adjust the number of local iterations based on the assessment of users’ available remaining computational resources to improve user participation rates. Subsequently, we formulate and solve an optimization problem to minimize model parameter distortion caused by AirComp aggregation, thereby reducing communication overhead while accelerating model aggregation and enhancing model accuracy. Simulation results demonstrate that the proposed OA-FL method effectively reduces client unavailability, improves FL training efficiency, and achieves faster convergence compared to conventional FL approaches.

Federated learning (FL), as a distributed machine learning paradigm, facilitates collaborative training without sharing raw data and holds promise for effective application in the Internet of Vehicles (IoV) for tasks, such as traffic flow prediction and driving behavior analysis. However, the efficiency of FL systems relies on the integrity of the local dataset and the level of user contribution. Vulnerabilities to attacks by malicious users and suboptimal aggregation methods can compromise system performance. To address these issues, this article proposes a blockchain-based FL secure aggregation algorithm to bolster FL robustness. Specifically, in the absence of a centralized trust authority in the IoV, we establish a hierarchical blockchain-empowered IoV reputation management framework that leverages smart contracts to create a trustworthy environment for reputation sharing. Additionally, a lightweight consensus protocol tailored for blockchain efficiency is proposed, thus facilitating a flexible and effective implementation of FL in the IoV. Furthermore, we introduce a reputation-based model selection evaluation scheme and, based on this, a robust FL secure aggregation algorithm. This novel reputation assessment strategy mitigates the effects of interaction uncertainties and integrates a broader spectrum of IoV-specific reputation determinants, thereby enhancing the precision of model selection. The simulation results validate the proposed framework’s superiority in terms of robustness, adaptability, and security.

In this article, we propose a hybrid-field-aware two-stage beamforming (HFA-TSB) scheme for extremely large-scale MIMO (XL-MIMO) systems. The HFA-TSB scheme can reduce the high pilot overhead associated with channel estimation by leveraging statistical channel state information (SCSI). Utilizing our derived ergodic spectral efficiency (SE), which relies solely on SCSI, we can eliminate the interference resulting from the nonorthogonality of near-field polar domain codewords. Specifically, the HFA-TSB scheme comprises two stages: 1) pre-beamforming and 2) precoding. In the pre-beamforming stage, we use SCSI to design the pre-beamforming matrix, focusing on eliminating interference among asymptotically orthogonal channels. This can establish an equivalent reduced-dimensional channel matrix, which can be estimated using less pilot overhead. Additionally, to address the interference caused by the nonorthogonality of near-field codewords, we derived an ergodic SE that relies solely on SCSI. Using this as an optimization objective, the pre-beamforming matrix design problem is formulated as a 0-1 integer nonlinear programming problem, which is challenging to solve. To address this, we propose a constraint-driven two-phase greedy beam selection algorithm for identifying effective beams. In the precoding stage, we design the precoder based on the estimated equivalent sparse channel to effectively mitigate any residual interuser interference. Simulations validate the superior performance of our proposed HFA-TSB scheme in terms of net SE.

In the Internet of Vehicles (IoV), federated learning (FL) provides a privacy-preserving solution by aggregating local models without sharing data. Traditional supervised learning requires image data with labels, but data labeling involves significant manual effort. Federated self-supervised learning (FSSL) utilizes self-supervised learning (SSL) for local training in FL, eliminating the need for labels while protecting privacy. Compared to other SSL methods, Momentum Contrast (MoCo) reduces the demand for computing resources and storage space by creating a dictionary. However, using MoCo in FSSL requires uploading the local dictionary from vehicles to base station (BS), which poses a risk of privacy leakage. Simplified contrast (SimCo) addresses the privacy leakage issue in MoCo-based FSSL by using dual temperature instead of a dictionary to control sample distribution. Additionally, considering the negative impact of motion blur on model aggregation, and based on SimCo, we propose a motion blur-resistant FSSL method, referred to as BFSSL. Furthermore, we address energy consumption and delay in the BFSSL process by proposing a deep reinforcement learning (DRL)-based resource allocation scheme, called DRL-BFSSL. In this scheme, BS allocates the central processing unit (CPU) frequency and transmission power of vehicles to minimize energy consumption and latency, while aggregating received models based on the motion blur level. Simulation results validate the effectiveness of our proposed aggregation and resource allocation methods.

To fulfill future diverse user requirements, 6G networks are envisioned to provide everyone-centric customized services ubiquitously and precisely. However, the diversity in user requirements and the heterogeneity in network resources challenge conventional network operators in network management and service provision. In this article, we investigate the artificial intelligence (AI) service provision in the multilayer heterogeneous network. To provide ubiquitous intelligence to users with different computing requirements, an intelligence-native network architecture is designed. Based on the proposed architecture and the AI model stitching mechanism, we formulate the joint AI provision and access selection problem as a mixed integer nonlinear programming (MINLP) problem to maximize the average user satisfaction value and user satisfaction rate. Then, a heuristic solution based on Dung Beetle algorithm is proposed to optimize the AI model selection, AI service deployment, user access, and stitching coefficient jointly. Extensive simulations are conducted to evaluate the performance of our proposed architecture and algorithm.

Presents corrections to the paper, (Correction to “Toward Native Intelligence: An Efficient and Flexible AI Services Provision Scheme in Multilayer Heterogeneous Networks”).

In the rapidly evolving landscape of Internet of Vehicles (IoV) technology, cellular vehicle-to-everything (C-V2X) communication has attracted much attention due to its superior performance in coverage, latency, and throughput. Resource allocation within C-V2X is crucial for ensuring the transmission of safety information and meeting the stringent requirements for ultralow latency and high reliability in vehicle-to-vehicle (V2V) communication. This article proposes a method that integrates graph neural networks (GNNs) with deep reinforcement learning (DRL) to address this challenge. By constructing a dynamic graph with communication links as nodes and employing the graph sample and aggregation (GraphSAGE) model to adapt to changes in graph structure, the model aims to ensure a high success rate for V2V communication while minimizing interference on vehicle-to-infrastructure (V2I) links, thereby ensuring the successful transmission of V2V link information and maintaining high transmission rates for V2I links. The proposed method retains the global feature learning capabilities of GNN and supports distributed network deployment, allowing vehicles to extract low-dimensional features that include structural information from the graph network based on local observations and to make independent resource allocation decisions. Simulation results indicate that the introduction of GNN, with a modest increase in computational load, effectively enhances the decision-making quality of agents, demonstrating superiority to other methods. This study not only provides a theoretically efficient resource allocation strategy for V2V and V2I communications but also paves a new technical path for resource management in practical IoV environments.

The communication among nodes in the space–air–sea integrated network (SASIN) relies on collaborative multihop transmission. Hence, effective routing techniques should be designed to optimize multiple indicators. Routing optimization for multihop is usually focused on optimizing a single metric. Moreover, designing effective routing strategies for multihop networks with SASIN is challenging as balancing multiple performance metrics can lead to conflicts. In this article, we propose near-Pareto multiobjective routing optimization for SASIN, which adopts multiobjective combinatorial optimization (MOCOP) to strike a tradeoff among multiple objectives. We establish the SASIN system model, including channel models of communication links between satellites, aircraft, and ships. Furthermore, we use multiobjective optimization methods to formulate objective functions of spectral efficiency, energy efficiency, and delay. We employ the multiobjective evolutionary algorithms (MOEAs) for approximating the set of the Pareto optimal solutions. An improved nondominated sorting genetic algorithm II (INSGA II) and an improved strength Pareto evolutionary algorithm II (ISPEA II) are proposed to generate approximations of the Pareto optimal set. We evaluated the MOCOP formulation, and the SASIN network topology was built based on real data and simulated data. The simulation results indicate that a set of beneficial tradeoff solutions can be obtained for providing flexible selection of communication connections by addressing the multiobjective routing problem formulated. The results demonstrate that the MOEAs utilized have the potential to find Pareto-optimal solutions for SASIN.

Vehicular edge computing (VEC) is an emerging technology with significant potential in the field of Internet of Vehicles (IoV), enabling vehicles to perform intensive computational tasks locally or offload them to nearby edge devices. However, the quality of communication links may be severely deteriorated due to obstacles such as buildings, impeding the offloading process. To address this challenge, we introduce the use of reconfigurable intelligent surface (RIS), which provide alternative communication pathways to assist vehicle communication. By dynamically adjusting the phase-shift of the RIS, the performance of VEC systems can be substantially improved. In this work, we consider an RIS-assisted VEC system, and design an optimal scheme for local execution power, offloading power, and RIS phase-shift, where random task arrivals and channel variations are taken into account. To address the scheme, we propose an innovative deep reinforcement learning (DRL) framework that combines the deep deterministic policy gradient (DDPG) algorithm for optimizing RIS phase-shift coefficients and the multiagent DDPG (MADDPG) algorithm for optimizing the power allocation of vehicle user (VU). Simulation results show that our proposed scheme outperforms the traditional centralized DDPG, twin delayed DDPG (TD3), and some typical stochastic schemes.

In the digital twin mobile edge network, the maintenance of the vehicle twin model and vehicular task processing in the server require the support of computing resources. In addition, they are performed simultaneously. Therefore, how to allocate resources for twin maintenance and task processing under limited server resources is crucial. However, current research tends to ignore the aspect of resource competition for twin maintenance. In this study, we analyze the delays of these two affected by resource allocation under a generic digital twin mobile edge network (DTMEN) to construct the optimization problem. For this problem, we transformed the problem using a Markov decision process. Meanwhile, we propose a multi-agent reinforcement learning (MADRL) based twin maintenance and task processing resource collaborative scheduling (TMTPRCS) algorithm to solve the problem. Experiments show that our proposed approach is effective in terms of resource allocation compared to other alternative algorithms.

Due to the advantages of flexible deployment and strong environmental adaptability of an unmanned aerial vehicle (UAV), UAVs serve as aerial base stations (BSs) to meet Quality of Services (QoSs) of ground users in internet of things (IoT) networks. NOMA (Non-Orthogonal Multiple Access) is a potential technique in wireless communications area, which can significantly improve sum spectrum efficiency (SE) of systems. To avoid the limitation of perfect CSI, opportunistic beamforming (OBF) is proposed, where a set of randomly generated weights is used to preprocess transmitted signals. Due to multiuser diversity gain introduced by OBF, OBF-NOMA systems can achieve approximate sum SE to conventional NOMA systems. Additionally, reconfigurable intelligent surfaces (RISs) are involved to overcome obstruction and obtain further improvements of SE. Therefore, this paper proposes a RIS-aid OBF-NOMA system in UAV-IoT networks, where random weights and opportunistic phase matrix are respectively applied in a UAV and RIS. Statistical characteristics of equivalent channels are derived in Nakagami-m (m≥1) fading channels. Theoretical asymptotic analyses of SE and bit error rate (BER) are then presented. Furthermore, a non-convex optimization problem is formulated to maximize SE. To obtain the optimal solution, we divide the problem into two sub-optimization problems and apply a joint iterative algorithm. Numerical results show that the proposed method achieves a satisfactory SE without complex channel estimation and perfect CSI.

To meet the growing demand for data traffic, spectrum-rich optical wireless communication (OWC) has emerged as a key technological driver for the development of 6G. The resonant beam communication (RBC) system, which employs spatially separated laser cavities as the transmitter and receiver, is a high-speed OWC technology capable of self-alignment without tracking. However, its transmission through the air is susceptible to losses caused by obstructions. In this paper, we propose an intelligent reflecting surface (IRS) assisted RBC system with the optical frequency doubling method, where the resonant beam in frequency-fundamental and frequency-doubled is transmitted through both direct line-of-sight (LoS) and IRS-assisted channels to maintain steady-state oscillation and enable communication without echo-interference, respectively. Then, we establish the channel model based on Fresnel diffraction theory under the near-field optical propagation to analyze the transmission loss and frequency-doubled power analytically. Furthermore, communication power can be maximized in real-time by dynamically controlling the beam-splitting ratio between the two channels according to the varying loss levels encountered over air. Numerical results validate that the IRS-assisted channel can compensate for the losses in the obstructed LoS channel and misaligned receivers, ensuring that communication performance reaches an optimal value with dynamic ratio adjustments.

In this paper, we propose a transmissive reconfigurable intelligent surface (TRIS)-empowered distributed cooperative integrated sensing and communication (ISAC) network, which enhances the coverage and wireless environment understanding through the joint design of cooperative users (CUEs) and destination users (DUEs). Rate-splitting multiple access (RSMA) is implemented at the base station (BS), where the common stream is decoded and recoded by the CUEs and forwarded to the DUEs, while the private stream meets the CUEs’ own communication requirements. We construct an optimization problem with the objective of maximizing the minimum Radar mutual information (RMI), and jointly optimize the BS beamforming matrix, the CUE beamforming matrixs, common stream rate, and user scheduling vectors. To address the challenges of the nonconvex optimization problem, the consensus alternating direction multiplier framework (ADMM) is utilized to decouple the variables, and the subproblems are solved independently through iterative optimization until overall convergence is achieved. Numerical results validate the superiority of the proposed scheme in terms of improving communication sum-rate and RMI, and greatly reduce the algorithm complexity.

In this paper, we propose a novel transmissive reconfigurable intelligent surface (TRIS) transceiver-enhanced robust and secure integrated sensing and communication (ISAC) network. A time-division sensing communication mechanism is designed for the scenario, which enables communication and sensing to share wireless resources. To address the interference management problem and hinder eavesdropping, we implement rate-splitting multiple access (RSMA), where the common stream is designed as a useful signal and an artificial noise (AN), while taking into account the imperfect channel state information and modeling the channel for the illegal users in a fine-grained manner as well as giving an upper bound on the error. We introduce the secrecy outage probability and construct an optimization problem with secrecy sum-rate as the objective functions to optimize the common stream beamforming matrix, the private stream beamforming matrix and the timeslot duration variable. Due to the coupling of the optimization variables and the infinity of the error set, the proposed problem is a nonconvex optimization problem that cannot be solved directly. In order to address the above challenges, the block coordinate descent (BCD)-based second-order cone programming (SOCP) algorithm is used to decouple the optimization variables and solving the problem. Specifically, the problem is decoupled into two subproblems concerning the common stream beamforming matrix, the private stream beamforming matrix, and the timeslot duration variable, which are solved by alternating optimization until convergence is reached. To solve the problem, S-procedure, Bernstein’s inequality and successive convex approximation (SCA) are employed to deal with the objective function and non-convex constraints. Numerical simulation results verify the superiority of the proposed scheme in improving the secrecy energy efficiency (SEE) and the Cramér-Rao boundary (CRB).

In wireless communication adversarial scenarios, signals are easily intercepted by non-cooperative parties, exposing the transmission of confidential information. This paper proposes a true-and-false (T/F) frequency multiplexing based anti-intercepting transmission scheme capable of concealing truth while showing fake (CTSF), integrating both offensive and defensive strategies. Specifically, through multi-source cooperation, true and false signals are transmitted over multiple frequency bands using non-orthogonal frequency division multiplexing. The decoy signals are used to deceive non-cooperative eavesdropper, while the true signals are hidden to counter interception threats. Definitions for the interception and deception probabilities are provided, and the mechanism of CTSF is discussed. To improve the secrecy performance of true signals while ensuring decoy signals achieve their deceptive purpose, we model the problem as maximizing the sum secrecy rate of true signals, with constraint on the decoy effect. Furthermore, we propose a bi-stage alternating dual-domain optimization approach for joint optimization of both power allocation and correlation coefficients among multiple sources, and a Newton’s method is proposed for fitting the T/F frequency multiplexing factor. In addition, simulation results verify the efficiency of anti-intercepting performance of our proposed CTSF scheme.

Vehicular cooperative perception (VCP) facilitates the exchange of sensing data among vehicles through vehicle-to-everything (V2X) communication, significantly increasing the sensing range and precision of individual autonomous vehicles (AVs). However, efficiently managing the sharing and processing of large volumes of sensing data presents challenges due to restricted communication and computation resources. This study introduces an integrated sensing, communication, and computation (ISCC)-based task offloading and resource allocation (ITORA) framework, which optimizes cooperative perception by determining what data to share, which vehicles to involve, and how to process the data effectively. We develop an information value function to evaluate the data quality for each vehicle. Subsequently, we design strategies for sensing task allocation, task offloading, and resource allocation to enable value-driven data selection at a subregion level, facilitating collaborative computing among edge servers and vehicles. Additionally, we formulate an optimization problem aimed at maximizing information value while minimizing delay and energy consumption, subject to constraints on a full region of interest (RoI) coverage, delay, wireless bandwidth, and computational resources. We decompose the mixed-integer nonlinear programming (MINLP) problem into two subproblems, devising a sensing task allocation algorithm and a proximal policy optimization (PPO)-based task offloading and resource allocation (PTORA) algorithm to address them. Comprehensive simulations validate the effectiveness of the proposed PTORA in optimizing information value, reducing task execution delay, and minimizing energy consumption.

In digital-twin-enabled heterogeneous vehicular networks (DT-HetVNets), vehicles need to synchronize data to their DTs deployed in the cloud for decision-making. However, for a vehicle which is simultaneously covered by a group of heterogeneous network infrastructures, the DT of the vehicle (DT-V) can connect with the DTs of infrastructures (DT-Is) in different infrastructure groups across regions in the virtual networks so that each DT-V may deceive the DT-Is by interacting with multiple DT-I groups and selecting the optimal one to synchronize data. To this end, we propose an on-demand data synchronization scheme for DT-Vs and DT-Is. In the scheme, infrastructures and vehicles are grouped based on their geographical locations and the arrival time of each vehicle through which the DT-Vs and DT-Is can interact with each other to make decisions in groups. Then, the requirements of DT-Vs (i.e., minimize synchronization cost and maximize synchronization satisfaction) and DT-Is (i.e., maximize profits) are considered to design their utility functions and the decision-making process between the DT-Vs in each group and the DT-Is in each group is formulated as a Stackelberg game to obtain their optimal strategies. After that, considering the deceptive behavior of vehicles, a joint optimization algorithm that integrates the Stackelberg game and the selection of each DT-V is designed to obtain the real equilibrium solution for DT-Vs and DT-Is to maximize their utilities. Simulation results show that our scheme can obtain the highest utilities compared with the traditional schemes.

Edge collaboration is expected to effectively relieve the load of base stations and enhance the driving experience of autonomous vehicles (AVs). However, in existing edge collaboration schemes, the frequent information exchange between AVs will consume a significant amount of resources. In addition, the existing schemes ignore the types of services, where services with different types may be combined into a composite service which affects the utility of AVs. To this end, we consider various types of services in autonomous vehicular networks (AVNs) and propose a digital twin (DT)-enabled edge collaboration scheme for composite services. Specifically, we first divide the DTs of service requesters (DT-SRs) into service request groups (SRGs) based on the same basic service requests and propose an architecture to facilitate the edge collaboration between the DTs of the leaders of SRGs (DT-L-SRGs) and the DTs of the service providers (DT-SPs). In this architecture, different service composition forms will result in different resource purchase strategies for DT-L-SRGs and different resource pricing strategies for DT-SPs. Therefore, we model the process of service composition as a coalition game to determine the optimal service composition form for each basic service. In the process of the coalition game, in order to obtain the optimal resource purchase strategy for each DT-L-SRG and the optimal resource pricing strategy for each DT-SP under different coalition structures, the interaction between the DT-L-SRGs and the DT-SPs is formulated as a Stackelberg game. By obtaining the game equilibrium, the optimal strategies of each DT-L-SRG and each DT-SP can be determined to measure the performance of the given coalition structure until a stable and optimal composite service structure is finally formed through multiple rounds of iterations. Compared with traditional schemes, the simulation results demonstrate that our scheme can bring the highest utilities to both the SRs and the SPs.

Wireless Traffic Prediction (WTP) plays a significant role in achieving intelligent resource management for communication systems. However, WTP still faces challenges such as inaccurate prediction resulting from the complex spatial-temporal characteristics due to user mobility, high communication overhead caused by the complexity of the prediction model, and user privacy issues stemming from Centralized Learning (CL). To address the aforementioned issues, this paper proposes a WTP framework under the Federated Learning (FL) strategy called Federated Spatial-Temporal Dual-attention based Network (FedSTDN). Aiming at improving communication efficiency and simultaneously representing various wireless traffic patterns, a data augmentation-based clustering algorithm is adopted, which groups cells into different regions using a small augmented dataset, facilitating subsequent processing. To improve prediction performance, a local prediction model based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) is proposed to capture the short- and long-term dependencies of traffic. Additionally, a novel Kolmogorov-Arnold Network (KAN) layer is introduced to replace the traditional Multi-Layer Perceptron (MLP) layer, further enhancing prediction performance. Simulations on two different real-world datasets verify the effectiveness and efficiency of FedSTDN. Compared to the well-performing baseline, the proposed FedSTDN achieves up to 32.83% and 24.30% improvements in Mean Square Error (MSE) and Mean Absolute Error (MAE) on the Milan dataset, respectively. For the Trentino dataset, FedSTDN achieves up to 17.25% and 5.86% improvements in MSE and MAE, respectively.

Satellite-terrestrial integrated networks based on edge computing can provide computation offloading service to terminal devices in remote areas. However, it faces various limitations, including satellite energy consumption, computation delay, and environmental dynamics, etc. In this paper, we propose a satellite-terrestrial integrated cloud and edge computing network (STCECN) architecture, including satellite layer, terrestrial layer and cloud center, where computing resources exist in multi-layer heterogeneous edge computing clusters. Optimization of system delay and energy consumption is defined as a mixed-integer programming problem. Moreover, we present a deep reinforcement learning-based computation offloading decision algorithm that can adapt to the dynamics and variability of satellite networks. A dual network computation offloading decision method is proposed for delay and energy consumption based on deep reinforcement learning offloading (DRLO), including deep convolutional network update method, quantization strategy, and bandwidth resource allocation. Meanwhile, the proposed method is based on previous experience and integrates deviation adjustment strategies for decision making to solve the problem of pseudo-patch loss caused by satellite network switching. The simulation results indicate that the proposed method performs almost consistently with traditional heuristic algorithms, with only 20% of the time consumption of the latter, and the number of pseudo packet loss also decreases to the original 10–20%.

The surge in mobile application services has led to diversified traffic and increased demands on network resources. Traditional multipath algorithms, designed for resource integration through subflow scheduling across paths, struggle with disharmonious transmission caused by terminal mobility and differentiated path resources. Especially when differentiated services are transmitted concurrently, disharmonious transmission can give rise to resource contention, causing a large number of subflows to congest a single path and leading to performance degradation. To mitigate these challenges, this paper introduces HarmonyPath, a fine-grained flexible multipath transmission mechanism that can ensure harmonious resource occupation. Specifically, HarmonyPath firstly employs an in-band telemetry protocol to gather path resource information, generating a network resource distribution map. Based on this map, it flexibly allocates path resources according to the network resource distribution and service requirements. Then, HarmonyPath establishes a collaborative matching model for service demands and path resources. Through matrix transformation and calculation, it rapidly generates and deploys the scheduling strategy. To further alleviate service contention, HarmonyPath employs heuristic algorithms to optimize the scheduling strategy and achieve precise multipath transmission. Experiments demonstrate that HarmonyPath surpasses traditional algorithms in the multipath transmission of differentiated services, offering flexible service resource guarantees and enhancing network resource utilization efficiency.

Current timestamp-supervised temporal action segmentation (TS-TAS) methods typically follow a two-phase pipeline: initializing the model with timestamp labels and refining it with pseudo-labels. However, limited by the sparsity of timestamp annotations, current methods' performance is sub-optimal. Specifically, initializing the model with only timestamp annotations may cause overfitting to labeled frames. Additionally, sparse timestamp annotations cannot capture the diverse action representations throughout the whole instance, especially those near the ambiguous action boundaries, leading to pseudo-label noise. Inspired by the cluster assumption of semi-supervised learning (SSL) that points within the same manifold likely share the same label, we here model TS-TAS as an SSL problem. Specifically, we propose a Temporal Embedding Consistency (TEC) strategy to mitigate the excessive focus on annotated frames. The TEC strategy encourages frames with similar representations within the video to have similar classification probability distributions, thereby propagating labeled frames' information to implicit ones. Besides, we design a TS-Mix strategy to further leverage unlabeled data to mitigate the influence of pseudo-label noise in a consistency regularization manner. The TS-Mix strategy includes intra-mix, which adds linear interpolation of two adjacent timestamps to every frame between them, and inter-mix, which mixes frames from two different untrimmed videos frame-by-frame. Then the mixed video is trained with the correspondingly mixed pseudo-labels. Comprehensive experimental results on different benchmarks show that we achieve new state-of-the-art performances. Furthermore, the proposed method can seamlessly enhance existing methods, significantly improving their performances.

Delay-sensitive applications like telemedicine and VR/AR intensify competition for network resources and elevate congestion risks, particularly in mobile networks with highly dynamic link conditions. Traditional end-to-end congestion control methods suffer from prolonged response times, rendering them ineffective for Delay-sensitive applications. To this end, this paper proposes a novel In-Network Congestion Control (INCC) mechanism that accelerates congestion control by enabling network nodes to proactively identify bottlenecks and promptly notify end-hosts. Unlike traditional end-host-centric approaches, INCC facilitates collaborative congestion decision-making between end-hosts and in-network unit. INCC classifies congestion into two phases: “yellow” and “red” based on the local queue length bottleneck awareness and global congestion flow bottleneck statistics. For the “yellow” local congestion phrase, we design an in-network local control algorithm that performs proactive packet dropping and rate adjustment to mitigate emerging congestion. For the “red” global congestion phrase, we design an end-host and network cooperative global congestion control algorithm to make precise sending rate adaptation by proactive bottleneck awareness. We implement INCC via Linux kernel modifications and design three experiments to compare with Cubic, NewReno, and BBR. Experimental results demonstrate INCC has good performance on round-trip time and throughput, achieving 99.03% scheduling fairness in flow contention scenarios. Additionally, INCC has low execution overhead on CPU utilization and realize microsecond computational latency.

This paper studies a multiple intelligent reflecting surfaces (IRSs) collaborative localization system where multiple semi-passive IRSs are deployed in the network to locate one or more targets based on time-of-arrival. It is assumed that each semi-passive IRS is equipped with reflective elements and sensors, which are used to establish the line-of-sight links from the base station (BS) to multiple targets and process echo signals, respectively. Based on the above model, we derive the Fisher information matrix of the echo signal with respect to the time delay. By employing the chain rule and exploiting the geometric relationship between time delay and position, the Cramér-Rao bound (CRB) for estimating the target’s Cartesian coordinate position is derived. Then, we propose a two-stage algorithmic framework to minimize CRB in single- and multi-target localization systems by joint optimizing active beamforming at BS, passive beamforming at multiple IRSs and IRS selection. For the single-target case, we derive the optimal closed-form solution for multiple IRSs coefficients design and propose a low-complexity algorithm based on alternating direction method of multipliers to obtain the optimal solution for active beaming design. For the multi-target case, alternating optimization is used to transform the original problem into two subproblems where semi-definite relaxation and successive convex approximation are applied to tackle the quadraticity and indefiniteness in the CRB expression, respectively. Finally, numerical simulation results validate the effectiveness of the proposed algorithm for multiple IRSs collaborative localization system compared to other benchmark schemes as well as the significant performance gains.

Integrated sensing and communication (ISAC), as an important usage scenario of 6G, is capable of seamlessly integrating wireless sensing and communication for their mutual benefit. Taking full advantage of ISAC heavily relies on effectively solving resource allocation problems, which, however, are generally high-dimensional and non-convex, resulting in the optimization-based algorithms exhibiting high computation complexity and the traditional learning-based algorithms returning infeasible solutions. In this paper, we consider an ISAC scenario featured by multiple communication users and multiple sensing targets, aiming to develop an efficient and scalable algorithm that optimizes the radar transmit beampattern under the communication performance constraint. To this end, we propose a graph neural network (GNN) with implicit projection framework, where GNN captures the intricate interactions between communication users and sensing targets and meanwhile enables the joint optimization of communication and sensing beamforming matrices, and the projection module is applied to ensure the feasibility of the beamforming matrices design. Via capturing the permutation equivalence for communication matrices and the permutation invariance for the sensing matrix, the scalability of the proposed algorithm is guaranteed. Simulation results show that the proposed algorithm significantly reduces the computation complexity compared to the baselines, and achieves excellent algorithmic scalability and constraint satisfaction.

Mobile edge computing (MEC) enhances data processing by enabling users to offload tasks to edge servers with enough computation resource. In multi-user and multi-server scenario, the offloading scheduling is overwhelming complex and significantly influences the processing delay, which makes deep learning (DL) become an appealing approach. Yet, prior DL-based methods often overlook dynamic topology challenges due to the inflexibility of fixed neural network structures, leading to constrained performance. To tackle this challenge, a novel reinforcement learning framework named dynamic topology adaptive reinforcement learning (DTA-RL) is proposed in this paper. The MEC network is modeled as a graph based on the communication relationships between users and servers, and the offloading process is formulated as a Markov decision process (MDP). Building on the graph model and MDP, DTA-RL leverages graph attention networks to handle dynamic observation spaces and incorporates an attention mechanism for decision-making in environments with evolving action spaces. Simulation results illustrate that DTA-RL effectively reduces task processing delays and offloading failure rates within the MEC system. Furthermore, the pre-trained model can be seamlessly implemented in networks with new topology without experiencing significant performance degradation. The code is available at https://github.com/UNIC-Lab/DTA-RL.

Automatic Modulation Classification (AMC) plays a crucial role in non-cooperative communication systems and is an essential component of blind signal processing. The application of deep learning methods in modulation classification has shown tremendous potential, surpassing the performance of traditional methods by a large margin. However, the high storage and computational requirements of existing deep learning methods limit their practical applications. In this paper, we propose an AMC technique using an Adaptive Lightweight Wavelet Neural Network (ALWNN) that features a streamlined design and lower computational demands. This innovative model introduces an adaptive wavelet-based feature extraction method that effectively captures information at different frequencies in the input data, ensuring classification accuracy. Additionally, the model incorpo-rates depthwise separable convolution techniques, transforming traditional convolutions into depthwise convolutions and point-wise convolutions, Substantially diminishing the count of the model’s parameters and the complexity of its computations. The proposed ALWNN model strikes a balance between efficiency and accuracy. Simulation results demonstrate that with only 9899 and 9700 parameters, it achieves accuracies of 62.14% and 63.93% on the datasets known as RML2016.10a and RML2016.10b, respectively. Furthermore, we evaluate the model in terms of Floating Point Operations Per Second (FLOPS) and Normalized Multiply-Accumulate Complexity (NMACC) to provide a more comprehensive measure of computational complexity. Compared to existing methods, ALWNN reduces FLOPS by 1.25 to 1.91 orders of magnitude and NMACC by 0.81 to 1.6 orders of magnitude.

This paper introduces defensive adversarial attacks aimed at enhancing the security of semantic communication systems by confusing potential eavesdroppers. Existing research predominantly focuses on enhancing the accuracy of semantic communications while neglecting the security vulnerabilities posed by eavesdroppers. In this study, from the standpoint of physical layer security, defensive adversarial attacks are employed to introduce artificial noise into semantic communications, effectively concealing real information. This artificial noise is generated by deep neural networks to mislead eavesdroppers into perceiving the content of images as unrelated information, with little probability of disrupting normal semantic communications. Experimental results demonstrate that the proposed model can selectively mislead the decoding efforts of eavesdroppers, while ensuring uninterrupted decoding by legitimate receivers.

In this paper, we study intelligent multi-domain collaborative computing offloading within the space-air-ground integrated Internet of Things (SAG-IoT). While non-terrestrial transmission alleviates the burden on scarce terrestrial resources, it introduces significant propagation delay, rendering it unsuitable for all tasks. To address this issue, we categorize tasks into priority and general groups and design a dynamic priority resource management (DPRM) framework. This framework strategically pre-allocates resources to priority tasks, ensuring their completion on edge nodes. Within this framework, we formulate an optimization problem focused on offloading path selection and multi-dimensional resource management, to maximize the completion rates of general tasks while meeting the quality of service requirements for priority tasks. We introduce a hierarchical hybrid policy optimization based on DPRM (HHPO-DPRM) algorithm to tackle the aforementioned problem in highly dynamic network environments. Comparative analysis with two traditional algorithms underscores the effectiveness of our approach.

The rapid development of Low Earth Orbit (LEO) satellite networks has provided ubiquitous Internet access to users around the world, especially in areas where there are no terrestrial networks. However, a dish can only communicate with one of the available satellites when uploading data in the current framework, resulting in low communication efficiency. As the number of satellites continues to increase, the current framework cannot make full use of the user-satellite link resources. In this paper, we first conduct a measurement of Starlink’s network performance and report some unique features. Then, we propose an adaptive multi-link data allocation framework for LEO satellite networks where a dish can communicate with multiple satellites at the same time to improve data transmission efficiency. With this framework, data can be split into chunks and uploaded simultaneously over multiple links. Our goal is to determine the data allocation strategies to jointly optimize the transmission latency and data processing costs. To this end, we propose a deep reinforcement learning-based algorithm integrated with the traffic prediction module to determine the optimal data allocation strategies in a dynamic network environment. Through extensive simulations, we demonstrate the effectiveness of our approach compared with baselines.

Federated learning (FL) presents a novel paradigm for constructing global models by leveraging distributed client data while preserving privacy. Despite clients’ readiness to contribute computational resources via WiFi networks, the concurrent model uploads often trigger the competitive backoff mechanism inherent in the carrier sense multiple access with collision avoidance (CSMA/CA) protocol, which impairs the training efficiency and performance of FL. To address this challenge, this paper proposes an innovative sliding window-based asynchronous update approach for federated learning, named as FedSW. By properly configuring the sliding window size at the wireless access point (AP) of the WiFi network, FedSW orchestrates local training, model upload, aggregation, and distribution in harmony with the sliding window progress. This synchronization significantly improves training efficiency and model performance. Furthermore, the versatility of FedSW is demonstrated through its seamless integration with state-of-the-art (SOTA) algorithms. Our methodology is rigorously evaluated against FL benchmarks, showcasing its superior effectiveness. Simulation results confirm that FedSW consistently outperforms conventional benchmarks in terms of convergence, regardless of the sliding window size, while significantly reducing latency.

With the advent of the Industry 4.0 era, the widespread deployment of Automated Guided Vehicles (AGVs) in factories has enabled them to serve as sensor relays, assisting in collecting sensor data in areas with poor signal quality. Traditionally, the objective of sensor data collection has been primarily to reduce the delay in data acquisition. However, latency alone offers an incomplete reflection of the significance of sensor data to industrial tasks. Value of information (VoI) has emerged as a novel metric that more accurately reflects the impact of sensor data on the performance of upstream tasks. In this background, we introduce an innovative AGV-assisted sensor data collection strategy to minimize the loss of sensor data VoI. This strategy encompasses the selection of data fusion nodes, choice of transmission modes, and AGV path planning. We introduce a new metric called structural value entropy, which effectively reduces VoI loss during the data fusion process, and through the design of a metaheuristic algorithm based on ant colony optimization, achieves the selection of transmission modes and the planning of AGV paths with minimal VoI loss. Simulation experiments validate the effectiveness of the proposed strategy in maintaining VoI, demonstrating significant performance enhancements and acceptable convergence speed compared to baseline strategies, affirming the strategy's efficiency and feasibility in handling large-scale sensor data collection tasks.

Cloud-based exchange-assisted data trading (EADT) has become the most important paradigm to trade data, where the exchange builds a bridge between data owners, brokers, and buyers to enable them to gain benefits from data, and cloud storage services serve as a key component to deliver data. With cloud-based EADT, the data can be traded in a customized way and the value of data can be unleashed as much as possible. Despite the great advantages of such a paradigm, critical issues also arise. The data content is confronted with leakage, leading to privacy violation. Conventional encryption can be utilized to resolve this tension, but it makes customized data trading inefficient and even impossible. In this paper, we propose a secure data delivery scheme, dubbed STAGE, for cloud-based EADT. STAGE supports customized data trading while freeing the broker from heavy costs in terms of computation and communication. We formally define the security notions of STAGE and prove that STAGE is secure against various attacks. We also implement a STAGE prototype and conduct a comprehensive performance evaluation to demonstrate its efficiency and practicality.

This paper delves into the realm of intelligent and cooperative computing offloading within satellite-assisted Inter-net of Vehicles (Sat-IoVs). More specifically, it focuses on enabling efficient computing offloading for highly mobile vehicle users by formulating and executing an offloading path selection and multidimensional resource management (OPS-MDRM) optimization problem at a central controller. Given the complex amalgamation of continuous and discrete action spaces, along with various timescales inherent to the OPS-MDRM problem, we introduce a two-timescale framework. In this framework, we present a hierarchical hybrid policy optimization (HHPO) based on-policy algorithm to effectively tackle the aforementioned problem. Our comparative analysis against three traditional resource allocation methods underscores the outstanding performance achieved by the HHPO-based approach in the Sat-IoV networks.

Three-layer data trading, where a data broker collects “data materials” from multiple data owners, and then provides customized data products to buyers, remains the most prevalent paradigm in current data marketplaces. However, a profit-driven broker may generate “low-quality” data products based on scratched data but sell them at a high price. Worse still, a malicious broker would pirate others' data products to disrupt data marketplaces. In this paper, we propose a practical proof of dealership scheme, dubbed PPoD, to resist malicious brokers. The key technique behind PPoD is a redactable certification generation mechanism, which enables a broker to prove its dealership of a customized data product in an efficient way. We provide a formal security proof of PPoD, which demonstrates that various attacks, e.g., piracy and deception, launched by a malicious broker can be thwarted. We also implement a PPoD prototype and conduct a comprehensive performance evaluation to show its efficiency and practicality.

The advent of deep learning (DL) technology has increasingly captivated the research community’s interest in harnessing DL to enhance data transmission efficiency. Notwithstanding, prevalent methodologies often overlook the computation delay of DL processing data during the inferencing procedure, and fail to adjust intelligent algorithm complexity based on user features. To bridge this gap, we introduce $\mathbf{C o m}^{2}$ (Communication-Computation) framework, to synergize the optimization of communication and computational delays. $\mathrm{Com}^{2}$ adeptly navigates the trade-offs between communication and computation delays, facilitated by autoencoders of varying depths, thus one user can reduce communication delay through more computation latency, and vice versa. Further enhancing this framework, we present an optimization algorithm that marries QMIX with a cascaded graph neural network (GNN), designed to select the optimal autoencoder depth and optimize transmission resources in a distributed manner. This algorithm pioneers a label-free training regime, employing reinforcement learning and unsupervised learning to adaptively improve without the need for high-quality labels. Simulation results show that $\mathrm{Com}^{2}$, alongside the proposed optimization algorithm, maximizes the utility of users’ computing and transmission resources, significantly curtailing the overall data transmission delay by intelligently managing delay trade-offs.

Due to the stringent latency requirements for computationally intensive rendering in virtual reality (VR) transmission and the limitations of computational resources on VR devices, extensive research has focused on task offloading with joint communication and computing resource scheduling to address these issues. Traditional model-based theoretical methods face challenges with long online processing times, while data-driven methods lack interpretability. This paper proposes a knowledge-driven rendering task offloading strategy for immersive wireless VR with mobile edge computing (MEC). The rendering approaches include local, MEC, and collaborative offloading between VR devices and MEC servers. First, we formulate an optimization problem to maximize user quality of experience (QoE), which is defined as the weighted sum of latency and video resolution. To solve the optimization problem, we propose a knowledge-driven belief propagation (KD-BP) algorithm where the structure of the BP algorithm is regarded as knowledge. Specifically, the operations with high computational complexity in the BP algorithm are replaced by a deep neural network, termed the knowledge-fused deep learning (DL) method. Finally, numerical results show that when the number of users reaches 10, the proposed KD-BP algorithm significantly reduces online processing latency and closely matches the convergence speed and performance compared to the BP algorithm.

The deepening process of urbanization poses formidable challenges to the current transportation carrying capacity. The utilization of near-ground space (NGS) and urban air mobility (UAM) greatly enhance spatial dimensions and traffic flexibility of the transportation system. However, the current limited sensing capability falls short in meeting the real-time collaborative environmental sensing and intelligent control requirements of aerial transportation. Integrated sensing and communication (ISAC) combines the sensing system of UAM with 6G communication technologies, enabling them to collaborate and achieve data sensing, transmission, processing, and decision control. The use of artificial intelligence-generated content (AIGC) facilitates real-time data fusion and decision-making, adapting to dynamic and unpredictable environments. In this paper, we first model and analyze the traffic flow in three-dimensional space, achieving knowledge embedding based on artificial potential energy field theory. Next, we design a multimodal data fusion neural network structure, which utilizes the Variational Autoencoder (VAE) to generatively achieve feature fusion and compression. Finally, we construct a UAM digital simulation platform using AirSim, which generates considerable aerial data. The simulation results demonstrate that our proposed approach achieves a feature recognition accuracy of 90.38%. The total latency is below 0.6ms, which exhibits high real-time performance.

Connected vehicles (CVs) utilizing cellular vehicle-to-everything (C-V2X) technology are increasingly coexisting on the road with regular vehicles (RVs). As these CVs interact with each other and with roadside infrastructure through vehicle-vehicle and vehicle-infrastructure cooperation, the characteristics of traffic flow are changing in significant ways. It is therefore crucial to understand how different parameters of CVs, roadside sensors, and V2X communications affect the stability of heterogeneous traffic flow. In this research, we investigate the impact of several transportation and infrastructure parameters on the stability of heterogeneous traffic flow. Specifically, we first examine the effects of traffic density, penetration rate of CVs, detection accuracy of roadside sensors, and time delays in V2X communications. We propose a novel C-V2X-based vehicle-vehicle/vehicle-infrastructure cooperation architecture and develop a car-following model based on it. Then, the theoretical stability condition for heterogeneous traffic flow is derived, which reveals the interdependence of transportation and infrastructure parameters. The numerical simulations show that the proposed C-V2X-based vehicle-vehicle/vehicle-infrastructure cooperation architecture achieves traffic flow stability at lower CV penetration rates compared to existing studies that only consider vehicle-to-vehicle communications. This finding highlights the importance of leveraging the full potential of C-V2X technology for improving traffic flow stability in real-world settings.

FL enables collaborative training of autonomous driving models without sharing the original data. It enhances the model’s environmental adaptability and establishes an effective distributed paradigm for connected and autonomous vehicles (CAVs) to share driving experiences as well as make collaborative decisions. However, participants’ negative behavior, such as free riding due to selfishness, can significantly reduce federated learning (FL) training efficiency and model accuracy. Unlike previous studies that focused solely on a single FL task, this article proposes an incentive mechanism for long-term driving model training, which models the interactions between participants and the server during the long-term FL process as an infinitely repeated game. The incentive mechanism considers the relationship between participants’ historical behaviors and their future incomes, motivating participants to maintain positive behaviors throughout the long-term FL process and ensuring the efficient operation of the training process. Furthermore, in order to increase CAVs’ enthusiasm, we design reward rules that attract new participants and encourage sustained engagement. The simulation results demonstrate that the proposed incentive mechanism maximizes the profits of both CAVs and the server in long-term FL, which effectively reduces negative CAVs’ behaviors and improves the efficiency of FL training.

Vehicle platooning is a promising traffic model in intelligent transportation systems (ITSs), which can effectively improve resource utilization and reduce traffic congestion. The resource allocation for vehicle-to-everything (V2X) communications that consist of intraplatoon communications and interplatoon communications is crucial for safe operation of multiple vehicular platoons. Considering dynamic coordination pattern of vehicular platoons and layered architecture of vehicle platooning networks, a hierarchical resource decision-making framework is proposed in this article. In the proposed framework, the resource decision-making process is divided into two levels. The high level that generates and distributes coordination meta policy is deployed on base station (BS), and the low level that generates ego resource decisions is deployed in each platoon. To deal with optimization of resource allocation for multiplatoon V2X communications, a hierarchical reinforcement learning method (HierNet) is designed based on the proposed hierarchical decision-making framework. In HierNet, meta policy of the high level can be preserved and needs to be updated only when cooperative conditions of multiple platoons undergo distinct changes. Simulation experiments have demonstrated that our proposed method not only optimizes resource efficiency but also reduces the communication costs for resource decision making of vehicle platooning networks.

This article proposes a two-stage beamforming (TSB) scheme with user scheduling for FDD massive MIMO. The developed TSB scheme designs the analog prebeamformer and schedules the users using statistical channel state information (S-CSI), reducing the overhead of the pilot and the feedback. Particularly, in the one-ring local scattering channel model, the prebeamformer design and user scheduling problem is formulated as a 0–1 quadratic constrained quadratic programming (QCQP), which is further linearized to a mixed integer linear programming (MILP). In the multiple scattering clusters channel model, we design the prebeamformer and schedule the users based on graph theory, where the chromatic number of the equivalent matrix represents the minimum number of orthogonal pilots. Then, we propose an iterative beam selection and user scheduling (I-BSUS) scheme that approximates the minimum pilot constraint by the maximum vertex degree. Moreover, the net spectrum efficiency (NSE) is improved using a multiuser digital precoder, which depends on the effective instantaneous CSI (EI-CSI). Simulation results validate the superiority of the proposed scheme in enhancing the NSE over the existing schemes.

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.

Sparse code multiple access (SCMA) has excellent application prospects in satellite-terrestrial links because of its high spectral efficiency and access capacity. In the end-to-end SCMA systems, channel modeling is a fundamental task for the communication algorithm design and performance optimization, which however is very challenging as it requires in-depth domain knowledge and technical expertise in radio signal propagations, especially for modeling satellite-terrestrial fading channels. In this article, a convolutional autoencoder-aided SCMA paradigm based on the stochastic channel modeling and autoencoder structure is developed. We are the first to exploit generative adversarial network to represent the satellite-terrestrial fading channel effects for the convolutional autoencoder-aided SCMA. Specifically, convolutional neural networks (CNNs) are employed to jointly construct the encoder and decoder for SCMA to alleviate the curse of dimensionality. Furthermore, we propose a conditional Wasserstein generative adversarial network with the gradient penalty (CWGAN-GP)-based channel modeling approach to achieve approximately accurate conditional channel distribution. Particularly, the received signal corresponding to the pilot symbol is used as a part of the condition information, and the Wasserstein distance is used as a measure of the distance between the distributions. Gradient penalty is adopted to solve the problem of weight pruning forcing Lipschitz constraints, which leads to some data being unable to converge. The numerical results demonstrate the effectiveness of the proposed approach in terms of the bit error rate (BER), block error rate (BLER), and complexity in satellite-terrestrial fading channels.

The convergence of Blockchain and Internet of Things (BIoT) is fully considered as a paradigm for mitigating threats related to the trust, security, and privacy of Internet of Things (IoT) data. However, because the bandwidth across nodes and time varies in practical IoT networks, it is difficult for existing BIoT mechanisms guarantee blockchain consensus performances. The consensus time could become long owing to low-bandwidth nodes taking longer to download blocks than high-bandwidth nodes. Conventional wisdom holds that removing low-bandwidth nodes can decrease the consensus time, but the nodes could have high-bandwidth at another time owing to bandwidth variability; thus, kicking which nodes out of the consensus is a great challenge. In this article, a novel lightweight BIoT convergence (namely, E-Chain) is proposed to overcome bandwidth variability. The E-Chain first decouples the blockchain into on-chain validating and off-chain voting components. In the off-chain voting part, each node incurs a one-bit communication overhead for voting on a block based on a reputation index. This voting component does not need to download the full content of the block, and is therefore not affected by bandwidth variability. The reputation index was formulated using a rating algorithm with multidimensional IoT network metrics. In addition, the voting mechanism is secure and can still reach the correct consensus when suffering from byzantine attacks. By contrast, a block is validated and stored in a dispersed manner in the on-chain validating part. The E-Chain performances were then evaluated and compared with state-of-the-art mechanisms. Experimental results show that the E-Chain mechanism can significantly decrease both the consensus time and memory resources, and incur an acceptable memory overhead for resource-constrained IoT nodes.

Unmanned aerial vehicles (UAVs), due to their low cost and high flexibility, have been widely used in various scenarios to enhance network performance. However, the optimization of UAV trajectories in unknown areas or areas without sufficient prior information still faces challenges related to poor planning performance and low distributed execution. These challenges arise when UAVs rely solely on their own observation information and the information from other UAVs within their communicable range, without access to global information. To address these challenges, this article proposes the Qedgix framework, which combines graph neural networks (GNNs) and the QMIX algorithm to achieve distributed optimization of the Age of Information (AoI) for users in unknown scenarios. The framework utilizes GNNs to extract information from UAVs, users within the observable range, and other UAVs within the communicable range, thereby enabling effective UAV trajectory planning. Due to the discretization and temporal features of AoI indicators, the Qedgix framework employs QMIX to optimize decentralized partially observable Markov decision processes (Dec-POMDP) based on centralized training and distributed execution (CTDE) with respect to mean AoI values of users. By modeling the UAV network optimization problem in terms of AoI and applying the Kolmogorov-Arnold representation theorem, the Qedgix framework achieves efficient neural network training through parameter sharing based on permutation invariance. Simulation results demonstrate that the proposed algorithm significantly improves convergence speed while reducing the mean AoI values of users. The code is available athttps://github.com/UNIC-Lab/Qedgix.

Due to the openness of wireless channels, wireless communication is vulnerable to be eavesdropped, which results in confidential information leakage. Physical Layer security (PLS) technology provides a new way to solve this hidden danger of Internet of Things system. However, traditional PLS methods are often restricted by limited communication resources and unknown instantaneous channel state information of eavesdroppers, which makes it challenging to strike a balance between security and reliability in the communication system. Therefore, an adversarial defense embedded waveform design (ADEWD) method for physical layer reliable communication (PLRC) is proposed in this paper. Firstly, we use generative adversarial networks to generate amplitude controllable adversarial perturbation, and then superimpose it with original communication signal to form an adversarial signal. At the same time, we also design a demodulation network based on the modulation type of legitimate users to constrain the amplitude of the generated perturbations, to reduce the bit error rate (BER) loss after demodulation of the adversarial signal. With this waveform design, the adversarial signal not only enables reliable communication between legitimate users, but also utilizes embedded defense traps to prevent eavesdroppers from recognizing legitimate users. The experimental results demonstrate that our ADEWD method for PLRC has stronger defense capability and lower BER in both white-box and black-box scenarios, which reflects the defense robustness and communication reliability of the proposed waveform design method.

This article investigates semantic communication in high-speed mobile Internet of Vehicles (IoV), focusing on spectrum sharing between vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. We propose a semantic-aware spectrum-sharing (SSS) algorithm using deep reinforcement learning (DRL) with a soft actor-critic (SAC) approach. We start with semantic information extraction, redefining metrics for V2V and V2I spectrum sharing in IoV environments, introducing high-speed semantic spectrum efficiency (HSSE) and semantic transmission rate (HSR). We then apply the SAC algorithm to optimize decisions V2V and V2I spectrum-sharing decisions on semantic information. This optimization aims to maximize HSSE and enhance the success rate of effective semantic information transmission (SRS), including determining the optimal V2V and V2I sharing strategies, transmission power, and the length of transmitted semantic symbols. Experimental results show that the SSS algorithm outperforms other baseline algorithms, including other traditional-communication-based spectrum-sharing algorithms and spectrum-sharing algorithm using other reinforcement learning approaches. The SSS algorithm exhibits a 15% increase in HSSE and approximately a 7% increase in SRS.

Internet of Things (IoT) play a paramount role in every aspect of our daily lives. Due to more diverse human needs, the future IoT networks are expected to be highly dynamic and heterogeneous with assistance of mobile nodes, such as unmanned aerial vehicles (UAVs). However, the broadcast and openness nature of wireless communication and high-mobile characteristic of UAV can cause security threats to UAV-assisted IoT systems. For this sake, we consider exploiting covert communication to provide such a system with a higher level communication security, which can prevent the legitimate transmission being detected by the adversary monitor. Specifically, this article jointly optimizes the transmit power and hovering location of the UAV to guarantee communication security in the IoT system. We maximize the signal-to-noise ratio (SNR) of the legitimate receiver in presence of a malicious warden, with constraints of communication covertness, the UAV’s spatial location and maximum transmit power. Particularly, the UAV’s location is represented in terms of angles, rather than the commonly used distance, in most of the literature. The optimal location is determined in two steps. The simulation results show that the proposed optimization schemes can effectively find the optimal hovering location and transmit power of the UAV to maximize the receiver’s SNR under the covertness constraint. The optimal hovering location is directly above the line connecting the legitimate receiver and the warden, and in close proximity within the small region directly above the receiver, which has implications for other analogous research scenarios or practical applications of UAV.

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.

The escalating growth of the Internet of Things (IoT) has intensified the demand for dependable and efficient communication networks to accommodate the massive data volumes produced by interconnected devices. Satellite networks have emerged as a promising alternative, particularly in remote and underserved regions where terrestrial communication infrastructures are inadequate. Nevertheless, guaranteeing secure uplink communications in satellite-based IoT networks is a daunting task due to similar satellite channels and limited resources at IoT nodes. In this article, we explore the potential of unmanned aerial vehicle (UAV) to improve the secrecy performance of uplink transmissions in satellite-supported IoT networks. Specifically, we first introduce a framework for UAV-aided secure uplink communications, presuming a secure UAV-to-satellite connection. To mitigate the risks of ground eavesdroppers intercepting uplink transmissions, we develop a max–min secrecy rate optimization problem with uplink power constraints. To address this nonconvex problem, a streamlined two-stage optimization approach is proposed. In the inner stage, we combine uplink power allocation and UAV beamforming and propose a successive convex approximation (SCA)-based joint optimization algorithm to address them. In the outer stage, we propose a synergized bisection and coordinate descent algorithm to optimize UAV positioning. Convergence is attained by alternating iterations between these two stages. Particularly, the secrecy fairness among IoT users is reached by solving the max–min problem. Additionally, we offer a complexity analysis of the proposed algorithm and validate the efficacy of the presented approach through comprehensive simulation results.

With the proliferation of cellular vehicle-to-everything (C-V2X), connected and automated vehicles (CAVs) are gradually being commercialized. CAVs can interact with road infrastructure and human-driven vehicles (HDVs) to acquire relevant traffic information, thereby altering the characteristics of the traditional traffic flow. The emergence of CAVs is widely believed to bestow benefits to the traffic system in terms of safety, efficiency, and energy consumption. Nevertheless, as with most phenomena, there are two sides to the coin. Further exploration is necessary to determine whether the emergence of CAVs will trigger adverse effects and the underlying factors that may induce adverse effects. To be specific, this article first delves into how selfish driving behaviors (egoism CAV control strategy) can have an unfavorable impact on the performance of the traffic systems, thereby lowering the traffic efficiency. Subsequently, we develop an unselfish (altruism) CAV control strategy that aims to achieve the global optimization and improve the overall road operational capacity. Based on the simulation results obtained at different inflow and outflow rates on highway, it is evident that egoism driving behavior leads to a 11.55% decrease in average speed performance as compared to the noncontrol strategy, while altruism driving behavior results in a 20.14% improvement. Furthermore, we compare the proposed strategy with the current road infrastructure control, which only improves the average speed performance by 11.6%. This indicates that controlling CAVs has the potential to replace the deployment of the traditional road infrastructure, thereby optimizing the social and economic benefits. This article can provide insightful guidance for the future policy formulation in the transportation authorities, wherein the emergence of CAVs needs to be effectively regulated based on the altruism, thus fostering the establishment and development of a safe and efficient mixed traffic ecosystem.

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.

Autonomous driving vehicle (ADV) that is ready to transform our society and economy, is in desperate need of precise positioning over itself as well as surrounding environments. However, it is still a challenging issue for ADV to retrieve real-time positioning knowledge over road participants and dynamic surrounding environments, due to unsatisfied perception accuracy caused by sparse observations and limited perception range. Cooperative perception, which advocates cooperatively disseminating perception data among vehicles, has the potential to overcome the above limitations. To this end, this article proposes a novel edge-assisted multi-vehicle perception system to enhance vehicles’ awareness over surrounding environments, which is termed as EdgeCooper. EdgeCooper first schedules vehicles to share complementarity-enhanced and redundancy-minimized raw sensor data with an edge server, using multi-hop cooperative 5G V2X communications. Then, EdgeCooper merges vehicles’ individual views to form a holistic view with a higher resolution, thus enhancing perception robustness and enlarging perception range. We formulate multi-vehicle multi-hop cooperative data sharing as a minimum cost flow problem with conflict, and further prove that there exists no polynomial-time approximation algorithm with a constant performance ratio unless P = NP. Furthermore, a two-dimension graph coloring algorithm with guaranteed performance is proposed to eliminate conflict. We evaluate EdgeCooper by building a comprehensive simulation platform through a joint manipulation of SUMO, CARLA, NS3, and PyTorch. The experiment results show that, compared to a single vehicle’s perception, EdgeCooper performs effective and efficient in enhancing vehicular awareness, e.g., extending up to 3.6 times detection range and improving perception accuracy by 20%.

In mobile edge computing (MEC) systems, data is frequently transmitted between MEC servers and users holding mobile devices for supporting related services. However, critical threats towards data confidentiality and authenticity are raised: adversaries always attempt to extract data content from the transmission and impersonate others to spread malicious data for profits. Furthermore, users have to store the (secret and public) keys used for data transmission locally. Consequently, only devices maintaining the keys can be utilized to access the services provided by MEC servers, and “portability” cannot be achieved. In this paper, we propose a portable authenticated data transmission scheme (dubbed Biplane) via blockchain for MEC systems. Biplane is based on two techniques. One is a blockchain-based authenticated hybrid encryption mechanism, which guarantees data authenticity and confidentiality without requiring a third party (e.g., a Certificate Authority) to assist the MEC servers in certifying users’ public keys. The other one is a blockchain-based portable key management mechanism, which enables the user to transmit data without maintaining any parameter in her/his local devices. We formally prove that Biplane achieves confidential and authenticated data transmission in the universally composable (UC) framework. We also conduct a comprehensive evaluation to demonstrate that Biplane is efficient.

Timed data delivery is a critical service for time-sensitive applications that allows a sender to deliver data to a recipient, but only be accessible at a specific future time. This service is typically accomplished by employing a set of mailmen to complete the delivery mission. While this approach is commonly used, it is vulnerable to attacks from realistic adversaries, such as a greedy sender (who accesses the delivery service without paying the service charge) and malicious mailmen (who release the data prematurely without being detected). Although some research works have been done to address these adversaries, most of them fail to achieve fairness. In this paper, we formally define the fairness requirement for mailmen-assisted timed data delivery and propose a practical scheme, dubbed DataUber, to achieve fairness. DataUber ensures that honest mailmen receive the service charge, lazy mailmen do not receive the service charge, and malicious mailmen are punished. Specifically, DataUber consists of two key techniques: 1) a new cryptographic primitive, i.e., Oblivious and Verifiable Threshold Secret Sharing (OVTSS), enabling a dealer to distribute a secret among multiple participants in a threshold and verifiable way without knowing any one of the shares; and 2) a smart-contract-based complaint mechanism, allowing anyone to become a reporter to complain about a mailman’s misbehavior to a smart contract and receive a reward. Furthermore, we formally prove the security of DataUber and demonstrate its practicality through a prototype implementation.

Federated learning (FL) is a promising technology for autonomous driving, enabling connected and autonomous vehicles (CAVs) to collaborate in decision-making and environmental perception while preserving privacy. However, traditional FL algorithms face challenges related to imbalanced data distribution, fluctuating channel conditions, and potential security risks associated with malicious attacks on local models. This paper proposes a fair and secure FL algorithm that not only addresses the challenges arising from imbalanced data distribution and fluctuating channel conditions, but defends against malicious attacks. Specifically, we first propose a personalized local training round allocation algorithm to balance energy costs and accelerate model convergence. Next, in order to further guarantee security, we embed an attack module based on Gini impurity. Extensive simulations demonstrate that the proposed algorithm achieves energy fairness, reduces global iteration time, and exhibits resistance against malicious attacks.

In vehicular networks (VNets), vehicular federated learning (VFL) is a new learning paradigm that can protect data privacy of vehicle nodes (VNs) while training models. In VFL, the importance of data (IoD) is a key factor that affects model training accuracy. However, due to the heterogeneity of data in the VFL, it is a challenge to evaluate the quality of data owned by different VNs and design an efficient federated learning scheme to enable the VNs to complete learning tasks collaboratively. In this paper, we consider the IoD and propose a redundancy-aware collaborative federated learning (RCFL) scheme for the VFL. In the scheme, by jointly considering the data quality and the cooperation among VNs, we first design a redundancy-aware federated learning architecture to efficiently provide learning services in VNets. Then, we develop a data importance model that integrates the non-independent and identically distributed (non-IID) degree and the redundancy of data (RoD) to evaluate the data quality and formulate the cooperation of the VNs as a coalition game to improve their data importance, where the equilibrium of the coalition game is obtained by designing a coalition formation algorithm. After that, by considering the diversified characteristics of data and the available resources of different VNs in each coalition, a coalition-based federated learning algorithm is designed to enable the distributed coalitions to complete the learning task cooperatively with the target of improving the learning accuracy. The simulation results show that the proposed scheme outperforms the benchmark schemes in terms of the IoD obtained by the VNs and the training accuracy.

Unmanned aerial vehicle (UAV) relay networks with flexible and controllable characteristics are expected to complement the capacity of the gNB. This paper studies the multiplexing of enhanced Mobile BroadBand (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC) in a multi-UAV relay network, where the strict latency requirement of URLLC can be achieved by the preemptive multiplexing of eMBB resources. However, this may affect eMBB reliability due to the transmission interruptions. Moreover, given the limited energy resources of UAVs, there is an inherent tradeoff among reliability, delay, spectral efficiency, and energy efficiency. To address these challenges, this paper develops a hierarchical UAV-assisted eMBB/URLLC multiplexing scheduling framework. For the eMBB scheduler, we first utilize multiple UAVs to assist the gNB in relaying eMBB traffic and formulate the eMBB resource allocation problem as an optimization problem. Then, we propose a decomposition-relaxation-optimization algorithm to maximize eMBB data rates while considering the personalized fairness of resource allocation and UAV power consumption. For the URLLC scheduler, we further consider the multiplexing of eMBB/URLLC traffic based on the optimization of eMBB resources. To reduce the performance fluctuations of eMBB, we propose a novel cross-slot strategy to schedule URLLC within two time slots rather than one time slot as in existing works. With this strategy, a deep reinforcement learning-based algorithm is proposed to obtain the optimal strategy for the preemption of URLLC on eMBB. Simulation results show that the proposed algorithms outperform the benchmark schemes in terms of convergence rate, eMBB reliability, personalized resource fairness, UAV consumption, and URLLC satisfaction.

Air-ground Integrated Mobility (AIM), as a disruptive mode of travel, has the tremendous potential to alleviate ground traffic congestion issues substantially. However, the primary challenge in achieving this leapfrog development lies in ensuring driving safety. Receiving collision warnings in time within a limited distance can significantly reduce collision risks, which is crucial for ensuring driving safety in AIM. However, due to challenges in aerial network coverage, ensuring the communication quality of aerial Personal Aerial Vehicles (PAVs) remains difficult, thereby affecting the effective transmission of messages. Furthermore, the integration of ground Connected and Automated Vehicles (CAVs) with aerial PAVs in AIM results in significant differences in user resource requirements. Given the complexity of the AIM environment and the high mobility of PAVs, it is challenging to rapidly and accurately capture user communication quality. Therefore, addressing the differential resource requirements of users in this environment is particularly challenging. To this end, we propose a personalized resource allocation strategy assisted by a Channel Knowledge Map (CKM) in AIM. This strategy aims to meet the personalized resource requirements of users while maintaining the maximum Perception Response Time (PRT), thereby ensuring driving safety. Specifically, the CKM in AIM is constructed to obtain channel states through environment-aware communication. Next, a 3D collision warning system is designed to analyze rigorously the maximum PRT of vehicles under different motion states in avoiding collisions. On this basis, with the help of CKM, the channel knowledge of the user’s location is obtained to quantify the communication and computing resources required by each user to maintain the maximum PRT. Finally, we establish the PRT-driven resource optimization problem and employ Deep Reinforcement Learning (DRL) to seek the optimal resource allocation strategy. Simulation results indicate that the proposed method effectively enhances safety and resource utilization in AIM under resource constraints and uneven distribution.

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.

Multipath transmission brings strong bandwidth aggregation capability for services in wireless networks. Nonetheless, the heterogeneous nature of paths and the motion of terminals results in varying transmission delays, leading to out-of-order (OFO) delivery and transmission quality decrease. Traditional algorithms, limited in their scope, fail to strike a balance between high bandwidth and low OFO extent. Recent studies have focused on utilizing learning algorithms to find a multi-performance joint optimal transmission strategy. In light of this, this paper proposes a framework called DRL-based OFO-Friendly Multipath Scheduling (DOFMS) to ensure high bandwidth and low OFO extent transmission in mobile heterogeneous networks. In particular, the framework introduces a novel OFO evaluation index to assess the degree of OFO more accurately. To achieve elastic scheduling, the framework employs the Double Deep Q Network (DDQN) to dynamically regulate the scheduling ratio. Recognizing the dynamic and unpredictable nature of path delays, an asynchronous module is introduced to enhance learning accuracy. Experimental results demonstrate that the framework reduces the OFO rate by 25% compared to traditional bandwidth aggregation algorithms, while maintaining low bandwidth and packet loss rates. Furthermore, compared to conventional OFO avoidance algorithms, the framework improves bandwidth by 4% and reduces fluctuation by 90%.

Machine learning models are widely applied for vehicular applications, which are essential to future intelligent transportation system (ITS). Traditional model training methods commonly employ a client-server architecture to perform local training and global iterative aggregations, which can consume significant bandwidth resources that are often absent in vehicular networks, especially in high vehicle density scenarios. Modern vehicle users naturally can collaboratively train machine learning models as they are the data owner and have strong local computing power from the onboard units (OBU). In this paper, we propose a novel collaborative learning scheme for mobile vehicles that can utilize the opportunistic vehicle-to-roadside (V2R) communication to exploit the common priors of vehicular data without interaction with a centralized coordinator. Specifically, vehicles perform local training during the driving journey, and simply upload its local model to roadside unit (RSU) encountered on the way. RSU's model will be updated accordingly and sent back to the vehicle via the V2R communication. We have theoretically shown that RSUs' models can eventually converge without a backhaul connection. Extensive experiments upon various road configurations demonstrate that the proposed scheme can efficiently train models among vehicles without dedicated Internet access and scale well with both the road range and vehicle density.

Packet loss inferring can enable a transceiver to distinguish between channel impairment and collision for transmission failures, and thus can improve the network performance by exclusively performing rate adaptation or adjusting the medium access parameter. Machine learning methods from literature have shown great potential in producing models that can detect the loss causes over various network trace, however haven't considered accurate data-driven loss inferring on resource-constrained devices that cannot accommodate deep models. In this paper, we propose a novel packet loss inferring framework that can train lightweight models to distinguish between channel losses and collisions by learning the data trace from both simulation and real devices. Specifically, we first train a sophisticated teacher model based on extensive simulation datasets, whose knowledge is then transferred to a small student model that can be deployed on tiny device. The simulation-reality distillation is conducted via personalized trace from each client correspondingly, whose performance bound is analytically guaranteed. We have implemented our method on real testbed and show that the network access performance can be significantly improved, especially for sudden network variations.

Low Earth orbit (LEO) satellite networks have shown strategic superiority in maritime communications, assisting in establishing signal transmissions from shore to ship through space-based links. Traditional performance modeling based on multiple circular orbits is challenging to characterize large-scale LEO satellite constellations, thus requiring a tractable approach to accurately evaluate the network performance. In this paper, we propose a theoretical framework for an LEO satellite-aided shore-to-ship communication network (LEO-SSCN), where LEO satellites are distributed as a binomial point process (BPP) on a specific spherical surface. The framework aims to obtain the end-to-end transmission performance by considering signal transmissions through either a marine link or a space link subject to Rician or Shadowed Rician fading, respectively. Due to the indeterminate position of the serving satellite, accurately modeling the distance from the serving satellite to the destination ship becomes intractable. To address this issue, we propose a distance approximation approach. Then, by approximation and incorporating a threshold-based communication scheme, we leverage stochastic geometry to derive analytical expressions of end-to-end transmission success probability and average transmission rate capacity. Extensive numerical results verify the accuracy of the analysis and demonstrate the effect of key parameters on the performance of LEO-SSCN. Notably, with common parameter settings, after incorporating the space link, the transmission success probability increases by 886% with a 13 dB predefined signal-to-noise ratio (or signal-to-interference-plus-noise-ratio) threshold. This superior performance is attributed to the fact that the space link uses a wider bandwidth and greater power for signal transmission compared to the maritime link. It’s undeniable that the integration of the space link inevitably incurs additional expenses.

In vehicular edge computing networks, the real-time, on-demand scheduling of scarce network resources for environmental perception, task offloading, computation, and feedback is vital. However, these coupled processes make resource allocation challenging. Moreover, existing real-time channel measurement techniques in complex vehicular topologies present load, accuracy, and customization difficulties. To address these issues, this paper proposes an on-demand environmental perception and resource allocation strategy. Specifically, with the introduction of a channel knowledge base, we first analyze the coupling relationship between environmental perception, communication, and computation. A model is then proposed for task offloading to schedule the granularity of environment perception, communication resources, and computational resources dynamically. Subsequently, the resource allocation problem is formulated as an optimization problem, aiming to minimize system processing delay and maximize resource utilization while ensuring perception accuracy. To address this, a two-phase optimization-assisted deep reinforcement learning (DRL) algorithm is proposed. The initial phase uses convex optimization to approximate a solution. The second phase proposes a DRL-based algorithm to intelligently schedule dynamic network resources, with the first phase’s solution guiding the initial exploration space to enhance DRL training efficiency. Extensive simulation experiments verify the effectiveness of our proposal.

Semantic communication (SC) is one of the promising solutions for future wireless communications due to its superior transmission efficiency. However, the semantic information cannot be transmitted accurately under noisy channels by the existing methods. For this reason, we propose a fairness-based transmission strategy for STAR-RIS assisted SC systems. On this basis, we investigate two operating protocols of STAR-RIS, energy splitting (ES) and mode switching (MS). More specifically, we maximize the minimum signal-to-noise ratio (SNR) of the users by jointly optimizing the active beamforming and the passive beamforming under the constraint of limited power at the base station (BS). To tackle this max-min optimization problem, for ES, we develop a double-loop iterative algorithm by using the successive convex approximation (SCA) and penalty function methods. For MS protocol, we further add an additional penalty in the objective function to address the optimization problem. Moreover, we rigorously prove that the proposed algorithm can converge to a locally optimal solution. At last, we conduct various experiments to verify the performance of the proposed algorithms. Simulation experiments demonstrate that our algorithm outperforms other benchmark methods in fairness and semantic similarity.

Aerial video streaming is a promising application of unmanned aerial vehicles (UAVs), which extends video service from ground to three-dimensional (3D) airspaces. However, high data rates and smooth transmission are required along with ubiquitous and environment-aware communications. To this end, we study the quality of experience (QoE) maximization problem in this paper for aerial video streaming over 3D cellular networks in urban environments with building avoidance. Different from the typical channel model based optimization in prior works, we tackle the joint design of 3D UAV trajectory and transmission scheduling as well as playback rate adaption with an environment and channel knowledge map (ECKM) approach, which provides rich information about the location-specific channel for enabling environment-aware communications. Specifically, we first consider the scenario with perfect ECKM, and propose efficient algorithms to obtain suboptimal solutions by utilizing two graph models and the iterative parameter-enabled block coordinate descent method. For the scenario without such map information, we propose a dueling Deep Q-learning (DQL) solution with map construction such that the learning process can be facilitated for path planning. Simulation results are provided to demonstrate the improvement in QoE by the proposed solutions over baseline schemes, as well as a tradeoff between video quality and rate variation.

In this paper, we investigate a practical structure of reconfigurable intelligent surface (RIS)-based double spatial scattering modulation (DSSM) for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. A suboptimal detector is proposed, in which the beam direction is first demodulated according to the received beam strength, and then the remaining information is demodulated by adopting the maximum likelihood algorithm. Based on the proposed suboptimal detector, we derive the conditional pairwise error probability expression. Further, the exact numerical integral and closed-form expressions of unconditional pairwise error probability (UPEP) are derived via two different approaches. To provide more insights, we derive the upper bound and asymptotic expressions of UPEP. In addition, the diversity gain of the RIS-DSSM scheme was also given. Furthermore, the union upper bound of average bit error probability (ABEP) is obtained by combining the UPEP and the number of error bits. Simulation results are provided to validate the derived upper bound and asymptotic expressions of ABEP. We found an interesting phenomenon that the ABEP performance of the proposed system-based phase shift keying is better than that of the quadrature amplitude modulation. Additionally, the performance advantage of ABEP is more significant with the increase in the number of RIS elements.

This paper proposes a manifold optimization-based tensor recovery algorithm for channel estimation (MO-TRACE) in reconfigurable intelligent surface (RIS)-assisted millimeter wave (mmWave) MIMO-OFDM systems. Specifically, considering the inherent sparse scattering characteristics of mmWave channels, the multidimensional cascaded channel in the angular-delay domain is represented by a sparse and low-rank tensor, and we formulate the channel estimation problem as a sparse and low-rank tensor recovery problem. Then, we use the canonical polyadic (CP) decomposition technique to decompose the tensor into multiple factor matrices, where the factor matrices are related to the channel parameters. To account for the sparse factor matrices, we add the sparse regularization terms of the factor matrices to the optimization objective, which can also reduce the number of nonzero columns in factor matrices, i.e., the rank of the tensor. As the concatenation of multiple factor matrices can be seen as a point on a product manifold, we apply MO algorithms to search for the optimal sparse point with enhanced convergence speed. The proposed MO-TRACE algorithm provides a more precise description of channels and ensures that the iteration point always remains within the feasible domain, thereby enhancing solution accuracy. Simulation results validate the superiority of the proposed MO-TRACE in terms of estimation accuracy.

Federated Learning (FL) is a promising paradigm widely used in privacy-preserving machine learning. It enables distributed devices to collaboratively train a model while avoiding data transfer between clients. Nevertheless, FL suffers from bottlenecks in training speed due to client heterogeneity, resulting in increased training latency and server aggregation lagging. To address this issue, a novel Split Federated Learning (SFL) framework is proposed. It pairs clients with different computational resources based on their computational resources and inter-client communication rates. The neural network model is split into two parts at the logical level, and each client computes only its assigned part using Split Learning (SL) to accomplish forward inference and backward training. Besides, a heuristic greedy algorithm is proposed to effectively deal with the client pairing problem by reconstructing the training latency optimization as a graph edge selection problem. Simulation results show that the proposed method can significantly improve the FL training speed and achieve high performance in both independent identical distribution (IID) and Non-IID data distribution.

As the number of connected and autonomous vehicles (CAVs) on road networks continues to increase, mixed transportation scenarios where CAVs and human-driven vehicles (HDVs) coexist are becoming more common. Establishing dedicated lanes (DLs) for CAVs is crucial for managing mixed traffic and improving road capacity. In this paper, we provide a theoretical analysis of the relationship between the market penetration rate (MPR) of CAVs and road capacity in both single-lane scenarios and multiple-lane scenarios with DLs. We derive a critical MPR for CAVs, at which they can be seamlessly accommodated within the DLs. Our numerical results show that CAVs should be prioritized to enter DLs first to optimize road capacity in mixed traffic. We also derive and validate the road capacity in multiple-lane scenarios and provide an optimal strategy for setting up DLs under varying MPRs to maximize road capacity. Overall, our study provides valuable insights into the significance of DLs for CAVs in mixed traffic and offers guidance on their implementation to improve road capacity.

In Space-air-ground integrated networks (SAGIN), the inherent openness and extensive broadcast coverage expose these networks to significant eavesdropping threats. Considering the inherent co-channel interference due to spectrum sharing among multi-tier access networks in SAGIN, it can be leveraged to assist the physical layer security among heterogeneous transmissions. However, it is challenging to conduct a secrecy-oriented access strategy due to both heterogeneous resources and different eavesdropping models. In this paper, we explore secure access selection for a scenario involving multi-mode users capable of accessing satellites, unmanned aerial vehicles, or base stations in the presence of eavesdroppers. Particularly, we propose a Q-network approximation based deep learning approach for selecting the optimal access strategy for maximizing the sum secrecy rate. Meanwhile, the power optimization is also carried out by an unsupervised learning approach to improve the secrecy performance. Remarkably, two neural networks are trained by unsupervised learning and Q-network approximation which are both label-free methods without knowing the optimal solution as labels. Numerical results verify the efficiency of our proposed power optimization approach and access strategy, leading to enhanced secure transmission performance.

Task offloading is a potential solution for computation-intensive vehicular applications due to limited on-board computing resources. However, traditional model-driven methods are hindered by long online processing time, while data-driven methods are deficient in interpretability and generalizability. To overcome this challenge, this paper formulates the resource allocation for task offloading in connected autonomous vehicles (CAVs) as a multi-objective optimization problem, and proposes a novel knowledge-driven algorithm that integrates both model-driven and data-driven methods. Specifically, the framework of a model-driven alternating minimization (AM) algorithm, which solves the formulated problem via alternatively optimizing power allocation subproblem and bandwidth and CPU frequency allocation subproblem, is regarded as knowledge. Inspired by such knowledge, our proposed knowledge-driven neural network consists of two long short term memory networks (LSTMs) to alternatively updating these two subproblems. Furthermore, to get away from the local optimum usually occurred in the AM algorithm, our proposed knowledge-driven neural network updates network parameters with the global loss function. Simulation results demonstrate that our method outperforms both the AM algorithm and the LSTM without knowledge.

Low Earth Orbit (LEO) satellite networks are gaining more interest as a crucial component of future space-air-ground integrated networks. However, the traditional IP-based communication mode is not well-suited for supporting low-cost and highly reliable routing in inter-satellite packet transmission. On one hand, the centralized IP address allocation model increases server resource consumption and also leads to excessive communication between satellites. On the other hand, the single-path routing feature of IP cannot guarantee timely recovery of the path in the event of a satellite node failure. Therefore, this paper proposes a mechanism called Relative Position-based Efficient Routing (RP-ER) for LEO satellite networks. RP-ER can achieve distributed address allocation at a low cost and enable redundant routing in the event of a path failure. In particular, RP-ER first establishes the relative position model based on the laws of satellite motion. Then, the central satellite broadcasts the address allocation instructions, and each satellite reacts and disperses packets. Finally, these satellites allocate independent addresses and generate primary and backup routes simultaneously. Compared to other routing mechanisms, RP-ER utilizes fewer satellite resources during the network addressing phase. Additionally, it can establish redundant high-quality paths during the communication phase with a concise routing table.

Multipath transmission can effectively utilize multiple paths and provide high Quality of Service (QoS) performance for video streaming services. However, when multiple video streaming services are transmitted simultaneously, the network is prone to the preemption of path resources by these services, which can reduce QoS. This is because the traditional multipath scheduling algorithm aims to achieve high QoS performance for all services. Therefore, this paper proposes a Friendliness Balancing based Multipath Scheduling algorithm (FBMS) to maximize the utilization of path resources and achieve a friendly and balanced consumption of network resources. First, FBMS obtain the path resources and service requirements to build adaptation matrices. Then, FBMS considers the friendliness balancing value as the optimization objective and utilizes a two-stage evaluation-based Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) algorithm to assess each path. Finally, FBMS preferentially selects a single transmission path that meets the service requirements in order to avoid resource competition. If there is no qualified path, FBMS will balance the demands of each service, integrate them friendly, and schedule multiple paths for transmission. Experiments show that, compared to traditional scheduling algorithms, FBMS improves path resource utilization, reduces competition among services, and ensures high QoS performance for each service.

With the development of economic globalization and emerging applications such as transnational communication and webcasting, inter-cloud link transmission has become a key factor for green networking. Optimized transmission policies can effectively reduce network energy consumption and improve resource utilization by dynamically adjusting the optimal network paths. In this paper, we collect first-hand and over-million-level network datasets from practical nodes on three continents (Asia, Europe, and North America), and analyze the characteristics of international links among cloud centers in different continents. According to the actual test results, we discover that the network transmission quality strongly correlates with factors such as time zone and cloud service provider. Besides, we conclude that the quality of inter-cloud networks can be effectively improved according to regional activity and triangular routing. Based on the in-depth observation, we further discuss the optimization directions of network transmission and provide relevant suggestions. This work is of significant reference value for research on green transmission of inter-cloud links.

Urban air mobility (UAM) provides a new solution to relieve urban transportation pressure by expanding transportation resources of near-ground space. The vigorous development of emerging technologies such as artificial intelligence, intelligent transportation, and sixth-generation (6G) communication technologies have greatly promoted the progress of UAM. However, UAM also increases traffic safety hazards while introducing vertical dimension transportation resources. Traditional collision avoidance is not suitable for three-dimensional (3-D) air-ground integrated mobility scenario, which considers safety hazards in vertical dimensions as well as the resource supply and demand conflict due to the combined effect of directional antenna angle and limited communication distance. Therefore, a safety-oriented on-demand resource allocation strategy for air-ground integrated mobility is proposed. Specifically, we first model the 3-D safety distance model in the air-ground integrated mobility scenario and construct its quantitative relationship with communication and computing resources. Secondly, a 3-D safety distance optimization model is proposed with joint consideration of safety-oriented resource requirements and resource distribution, which can allocate resources in the scenario. Furthermore, a 3-D safety distance optimization algorithm based on deep reinforcement learning (DRL) is designed for solving the optimization model, which implements a safety-oriented resource allocation. Simulation results show that the proposed safety control strategy can effectively improve the safety of air-ground integrated mobility and alleviate the contradiction between the supply and demand of resources.

This work studies optimal solar charging for solar-powered self-sustainable UAV communication networks, considering the day-scale time-variability of solar radiation and user service demand. The objective is to optimally trade off between the user coverage performance and the net energy loss of the network by proactively assigning UAVs to serve, charge, or land. Specifically, the studied problem is first formulated into a time-coupled mixed-integer non-convex optimization problem, and further decoupled into two sub-problems for tractability. To solve the challenge caused by time-coupling, deep reinforcement learning (DRL) algorithms are respectively designed for the two sub-problems. Particularly, a relaxation mechanism is put forward to overcome the “dimension curse” incurred by the large discrete action space in the second sub-problem. At last, simulation results demonstrate the efficacy of our designed DRL algorithms in trading off the communication performance against the net energy loss, and the impact of different parameters on the tradeoff performance.

As an integral component of space-air-ground integrated networks (SAGINs), the low Earth orbit (LEO) satellite networks have displayed immense potential in providing ubiquitous connectivity and broadband mobile communication. However, the intrinsic dynamics of LEO satellites poses unprecedented challenges in network management, multi-dimensional resource scheduling, and service delivery. In this paper, we study the service function chain (SFC) orchestration in dynamic LEO satellite networks, with the aim of achieving flexible and efficient service provision. Considering the service requirements and the load fairness of LEO satellite networks, we formulate the SFC deployment problem as an integer nonlinear programming (INLP) problem. We then introduce a load-aware SFC orchestration algorithm to improve serving capacity and load fairness. Additionally, we address the issue of SFC migration in dynamic LEO satellite networks to ensure service continuity. To minimize the service interruption and network resource wastes, a Tabu search (TS)-based approach is presented to optimize the virtual network function (VNF) migration. Simulation results demonstrate that our proposed approaches outperform the benchmark by a substantial margin in terms of load fairness, without compromising service acceptance.

The utilization of machine learning models has become prevalent in various wireless devices to deduce the network status from various tracing data, e.g., link capacity, channel fading, etc. To cope with the increasing complexity of the network environment, deep neural models are leveraged to mine the high-dimensional network tracing data for a variety of intelligent applications. However, due to the limited resource that allocated to the network stack process, it is infeasible to train deep neural models due to the constrained computing power and absence of large-scale labeled data. Besides, the network device can barely support quick inference of large model, thus cannot support promptly response to the network conditions. In this paper, we propose a practical fast model inference system that can run high accuracy model over tiny wireless devices that are constrained in both memory and CPU power. Specifically, we design a knowledge-distillation based training method for a light-weight model that deployed at device side that can migrate the knowledge from a well-trained deep model. It is shown that our system can support fast model inference over tiny devices, which can greatly improve the network throughput in a multi-user access system by inferring the transmission collision from channel error, and thus can improve the accuracy of the link adaptation. We have conducted practical experiments to verify our system and discuss the possible extensions.

Edge intelligence and federated learning (FL), as key enablers of 6G, is a promising solution for networked Autonomous Driving (NAD). However, traditional federated learning is a server-client architecture, which makes the model training overly dependent on a fixed single aggregation server and makes the FL process insecure and unreliable due to the vulnerability of the aggregation server to a single point of failure. In this paper, we propose a rotating server FL scheme (RSFL) to solve the problem of single point of failure and limited resources and improve environmental adaptability. Specifically, we consider multiple factors to measure the vehicle performance and find the vehicle with the highest performance score in this round as the server for the next round while setting weights that are randomized in each round, which reduces even more the likelihood that a malicious user will recognize the regularity of the chosen server. Finally, the performance of RSFL is evaluated through a large number of experiments, and the results show that compared with baseline FL, FL with randomly selected servers, and peer-to-peer decentralized FL, RSFL can effectively reduce the cases of servers being detected and attacked by malicious adversaries, and improve the accuracy of the model.

Connected and Autonomous Vehicles (CAVs) provide significant societal benefits but pose serious security risks due to their high connectivity and openness. Traditional security measures like cryptography and intrusion detection systems (IDSs) are reactive and passive, posing significant challenges to securing CAVs. We propose a proactive and collaborative threat-sharing framework to tackle the above challenges and enhance CAV security through vehicular honeypots. The proposed framework leverages federated learning, which allows CAVs to share threat information decentralized while preserving their privacy. Additionally, we design an optimal incentive mechanism that considers three private information of CAVs, including deployment, training, and communication costs. Specifically, we leverage the self-disclosure property of the contract theory, which can effectively address information asymmetry and incentive mismatches between CAVs and the IDS server, motivating CAVs to participate in threat sharing. Finally, through a series of simu- lation experiments, we validate the feasibility of the contract and evaluate the effectiveness of our proposed incentive mechanism.

As a fundamental problem, many studies are dedicated to the optimization of signal-to-interference-plus-noise ratio (SINR), in a multi-user setting. Although traditional model-based optimization methods achieve strong performance, it has high complexity. To fully leverage the high performance of traditional methods and the low complexity of the neural network (NN) based method, a knowledge distillation (KD) based algorithm distillation (AD) method is proposed in this paper, where traditional optimization methods serve as "teachers" for NN "students", improving unsupervised and reinforcement learning. This approach tackles common issues: unattainable optimal labels, overfitting, and inefficient training. Simulations confirm the advantages of AD, paving the way for traditional optimization integration with NNs in wireless communication.

With the development of communication technology and virtual reality (VR) technology, virtual Metaverse services are gradually entering people’s lives to provide immersive experience. As one of the important travel tools for people, vehicles have the opportunity to become the carrier of Metaverse, thereby enhancing the driving experience and entertainment experience of vehicle users (VUs). However, due to the high-speed movement of vehicles, how to dynamically adapt to environmental changes to allocate transmission and computing resources so that VUs can better experience VR services in the Metaverse has become a challenge. To this end, in this paper, we propose an environment-aware dynamic resource allocation scheme for VR video services in vehicle Metaverse, aiming to efficiently allocate computing and communication resources to maximize the quality of experience (QoE) of VUs when requesting VR video services. Specifically, we first establish the system model which includes network model, communication model, and VR video model. Then, considering the dynamic changes in the driving environment, we design a QoE model for each VU based on its VR video buffer. After that, we design a deep deterministic policy gradient (DDPG) algorithm to optimally allocate communication and computing resources to maximize the QoE of each VU. The simulation results show that our scheme can bring the highest reward to the VUs compared with the benchmark schemes.

Millimeter wave (mmWave) communication requires huge beam training overhead, which is highly undesirable in vehicle-to-infrastructure (V2I) communication, due to the requirement for low latency and high reliability. Sensory information can be utilized to reduce the beam training overhead. However, single-type sensors have limitations that result in inadequate performance when used independently for assistance, while employing multi-type sensors sensors for assistance presents challenges such as data redundancy and a large volume of data. To tackle the aforementioned issues, we propose a multisource low redundancy data-aided beam prediction (MLRDBP) scheme. Specifically, we first extract and fuse features from LiDAR, GPS, and camera data. Then, we employ the principal component analysis (PCA) algorithm to reduce the dimensionality and redundancy of the fused features. Finally, we design a classification model based on a multi-layer perceptron (MLP), training it with the fused low redundancy features to predict the optimal beam direction for communication parties. The simulation results indicate that the proposed scheme achieves the accuracy exceeding 76.9% for top-1 beam prediction, offering satisfactory performance with lower data redundancy compared to single-sensor-aided schemes and other existing schemes.

By offloading computation-intensive tasks of vehicles to roadside units (RSUs), mobile edge computing (MEC) in the Internet of Vehicles (IoV) can relieve the onboard computation burden. However, existing model-based task offloading methods suffer from heavy computational complexity with the increase of vehicles and data-driven methods lack interpretability. To address these challenges, in this paper, we propose a knowledge-driven multi-agent reinforcement learning (KMARL) approach to reduce the latency of task offloading in cybertwin-enabled IoV. Specifically, in the considered scenario, the cybertwin serves as a communication agent for each vehicle to exchange information and make offloading decisions in the virtual space. To reduce the latency of task offloading, a KMARL approach is proposed to select the optimal offloading option for each vehicle, where graph neural networks are employed by leveraging domain knowledge concerning graph-structure communication topology and permutation invariance into neural networks. Numerical results show that our proposed KMARL yields higher rewards and demonstrates improved scalability compared with other methods, benefitting from the integration of domain knowledge.

With the dispersed and privacy-preserving features, federated learning (FL) enables connected and autonomous vehicles (CAVs) to achieve cooperative perception, decision-making, and planning by utilizing the learning capabilities and sharing model parameters. However, the discrepancies in local training cost and model upload durations between various CAVs make the energy and time costs caused by traditional FL algorithms unfair. In this paper, a fair and efficient FL algorithm is proposed with to address the challenges arising from imbalanced data distribution and fluctuating channel conditions. Specifically, to achieve uniformity in total time and energy cost among CAVs, a personalized approach is employed for the local training rounds of each CAV. This approach ensures fairness and training effectiveness while reducing the local training time in each round of global iteration. Furthermore, it enhances the convergence speed of the global model. Extensive simulations demonstrate that the proposed algorithm achieves fairness in energy cost while reducing the duration of each round of global iteration.

Accurately forecasting the future motions of road participants is essential for proactive hazard avoidance and safety planning of autonomous vehicles. Existing methods for motion prediction based on probabilistic generative models are limited to low-accuracy likelihood calculations and relatively finite mode distributions. Recent studies show that score-based models can naturally overcome these limitations. In this work, we present a novel paradigm of conditional score-based models for vehicle motion prediction, called Motion-CSM. First, we model scene contextual representations of interaction regions at the feature level via graph convolutional networks. We then interpolate these representations as conditions into the solution process of the continuous-time reverse stochastic differential equation (SDE) to guide trajectory generation, which progressively converts the known prior distributions into multimodal trajectories including the ground truth modes. The designed stacked Transformer structure with dual control conditions is adopted to learn the score function approximation of the Gaussian perturbation kernel. Finally, we develop multiple consistency constraints to align the inference results of Motion-CSM in reverse SDE solving to improve the self-consistency and stability of multimodal trajectory generation. Experimental results on the real-world motion dataset demonstrate that the multimodal forecasting accuracy of Motion-CSM outperforms state-of-the-art methods.

In digital twins enabled space-air-ground integrated networks (DT-SAGINs), the DT of a vehicle (DT-V) needs to constantly migrate between the infrastructures deployed on the path of the vehicle as the vehicle moves to provide stable and continuous driving services for the vehicle. However, each DT-V has differentiated migration requirements and the heterogeneous network infrastructures have various migration performances. Therefore, how to design a scheme that jointly considers the above factors to determine the optimal migration strategy for each DT-V becomes a challenge. In this paper, we propose a game-based migration scheme for the DT-Vs in DT-SAGINs. In this scheme, we first design a two-layer DT migration architecture, where each vehicle has two DTs and each network infrastructure only has one DT. The two DTs of the vehicle are respectively deployed in the cloud layer (Primary DT-V) and the edge layer (Second DT-V). In contrast, the DT of each network infrastructure is deployed in the cloud layer (DT-I). Based on the designed architecture, the interaction of the Primary DT-Vs and the DT-Is deployed in the cloud layer is formulated as a matching game, where an integrated algorithm that couples bilateral matching and dynamic programming is designed to obtain the optimal migration strategy for each Second DT-V deployed in the edge layer to maximize its average utility. The simulation results show that the proposed scheme can lead to a higher utility for each Second DT-V than the conventional schemes.

Mobile Edge Computing (MEC), as a promising key technology, provides tremendous support for latency-sensitive applications in Internet of Vehicles (IoV). In this paper, we focus on the MEC-assisted computation offloading problem for mixed traffic scenarios that autonomous and human-driven connected vehicles coexist. With the objective of minimizing system average latency, a priority-based serial and parallel joint offloading scheme is designed and formulate the optimization problem as a Markov decision process (MDP). Then, we propose an adaptive offloading strategy based on deep reinforcement learning. Simulation results compared to contrast algorithm and baseline schemes demonstrate the superiority of the proposed priority-based offloading scheme, effectively reducing the system average latency and ensuring the latency requirements of latency-sensitive tasks.

Passive localization is commonly achieved through the direction finding and positioning technique, which uses a airborne or ground multi-station angle measuring system to intersect pointing lines for fast and omnidirectional positioning. However, as the number of targets increases, so does the occurrence of false points. This poses a challenge to the positioning performance of system, requiring the prompt elimination of false points. To address the issue, we propose a high detection rate passive localization method based on density peak clustering (DPC). In this method, a suitable non-ideal location model is established, and improved density peak clustering is utilized to achieve data association and target localization. Simulation results confirm the proposed positioning method’s superior performance and adaptation to the non-ideal conditions of multi-target localization.

Space-air-ground integrated networks (SAGIN) have been envisioned as the promising and key network architecture for the 6G vehicular networks to provide seamless coverage for the connected vehicles. To access the most appropriate network quickly, this paper proposed a knowledge-driven network access approach, where the communication knowledge is explicitly integrated into neural networks, to deal with multiple tasks in SAGIN-based vehicular networks. Specifically, the formulated long-term network access problem is handled by asynchronous advantage actor-critic algorithm (A3C) in reinforcement learning. During this process, the space-time correlation knowledge is introduced to effectively reduce the action space in channel selection and the reward shaping exploiting the problem-specific communication and mathematical knowledge is adopted to solve the sparse reward problem in reinforcement learning. In addition, by modifying the sub-net learning rate of the A3C algorithm with experimental experience, this paper speeds up the network convergence speed by 1.5%. Numerical results also show that integrating knowledge into traditional deep reinforcement learning algorithm can improve the reward by 4%.

The fifth-generation network (5G) has made great progress. With the continuous development of communication technology, by analyzing the characteristics of 5G scenarios, the sixth-generation network (6G) technology combined with multiple scenarios provides effective solutions for the implementation of emerging services with stringent requirements. It is worth noting that the vigorous development of emerging services has been weakened due to the limited resources provided by a single scenario, cross-scenario technologies are urgently needed to enable emerging services in the 6G stage. However, most of the existing work only focuses on a single scenario, which leads to emerging services with complex requirements still difficult to achieve in practice. Therefore, we propose an efficient representation and scheduling framework to achieve the unification of cross-scenario resources, aiming to solve the problem of resource scheduling in cross-scenario. In the above framework, first of all, considering the strict resource requirements of emerging services, we establish a unified resource representation model based on the Time-Expanded Graph (TEG). Secondly, to maximize resource utilization, based on the representation model, a cross-scenario resource scheduling model is proposed. Then, considering the complexity of solving the scheduling model, a resource utilization maximization strategy is presented through the primal decomposition. Simulation results show that the unified framework can effectively improve resource allocation efficiency in complex 6G scenarios.

The ever-growing Internet of Things (IoT) provides a powerful means for complex and changeable environmental monitoring. Directional sensor networks (DSNs), as a typical architecture of IoT, can efficiently facilitate various digital and intelligent IoT applications. In the DSNs, due to the asymmetry in coverage focus and diversity in detection angle of the directional IoT sensors, how to enhance the coverage performance with the limited sensors becomes a new challenge. To this end, we develop a novel sensor redeployment scheme based on the minimum exposure path (MEP) to optimize the coverage performance of the DSNs. Specifically, we first propose a minimum exposure path searching algorithm based on the particle swarm optimization (MEP-PSO) algorithm with the target of obtaining the MEP in the DSNs. With this algorithm, the traditional MEP problem can be analyzed and simplified by conducting the grid discretization and building the weighted undirected graph. Then, an MEP-based coverage optimization (MEP-CO) algorithm is proposed to determine the optimal deployment locations and the dispatch sensors so that the IoT sensors can be dynamically redeployed to achieve the coverage optimization. After that, we derive the formula for the coverage upper bound (CUB) and develop a CUB algorithm to provide a benchmark for evaluating the effectiveness of different coverage optimization algorithms. Simulation results demonstrate that the proposed coverage optimization scheme can significantly promote the minimum exposure value (MEV) and coverage ratio of the monitoring area compared with the existing algorithms.

With the widespread application of wireless networks, the importance of intelligent analysis of network behaviors is becoming increasingly prominent. In the analysis of networks behaviors, learning and reasoning about the connectivity of unknown networks is a fundamental problem. To obtain the topology information of a noncooperative wireless network that could not be accessed by the monitoring sensors, we propose a topology inference algorithm based on the network two-dimensional spatiotemporal features (TDSTFs). Specifically, the monitoring sensor network monitors the power of the noncooperative network and locates the nodes of the noncooperative network exploiting the neural network (NN)-based method. Then, the communication time and distance between the noncooperative nodes are used as characteristics to infer the topology of the noncooperative network based on$K$-nearest neighbors (KNNs). Simulation results validate that the proposed TDSTF topology inference algorithm outperforms other topology inference algorithms that do not consider both spatial and temporal features and can greatly improve the inference accuracy.

With the rapid development of the Internet of Things (IoT), there are a dramatically increasing number of devices, leading to the fact that only using terrestrial infrastructure can hardly provide high-quality services to all devices. Due to their flexibility, maneuverability, and economy, unmanned aerial vehicles (UAVs) are widely used to improve the performance of IoT networks. UAVs can not only provide wireless access to IoT devices in the absence of a terrestrial network but can also perform rich IoT services and applications such as video surveillance, cargo transportation, pesticide spraying, and so forth. However, due to the high complexity, dynamics, and heterogeneity of the UAV-assisted IoT networks, growing attention has focused on using artificial intelligence (AI)-based methods to optimize, schedule, and orchestrate UAV-assisted IoT networks. In this article, we comprehensively analyze the impact of applying advanced AI architectures, models, and methods to different aspects of UAV-assisted IoT networks, including key IoT technologies, tasks, and applications. In addition, this article also explores challenges and discusses potential research directions of AI-enabled UAV-assisted IoT networks.

The heterogeneous vehicular networks (HetVNets) can accelerate the deployment of Internet of Vehicles (IoV) and enrich the content distribution methods. However, the diverse requirements of vehicular users (VUs), the limited cache resources of roadside units (RUs), and the frequent interactions between VUs and RUs pose great challenges to efficiently distribute contents. To address these challenges, we propose an on-demand content delivery scheme in digital twin-enabled HetVNets (DT-HetVNets). Specifically, we first design an on-demand content delivery architecture in DT-HetVNets which uses DT communication mode to simplify the frequent interactions between VUs and RUs. With this architecture, by jointly considering the popularity of each content and the relevance between different contents, the personal content requirement of each DT of VU (DT-VU) can be perceived and the VUs within the coverage of the same RU can collaboratively request contents in groups. Then, we formulate the interaction between each group and the DT of the RU (DT-RU) as a double auction game to determine the transaction price of the perceived content, where the request information of the contents which are accepted by the groups can be shared between different DT-RUs based on the path of each group, enabling collaborative content recommendation between the RUs. After that, by jointly considering the contents recommended by different DT-RUs and the content popularity, the content caching model of each DT-RU is formulated as a knapsack problem, where a collaborative content caching algorithm is designed to obtain the optimal caching strategy with the target of making full use of the limited cache resources. Compared with the conventional schemes, the simulation results show that our scheme can not only bring the highest utility to the RUs but also lead to the highest hit ratio and the lowest delay.

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.

Certificate revocation checking (CRC) is a fundamental requirement in certificate-based public-key cryptographic systems. Most existing CRC schemes are not tailored for edge-cloud computing systems, and directly applying these schemes would cause security and efficiency problems. In this article, we first propose a two-layer edge-cloud-assisted CRC framework, dubbed ECA-CRC, where edge nodes utilizing a probabilistic checking algorithm serve as a first layer, and the cloud server utilizing a deterministic checking algorithm serves as a second layer. Both the edge nodes and the cloud server collaboratively provide verifiable CRC services for devices. The most prominent manifestations of ECA-CRC are that: 1) most CRC requests can be processed with the probabilistic checking layer, which reduces the checking delay significantly while providing an accurate CRC service and 2) devices can detect the irresponsible behavior of the service provider, including using an incorrect revoked certificate set (RCS) to compute checking results or procrastinating on updating the RCS, as soon as possible. We then propose an efficient instantiation of ECA-CRC, dubbed eECA-CRC, by utilizing a Merkle hash tree (MHT)-based homomorphic signature, Cuckoo filter, and Othello. We formally prove the security of eECA-CRC against the irresponsible service provider under the random oracle model. We implement an eECA-CRC prototype and conduct a comprehensive performance evaluation based on a public certificate database. Our results show that 95% of CRC requests are completed on the edge nodes, and only 5% of CRC requests need to be handled by the cloud server.

The newly emerging intelligent reflecting surface (IRS) with large-scale passive reflecting elements has great potentials to enhance the performance of wireless-powered Internet of Things (IoT) networks, by manipulating the wireless channel. However, most of the existing works considered the ideal reflection of IRS elements with independent amplitude and phase shift. In this article, an IRS-assisted wireless-powered multiuser multi-input-multi-output network is considered, taking into account the practical coupling effect between the reflecting amplitude and the phase shift. Then, an uplink sum-rate maximization problem is investigated by jointly designing the active beamforming of multiple antennas, the passive beamforming of the IRS, and the time allocation ratio. Due to the tightly coupled optimization variables, the formulated problem is nonconvex. To effectively solve this problem, we decompose it into three subproblems, i.e., the active beamforming, the downlink passive beamforming, and the uplink passive beamforming. For the active beamforming design, access point’s optimal downlink energy beamforming matrix is proved to be rank-one, and IoT users’ optimal uplink information covariance matrices are derived in semi-closed forms. For the downlink passive beamforming design, a low-complexity algorithm based on the successive convex approximation and the penalty function method is proposed. For the uplink passive beamforming design, the multiuser problem is equivalently transformed into a virtual single-user problem, which is solved via an iterative algorithm. Numerical results show that, in comparison with algorithms without IRS, our proposed algorithm can significantly improve the uplink sum rate up to 50% when the number of passive elements is 100.

The autonomous vehicles (AVs), as intelligent mobile robots, can undertake tasks to facilitate various computation-intensive services in intelligent transportation system (ITS). Due to hardware device failures or environmental identification errors, the AVs controlled by intelligent algorithms may cause accidents during driving. However, the existing studies in the post-accident stage lack the analysis of the impact degree of the accidents and the computing tasks undertaken by the AVs to determine the optimal maintenance strategy. In this article, we consider the accidents in a continuous period of time and design a digital twin (DT)-enabled post-accident maintenance scheme. Specifically, by considering the computing tasks undertaken by the AVs and the impact degree of the accidents, we first design a DT-enabled post-accident maintenance architecture. With the designed architecture, an optimal maintenance method under an incomplete information scenario is then proposed to help each accident AV decide its optimal maintenance strategy. Besides, based on the maintenance strategies of the AVs and the capacities of the maintenance service providers (MSPs), the two-way selection problem between the AVs and the MSPs in the continuous period of time is modeled as a dynamic matching game to obtain the optimal AV-MSP pairs. Simulation results demonstrate that the proposed scheme outperforms the benchmark schemes in terms of the maintenance rate of the accident AVs, the average utility of the MSPs, and the average social welfare.

In this paper, we investigate the secure transmission of multi-resource heterogeneous radio access networks (RANs) in space-air-ground integrated network (SAGIN) from the perspective of physical layer security. Considering the network heterogeneity, resource constrain, and channel similarity, it is challenging to implement the physical layer security in SAGIN. Particularly, digital twin (DT) is considered in the cyberspace of SAGIN to reflect the physical network entities (i.e., satellite, unmanned aerial vehicle (UAV), and terrestrial base station), which is assumed to comprehensively control and manage the heterogeneous RANs’ resources. To ensure secure transmissions of multi-tier heterogeneous downlink communications in SAGIN, a multi-point symbiotic security scheme is proposed through DT-assisted multi-dimensional domain synergy precoding, where the co-channel interference due to spectrum sharing among these heterogeneous RANs is recast to unevenly corrupt the main and wiretap channels of each legitimate user. Specifically, to realize the multi-point symbiotic security, a max-min problem is formulated to maximize the minimum secrecy rate of three heterogeneous downlinks. Since this problem is non-convex and challenging, a list of mathematical reformulations is derived and the successive convex approximation (SCA) based multi-dimensional domain synergy precoding algorithm is proposed to solve it. Moreover, the computational complexity of our proposed approach is analyzed and meaningful discussions are made. In addition, extensive simulations are carried out to evaluate the secrecy rate performance and verify the efficiency of our proposed approach.

With the growing number of cars in cities, smart parking is gradually becoming a strategic issue in building a smart city. As the precondition in smart parking, accurate parking prediction can reduce the time drivers spend searching for parking spaces and relieve traffic congestion. Meanwhile, VANET and the Internet-of-things (IoT) are the key elements of the current intelligent transportation system. With the IoT devices based on VANET becoming more extensively employed, a large amount of parking data is generated every day, and various methods are proposed for parking prediction, therefore, it is time to systematically summarize the parking prediction issues and the state-of-the-art prediction methods. In this survey, we first provide a comprehensive review of the existing methods used for parking prediction ranging from conventional statistical methods to the latest graph neural network methods. Then, we classify a variety of parking problems such as parking availability prediction, parking behavior prediction, and parking demand prediction. We also compile all the evaluation metrics, open data, and open-source code of the surveyed literature. Finally, we present the challenges and future directions of the parking prediction technique. As far as we know, this is the first survey exploring parking prediction methods, which will be of interest to both researchers and practitioners engaging in intelligent transportation systems (ITS) and smart cities.

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.

Due to the wireless broadcasting and broad coverage in satellite-supported Internet of things (IoT) networks, the IoT nodes are susceptible to eavesdropping threats. Considering the distance difference between satellite and nearby destinations is negligible, the main and wiretapping channels between satellite and IoT node are similar, it poses great challenges to reach physical layer security in satellite-assisted IoT networks. In this paper, to guarantee secure transmissions for satellite-assisted IoT downlink communications, the multi-domain resource multiplexing based secure approach is proposed. Particularly, the self-induced co-channel interference between adjacent nodes is leveraged to increase the difference of signal transmission quality over both main and wiretapping channels. By comprehensively optimizing multi-domain resources, i.e., frequency, power, and spatial domains, secure transmissions from satellite to IoT nodes are reached. Specifically, the problem to maximize the sum secrecy rate of IoT nodes is formulated with a constraint of common communication rate of IoT nodes. To solve this non-convex problem, an alternating optimization (AO) algorithm with two inner successive convex approximation (SCA) algorithms are executed to solve the power allocation, spectral multiplexing, and precoding. In addition, simulation results are carried out to evaluate the secrecy rate performance and verify the efficiency of our proposed approach.

Nowadays, the ultra-dense low earth orbit (LEO) satellite network has become a promising paradigm in the next generation mobile communication network. With the development of content centric communication, multicast technology also attracts much attention. In this paper, we consider the downlink multicast transmission in the ultra-dense LEO satellite network. Multiple LEO satellites provide multicast service for multiple ground user (GU) groups under their coverage, where each GU group requests the same content. To improve the multicast performance, we propose an optimal subchannel allocation and beamforming scheme to maximize the system max-min fair (MMF) capacity of GUs. By leveraging the many-to-many matching model, we obtain the optimal subchannel allocation solution, and we propose a successive convex approximation (SCA) based algorithm for the downlink beamforming in the matching process. The many-to-many matching algorithm is convergent to a stable solution after finite iterations. Simulation results show the superiority and the effectiveness of the proposed subchannel allocation and beamforming method compared with other baseline schemes.

Caching popular contents at the base station (BS) in maritime Internet of Things (IoT) networks makes sensor nodes be free from frequently responding to user requests, which can remarkably save the energy consumption of sensor nodes. However, to ensure the freshness of contents, cached contents need to be updated periodically. Frequent content updating can minimize the age of information (AoI) of contents while increase the energy consumption of sensor nodes. To make a better tradeoff between the AoI and energy consumption, in this paper, both the cache placement and content updating interval are jointly optimized to minimize the weighted sum of AoI of contents and energy consumption of sensor nodes. As the formulated problem is a mixed integer nonlinear programming problem, the cache placement and the content updating interval are alternatively optimized. For the cache placement problem, a local optimal solution is achieved via the binary constraint reformulation and successive convex approximation. For the content updating problem, the optimal solution with semi-closed form is derived. Simulation results show that our proposed algorithm outperforms other benchmarks in terms of the weighted sum of AoI and energy consumption.

In 5G/B5G networks, the preemptive scheduling provides an efficient solution to the coexistence problem of eMBB/URLLC services. Current works usually assume that the downlink transmission duration of each URLLC service is within one mini-slot, which ignores the different requirements of URLLC users and may lead to the severe data rate loss of eMBB services and low resource utilization efficiency. To deal with above problem, we propose a novel URLLC preemptive strategy, where the arriving URLLC services could cross through multiple mini-slots rather than only one to puncture resources on demand. With the proposed strategy, considering the heterogeneous delay requirements of URLLC services and the preemptive influence on eMBB services, an efficient algorithm based on optimized sparrow search is also proposed. Through allocating time and frequency resources occupied by each URLLC service on de-mand, the number of URLLC services supported by the gNB is maximized while the satisfaction of eMBB services is ensured. The simulation results indicate that the proposed algorithm can achieve better performance compared with the benchmark schemes.

Integrated satellite-terrestrial network (ISTN) can provide a continuous service for vehicular users in remote areas with a seamless network coverage. However, considering the difference in the usage costs between satellite and terrestrial networks and the variability of services for latency requirements, it is of great significance to design a cost-effective data offloading decision for reducing network overhead and ensuring task delay requirements. In this paper, we design a cost-effective data offloading mechanism for vehicles in ISTN. The default transmission for remote areas is via the satellite, where the terrestrial networks can offload the data with intermittent coverage in an opportunistic manner due to the vehicle mobility. To model the diversity in service delay requirements, a virtual queue is exploited to capture the residual maximum delay tolerance of each service as time elapses. We formulate the satellite-terrestrial collaborative transmission as a non-linear programming (NLP) problem. To solve the problem, we propose a reinforcement learning (RL)-based data offloading algorithm for real-time decision making. Simulation results show that the RL-based data offloading algorithm reduces the network overhead and outperforms other baseline schemes we proposed.

Vehicle platooning is an effective way to improve the efficiency and safety of transportation systems, in which a group of vehicles maintains a moving pattern by minimizing the tracking error of each vehicle. In this paper, a joint optimization of radio resource allocation for kinetic status information transmission and platoon control is considered under resource-constrained conditions to maintain the targeted inter-vehicle spacing. The formulated problem is approximately solved by the decomposition method, where the radio resource allocation and the platoon control are considered alternatively in two stages. In the first stage, a tracking error based scheduling strategy is presented for radio resource allocation. In the second stage, the control inputs of each vehicle are optimized based on the model predictive control (MPC). Simulation results show that the proposed scheme can achieve the objective of platoon control while having a low tracking error compared with other scheduling strategies.

With the continuous development of autonomous vehicles, telemedicine, digital media and other time-sensitive applications, a soaring number of network services have high demand for the quality of service (QoS) with extra low delay and jitter. Traditional network architecture only offers best-effort services which cannot meet the stringent delay and jitter requirements. In this paper, we propose a resilient-priority queue scheduling algorithm (RPQ) for delay-sensitive services. RPQ can guarantee stable delay in a fine-grained manner. Particularly, on the premise of meeting the delay requirements of high priority streams, RPQ can give consideration to the delay requirements of lower priority streams depending on its resilient scheduling mechanism. We implement RPQ on programmable switch. The experimental results show that RPQ not only guarantees QoS with low delay and low jitter for delay-sensitive streams but also improves network throughput by comparing with the existing solutions, i.e., SP-PIFO and WRR.

This paper focuses on improving the sensor selection performance in the time difference of arrival (TDOA)-based localization scenario with the presence of source estimation error. In existing schemes, a coarse source position is first estimated and regarded as the actual counterpart for selecting optimal sensors. However, if the estimated position deviates from the actual position, the localization accuracy determined by the selected sensor subset will severely degrade. To solve the issue, we devise a distance-related weighted average Cramér Rao lower bound (WA-CRLB) to include the spatial information of the actual position by scattering sampling points around the estimated source position according to its distribution. Then, we formulate a Boolean vector-based sensor selection optimization problem to minimize WA-CRLB and propose a modified iterative swapping greedy (MISG) algorithm. Simulation results show that the proposed MISG algorithm achieves higher localization robustness with the increase of TDOA measurement error strength, and has lower computational complexity compared with the previous semi-definite relaxation (SDR)-based algorithms.

With the advancement of smart cities and the development of heterogeneous vehicular networks (HetVNets), vehicles can collect data and generate valuable information to obtain profits, thus forming a new vehicular self-media paradigm in HetVNets. However, in the HetVNets with potential security risks, the vehicular self-media market lacks the consideration of the values of the data owned by the media data producers (MDPs) and the capabilities of the media data sellers (MDSs) to improve their utilities. To this end, we propose a value-based secure self-media data trading scheme in the HetVNets. Specifically, we first design a vehicular self-media trading mechanism based on smart contracts to provide participants with a safe and reliable transaction environment. Then, we model the interactions between the MDPs and the MDSs as a Stackelberg game by considering the values of various media data and the sales capabilities of different MDPs. After that, we design an iterative method to obtain the optimal game strategies for the MDPs and the MDSs to maximize their utilities. Compared with the traditional schemes, the simulation results show that our scheme can obtain the optimal strategies for the MDPs and the MDSs and bring them the highest utilities.

In mobile edge computing (MEC) systems, data is frequently transmitted between MEC servers and mobile devices for supporting related services. However, critical threats towards data confidentiality and authenticity are raised, where adversaries always attempt to extract data content from the transmission and impersonate others to spread malicious data for profits. In this paper, we propose a blockchain-based authenticated data transmission scheme, dubbed Badge, to establish secure channels between MEC servers and mobile devices. Badge is based on a blockchain-based authenticated hybrid encryption mechanism, which frees MEC servers from maintaining devices’ certificates and allows them to encrypt/decrypt a large volume of data in a highly efficient way. We present security analysis to demonstrate that Badge achieves data confidentiality and authenticity. We conduct a comprehensive evaluation to demonstrate that Badge is efficient and practical to deploy.

Telemedicine services put forward high transmission demands for network transmission, such as low latency and high throughput. However, telemedicine services suffer undesirable latency due to the high re-transmission probability caused by congestion and queuing. To reduce the probability of re-transmission, this paper firstly proposes in-band network telemetry (INT)-based delay-guaranteed transmission framework (IDTF) to make concurrent transmission control. In IDTF, we propose a proximal policy optimization (PPO)-based adaptive multipath concurrent flow scheduling algorithm (PAMA) for control policy adjustment. In detail, PAMA makes a joint minimum optimization with flow scheduling and network resource management to reduce the probability of congestion and long-time queuing. Finally, we implement extensive simulations on a programming protocol-independent packet processors (P4)-based programmable network platform to perform performance analysis. Simulation results show that PAMA outperforms existing classical algorithms in re-transmission rate, round-trip time, and throughput.

We investigate a cooperative federated learning framework among devices for mobile edge computing,named (CFLMEC), where devices co-exist in a shared spectrum with interference. Keeping in view the time-average network throughput of cooperative federated learning framework and spectrum scarcity, we focus on maximize the admission data to the edge server or the near devices, which fills the gap of communication resource allocation for devices with federated learning. In CFLMEC,devices can transmit local models to the corresponding devices or the edge server in a relay race manner, and we use a decomposition approach to solve resource optimization problem by considering maximum data rate on sub-channel, channel reuse and wireless resource allocation in which establishes a primal-dual learning framework and batch gradient decent to learn the dynamic network with outdated information and predict the sub-channel condition. With aim at maximizing throughput of devices, we propose communication resource allocation algorithms with and without sufficient sub-channels for strong reliance on edge servers (SRs) in cellular link, and interference aware communication resource allocation algorithm for less reliance on edge servers (LRs) in D2D link. Extensive simulation results demonstrate the CFLMEC can achieve the highest throughput of local devices comparing with existing works, meanwhile limiting the number of the sub-channels.

In the heterogeneous vehicular networks (HetVNets), the base stations (BSs) can exploit the massive amounts of valuable data collected by vehicles to complete federated learning tasks. However, most of the existing studies consider the scenario of one task requester (TR) and ignore the fact that multiple TRs may concurrently generate their model training requests in the HetVNets. In this paper, we consider the scenario of multi-TR and multi-BS and propose a digital twin enabled scheme for multitask federated learning to address the two-way selection problem between the TRs and the BSs. We first analyze the diversified requirements of the TRs in the HetVNets. Then, we develop a novel model that jointly considers the available training data, the declared price, and the training experience to evaluate the differentiated training capabilities of the BSs. After that, based on the requirements of the TRs and the training capabilities of the BSs, the two-way selection problem between the TRs and the BSs is formulated as a matching game in the digital twin networks, where a matching algorithm is designed to obtain their optimal strategies. The simulation results demonstrate that the proposed scheme can obtain the highest model accuracy and bring the highest utility to the TRs compared with the conventional schemes.

The coupling of unmanned aerial vehicle (UAV) and Internet of Things (IoT) systems can provide an efficient method to collect ground data for the Sixth Generation (6G) networks. Under this UAV-IoT scenario, an intelligent energy balancing strategy should be designed to achieve tunable energy fairness level among all the IoT devices, such that sensors can differ in their lifespans to meet specific application requirements. In this paper, we propose an intelligent $\alpha-$fairness strategy to balance the energy consumption among IoT sensors. Specifically, the heterogeneities among the sensor nodes, i.e., different data loads, diverse residual energy levels, and distinct channel gains, have been taken into consideration. Based on this, an $\alpha-$utility function is designed. In the maximization of the utility function, the bandwidth allocation, transmission power, and the UAV’s trajectory are jointly optimized. In addition, we also demonstrate how to judiciously tune the $\alpha$ value according to the specific application scenarios, thus to achieve different levels of energy fairness and promote the functional longevity of the system to the best effort.

The space-air-ground integrated vehicular networks (SAGIVNs) can efficiently accelerate the deployment of the Internet of Vehicles (IoV) and enrich the content distribution methods in the networks. In this paper, we propose a content delivery scheme in SAGIVNs that integrates sensing, communication, and caching. Specifically, we first perceive the content requests of the vehicles through which the vehicles covered by the same roadside unit (RU) can be facilitated to request the contents collaboratively. Then, based on the location and path of each vehicle, the perceived request information can be transmitted between different RUs, enabling efficient collaborative content recommendation between the RUs. After that, by jointly considering the contents recommended by different RUs, the popularity of each content, and the limited cache resources, the content caching model of each RU is formulated as a knapsack problem, where a dynamic programming method is designed to obtain the optimal caching strategy. Compared with the conventional schemes, the simulation results show that the proposed scheme can lead to the highest hit ratio and the lowest transmission delay.

Status quo autonomous driving mechanisms rely on fusing the multimodal sensing data to integrate the information from onboard units of a vehicle, e.g., lidar, camera, etc., and have yet to consider the information obtained via the inter-vehicle communication, such as the status of neighboring peers. In this paper, we consider to integrate not only the local onboard sensing data, but also the neighboring vehicle information from the vehicle-to-vehicle (V2V) data pipe, which is demonstrated to improve the autonomous driving performance significantly. Specifically, the opportunistic V2V messages are input to a transformer based fusing framework to improve the driving accuracy in both short and long routes in CARLA environment. Unlike previous rule-based mechanisms of dealing with the V2V messages, to the best of our knowledge, the proposed method is the first to integrate the V2V data to the neural network which implicitly induce the waypoints for accurate end-to-end autonomous driving. We conduct extensive experiments, whose results well demonstrate the utility of the V2V information, and can provide useful inspirations for future driving system design.

Task scheduling is a critical problem when one user offloads multiple different tasks to the edge server. When a user has multiple tasks to offload and only one task can be transmitted to server at a time, while server processes tasks according to the transmission order, the problem is NP-hard. However, it is difficult for traditional optimization methods to quickly obtain the optimal solution, while approaches based on reinforcement learning face with the challenge of excessively large action space and slow convergence. In this paper, we propose a Digital Twin (DT)-assisted RL-based task scheduling method in order to improve the performance and convergence of the RL. We use DT to simulate the results of different decisions made by the agent, so that one agent can try multiple actions at a time, or, similarly, multiple agents can interact with environment in parallel in DT. In this way, the exploration efficiency of RL can be significantly improved via DT, and thus RL can converges faster and local optimality is less likely to happen. Particularly, two algorithms are designed to made task scheduling decisions, i.e., DT-assisted asynchronous Q-learning (DTAQL) and DT-assisted exploring Q-learning (DTEQL). Simulation results show that both algorithms significantly improve the convergence speed of Q-learning by increasing the exploration efficiency.

Mobile-edge computing (MEC) sinks computation and storage capacities to network edge, where it is close to users to support delay-sensitive services. However, due to the dynamic and stochastic properties of MEC networks, the deployed services may be frequently migrated among edge servers to follow the mobility of users, which greatly increases the network operational cost. In this article, considering the service migration cost brought by user mobility, we study the joint optimization problem of service deployment and request routing decisions to maximize the long-term network utility of MEC networks. First, we propose a Lyapunov optimization-based online service migration algorithm to decompose the continuous optimization problem into a number of one-slot online optimization problems. Then, to address the NP-hard issue of one-slot optimization, we use a randomized rounding technique to implement service migration and request routing. Furthermore, through a closed-form theoretical analysis, we prove that the proposed algorithm not only greatly meets the local user requests and enables approximate performance guarantees but also adaptively balances the service migration cost and system performance online. Finally, extensive simulations are conducted, which demonstrate that our algorithm can efficiently utilize the storage and computation resources of edge servers, and maximize the long-term network utility while ensuring the stability of service migration cost.

The sixth-generation (6G) wireless communication system is expected to integrate communication, intelligence, sensing, positioning, control, and calculation to adapt to time critical, ultrareliable, and energy-saving data delivery, as well as accurate positioning of personnel and equipment, serving the Internet of Things (IoT). On the one hand, reconfigurable intelligent surface (RIS) can intelligently manipulate radio waves and is considered to be one of the candidate technologies for the 6G wireless communication. Hence, there are more and more surveys on RIS-assisted communications. On the other hand, the potential of RIS in positioning has attracted growing attention, and articles on RIS-assisted positioning have been blown out. Therefore, it is time to review this literature to understand the potential of RIS positioning, research status, and point out the direction for future research. This article first explains the working principle and channel model of RIS and summarizes some characteristics of RIS suitable for positioning. Then, we give a concise review and classification of existing RIS positioning research. Finally, we put forward our views on the future research challenges and attractive directions for RIS-aided wireless positioning technology.

The customization of edge computing services is one of the key research fields in sixth-generation (6G) heterogeneous vehicular networks (HetVNETs). With various personalized requirements of vehicles on computation-intensive applications, how to explore the heterogeneous computing resources in the 6G HetVNETs to guarantee vehicles with the customized Quality of Experience (QoE), therefore, becomes a challenge. In this article, we develop a novel secure scheme to provide personalized edge computing services for moving vehicles (MVs) in 6G HetVNETs. In the scheme, a smart-contract-based secure edge computing architecture is designed by jointly considering the attack models and the characteristics of the 6G network infrastructures (e.g., satellites, drones, base stations, and roadside units), where each network infrastructure manages a number of parking vehicles to complete computing services collaboratively. With this architecture, based on the available computing resources owned by different network infrastructures, the collaborative computing resource allocation algorithm is designed to help each network infrastructure decide a customized service strategy (CSS) to satisfy the QoE of MVs. After deciding the CSSs, a model based on the second price-sealed auction is formulated to describe the competition among the network infrastructures, where the Nash equilibrium of the game is obtained to guide their optimal bidding strategies to obtain the chance for completing the services. The security analysis and the simulation results show that the proposed scheme can defend against the attacks and lead to a lower cost for completing the services than the conventional schemes.

The vehicular crowdsensing, which benefits from edge computing devices (ECDs) distributedly selecting autonomous vehicles (AVs) to complete the sensing tasks and collecting the sensing results, represents a practical and promising solution to facilitate the autonomous vehicular networks (AVNs). With frequent data transaction and rewards distribution in the crowdsensing process, how to design an integrated scheme which guarantees the privacy of AVs and enables the ECDs to earn rewards securely while minimizing the task execution cost (TEC) therefore becomes a challenge. To this end, in this article, we develop a blockchain-based collaborative crowdsensing (BCC) scheme to support secure and efficient vehicular crowdsensing in AVNs. In the BCC, by considering the potential attacks in the crowdsensing process, we first develop a secure crowdsensing environment by designing a blockchain-based transaction architecture to deal with privacy and security issues. With the designed architecture, we then propose a coalition game with a transferable reward to motivate AVs to cooperatively execute the crowdsensing tasks by jointly considering the requirements of the tasks and the available sensing resources of AVs. After that, based on the merge and split rules, a coalition formation algorithm is designed to help each ECD select a group of AVs to form the optimal crowdsensing coalition (OCC) with the target of minimizing the TEC. Finally, we evaluate the TEC of the task and the rewards of the ECDs by comparing the proposed scheme with other schemes. The results show that our scheme can lead to a lower TEC for completing crowdsensing tasks and bring higher rewards to ECDs than the conventional schemes.

Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges between AVs for determining the members in each collaborative group will consume a lot of time and resources. In addition, since AVs have different computing capabilities and costs, the collaboration types of the AVs in each group and the distribution of the AVs in different collaborative groups directly affect the performance of the cooperative driving. Therefore, how to develop an efficient collaborative autonomous driving scheme to minimize the cost for completing the driving process becomes a new challenge. To this end, we regard collaboration as a service and propose a digital twins (DT)-based scheme to facilitate the collaborative and distributed autonomous driving. Specifically, we first design the DT for each AV and develop a DT-enabled architecture to help AVs make the collaborative driving decisions in the virtual networks. With this architecture, an auction game-based collaborative driving mechanism (AG-CDM) is then designed to decide the head DT and the tail DT of each group. After that, by considering the computation cost and the transmission cost of each group, a coalition game-based distributed driving mechanism (CG-DDM) is developed to decide the optimal group distribution for minimizing the driving cost of each DT. Simulation results show that the proposed scheme can converge to a Nash stable collaborative and distributed structure and can minimize the autonomous driving cost of each AV.

The rise of Internet of Things (IoT) systems has enabled us to access real-time information about our surrounding environments. However, IoT data collection in hostile and inaccessible areas without infrastructure supports is a challenging issue due to the inherent physical constraints associated with the tiny sensors. A viable solution to this problem is to use agile and controllable unmanned aerial vehicles (UAVs) to collect the ground data and relay it to the remote cloud for further processing. Under this UAV–IoT scenario, the limited battery supply carried by the sensor must be efficiently utilized so as to prolong the lifetime of the IoT system. Nevertheless, lifetime extension does not merely entail the reduction of the sum energy expenditure of sensors. In this article, we first show that minimizing the sum energy consumption cannot effectively extend the system lifetime due to the imbalance in energy expenditure among sensors, which, in fact, can render early energy depletion for some overburdened sensors. We also reveal a tradeoff between energy efficiency and energy fairness. To tackle this imbalance issue, we then propose an$\alpha $-fairness approach to balance the energy consumption among IoT sensors. Specifically, in our study, the heterogeneities among the sensor nodes—different data loads, diverse residual energy levels, and distinct channel gains, have been taken into consideration. Based on this, an$\alpha $-utility function is designed. In the maximization of the utility function, the bandwidth allocation, transmission power, and the UAV’s trajectory are jointly optimized. In addition, we also demonstrate how to properly set the$\alpha $value according to the specific application scenarios, thus to achieve different levels of energy fairness and promote the functional longevity of the system to the best effort.

Unmanned aerial vehicles (UAVs) serve as aerial base stations to provide controlled wireless connections for ground users. Due to their constraints on both mobility and energy consumption, a key problem is how to deploy UAVs adaptively in a geographic area with changing traffic demand of mobile users, while meeting the aforementioned constraints. In this article, we propose a Quality of Experience (QoE)-driven and energy-efficient adaptive deployment strategy for multi-UAV networks based on hybrid deep reinforcement learning (DRL) to solve the problem of incomplete information game, where the UAVs can adjust their moving directions and distance to serve users who move randomly in the target area. Through the hybrid DRL with centralized training and distributed testing, UAVs can be trained offline to obtain the global state information and learn a completely distributed control strategy, with which each UAV only needs to take actions based on its observed state in the real deployment to be fully adaptive. Moreover, in order to improve the speed and effect of learning, we improve hybrid reinforcement learning, by adding genetic algorithms and temporal difference error-based resampling optimization mechanism. The simulation results show that the hybrid DRL algorithm has better efficiency and robustness in multi-UAV control, and has better performance in terms of QoE, energy consumption, and average throughput, by which average throughput can be increased by 20%–60%.

Offloading computation tasks through cloud–edge collaboration has been a promising way to improve the Quality of Service (QoS) of applications. Usually, cloud server (CS) and edge server (ES) are selfish and rational and, therefore, it is imperative to develop incentive mechanisms, which can encourage idle ESs or the CS to participate in the task offloading process. In this article, we propose a computation offloading method based on the game theory, which is suitable for cloud–edge computing networks. It is considered that the CS has a lot of computation tasks to conduct, and ESs usually have idle computational resources. The CS can offload computation tasks to ESs with idle computational resources to reduce its own cost and pressure, and ESs can profit by selling their computational resources. The interaction between the CS and ESs is modeled as a Stackelberg game, and the proposed game is analyzed by using the backward induction method. It is proved that the game can achieve a unique Nash equilibrium. Then, a gradient-based iterative search algorithm (GISA) is proposed to obtain the optimal solution in order to maximize the utility of the CS and ESs. Finally, numerical simulation results show that our proposed method greatly outperforms other benchmark schemes under different scenarios, and can encourage ESs to trade their computational resources with the CS effectively.

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.

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 vehicle localization, which aims to identify a vehicle and then position the vehicle with a high precision, can be used to facilitate various applications and services in vehicular networks. Unfortunately, conventional localization systems, e.g., global positioning system (GPS), hardly meet the accuracy requirements especially in certain specific scenarios, such as tunnels. At the same time, Ultrahigh frequency (UHF) radio frequency identification (RFID) has become an efficient booster for internet of things (IoT) due to the desirable advantages, such as low cost, battery-free, and unique identification. In this paper, based on the UHF-RFID, we propose a novel real-time vehicle localization scheme in GPS-Less Environments. Considering the practical implementation of multiple RFID reader antennas on a vehicle is constrained, we adopt single antenna multi-frequency ranging scheme, in which the integer ambiguity problem is solved by the maximum-likelihood estimation (MLE)-based robust Chinese remainder theorem (CRT). With the reconstructed distances between the tags and the reader, the coordinates of the vehicle then can be calculated with the Levenberg-Marquardt (LM) algorithm. Furthermore, the computational complexities of the algorithms and the time consumption of the proposed scheme are analyzed. The experimental results demonstrate that the proposed scheme can track vehicle’s location with error lower than 27 cm at the probability of 90%.

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.

The rapid growth of connected and autonomous vehicles (CAVs) shows an urgent demand for driving and transportation-related data, which gives rise to vehicular crowdsensing systems (VCSs). Nevertheless, the existing centralized VCS framework mainly faces the system reliability problem while the decentralized one cannot satisfy the management flexibility. In addition, when the privacy preservation scheme that prevents information leakage encounters the user selection scheme that desires detailed information of participants, how to balance this seemingly irreconcilable contradiction is inevitable for VCS. To remedy that, we take the first research attempt and explore the balance point between the system management, privacy preservation, and quality of experience (QoE) of participants. By fully exploiting the characters of participating entities, a blockchain-enabled conditional decentralized VCS is proposed in this paper. Firstly, we propose a privacy-preserving scheme where the zk-SNARK proof combines with the mixed-task smart contract to guarantee the interaction process will not reveal any private information of participants. Secondly, we propose an efficient reputation management mechanism that renders certain the participants can get a satisfactory QoE even under the condition that the private information of users is secured. And also, the malicious operations in the system will be effectively supervised. Theoretical analysis and extensive simulations demonstrate the security and efficiency properties of privacy preservation and indicate the effectiveness of reputation management.

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.

In this paper, we investigate secure transmissions in integrated satellite-terrestrial communications and the green interference based symbiotic security scheme is proposed. Particularly, the co-channel interference induced by the spectrum sharing between satellite and terrestrial networks and the inter-beam interference due to frequency reuse among satellite multi-beam serve as the green interference to assist the symbiotic secure transmission, where the secure transmissions of both satellite and terrestrial links are guaranteed simultaneously. Specifically, to realize the symbiotic security, we formulate a problem to maximize the sum secrecy rate of satellite users by cooperatively beamforming optimizing and a constraint of secrecy rate of each terrestrial user is guaranteed. Since the formulated problem is non-convex and intractable, the Taylor expansion and semi-definite relaxation (SDR) are adopted to further reformulate this problem, and the successive convex approximation (SCA) algorithm is designed to solve it. Finally, the tightness of the relaxation is proved. In addition, numerical results verify the efficiency of our proposed approach.

Efficient path planning is the key enabling technology for the realization of intelligent transportation systems (ITS). However, due to poor real-time performance and lack of effective incentive methods, it is difficult for traditional path planning schemes to significantly improve the efficiency of traffic management. In addition, existing solutions that use driving distance and driving time as indicators cannot meet the personalized requirements of vehicle users. To this end, by considering the personalized requirements of vehicle users, we propose a digital-twin (DT) enabled path planning scheme to facilitate traffic management. To be specific, based on the collection of traffic data, we first establish a DT architecture for traffic scheduling to reduce the delay of path planning. Then, according to the traffic density of different road sections, we regard road sections as resources and set different rewards for different road sections to encourage vehicles to obey the scheduling instructions. In addition, by jointly considering the driving time and rewards, we further design personalized utility models to map the requirements of different vehicle users. After that, based on the personalized requirement of the vehicle user, we use a$Q$-learning algorithm to obtain the optimal path with the target of maximizing the user's utility. The simulation results show that the proposed scheme can bring higher utility to the vehicle users than the conventional schemes.

In this paper, we propose an efficient private set intersection protocol, named LL-PSI, to enable two parties (where each party has an individual set) to obtain the intersection of their sets without leaking other information about their sets to each other. Compared with existing protocols, LL-PSI reduces the computational latency of the intersection between two sets significantly at the expense of communication costs between the parties. Based on LL-PSI, we propose a privacy-preserving friend matching scheme for mobile social networks, dubbed PAIRING. PAIRING allows users to match with those who have common interests while preserving users' private information against the semi-honest server and curious users. We analyze the security of PAIRING and conduct a comprehensive performance evaluation, which demonstrates that PAIRING is secure and efficient.

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.

Incident scene text detection, as the most crucial step of an incident scene text recognition system, has received increasing research attention. In this paper, a two-layer mobile federated learning model (TMFL) is proposed to protect data privacy and improve training efficiency. Particularly, a fast scene text detector is proposed to detect the multi-directional and multi-scale text by using an asymmetric convolution based feature pyramid network (AC-FPN). Compared with the traditional feature pyramid, asymmetric convolutions can effectively extract rotation-invariant features to improve the model's robustness to directed text. Moreover, in order to achieve a balance between the detection accuracy and efficiency, we modify the lightweight backbone of mobilenetv3, and integrate it with the asymmetric convolution based feature pyramid. In addition, we evaluate the performance of our detector on three benchmark datasets, where the results show that both the accuracy and the speed can be improved. Our detector can achieve an F-measure of 87.8 on the ICDAR2013, 80.5 on the MSRA-TD500 and 84.1 on the ICDAR2015 dataset, running at 32.5 FPS.

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.

How to alleviate the traffic congestion and improve the traffic capacity of road networks through smart prediction has become a top priority for the realization of Intelligent Transportation Systems (ITS). It is necessary to capture the complex spatio-temporal correlation through the traffic data of road networks to achieve an accurate traffic prediction. In this paper, we propose a prediction method of spatio-temporal optimal graph neural network (STOG). It can obtain the spatio-temporal features of road networks through diffusion graph convolution (DGC) and recurrent neural network (RNN). We further leverage a new spatial attention mechanism to gain the aggregated features of the sampled nodes through pooling operations. It not only avoids excessive parameters, but also makes the model pays more attention to the sampled nodes, thereby reducing the prediction error. Through comparison with various baseline methods on METR-LA dataset, the results show that the proposed model can achieve higher prediction accuracy.

This paper investigates the impact of communication factors on the convergence performance of federated learning (FL) in wireless networks. Considering the limited communication resources in wireless networks, it is difficult to schedule all users to participate in a comprehensive training and the convergence performance of training model relies much on the user scheduling scheme. To minimize the maximum update delay of user training, we propose a joint resource allocation and user scheduling scheme in this paper. Particularly, the user communication delay and user training results are jointly considered to dynamically schedule users and allocate communication resources. Simulation results show that the convergence time can be reduced by 41.6% compared with the random scheduling allocation scheme.

In smart cities, on-street parking space prediction is the key yet difficult point in smart parking system. However, conventional prediction methods generally neglect spatial and temporal dependencies and cannot predict long-term parking events accurately. To this end, we propose a parking space prediction scheme based on the spatial-temporal graph convolution networks (STGCN). We first consider the instantaneous status of the parking to calculate the on-street parking occupancy rate (POR). Then, based on the POR, we exploit a time convolution module and a graph convolution module to extract spatial and temporal dependencies of the parking spaces, respectively. Next, we design the parameters of the STGCN to predict the POR of all the parking spaces based on the spatial and temporal dependencies. Finally, based on the real-world data sets, we compare the proposed scheme with the benchmark models. The experimental results show that the proposed scheme has the best performance in predicting the POR.

Vehicle trajectory prediction technology is of great significance in autonomous driving and intelligent transportation systems. Ego-vehicles can judge the future motion state considering nearby vehicles by predicting their trajectories, which facilitates safe and effective decisions to avoid collisions. It is a challenging task to accurately predict the future trajectories of surrounding vehicles. To solve this problem, we propose a Self-Attention Social Generative Adversarial Networks (SA-SGAN) model to predict trajectories of surrounding vehicles. We use the Self-Attention mechanism to capture the correlation between the features in the vehicle trajectory sequence to effectively solve the problem of missing important information due to a long input sequence, and use training characteristic of Generative Adversarial Networks (GAN) to effectively learn the distribution of real trajectory data and improve prediction accuracy. We evaluate the proposed model through NGSIM dataset, use the trained model to investigate the vehicle trajectory in the next 5s in a three-segment scenario of the US-101 highway, and use the Average Displacement Error (ADE) and Final Displacement Error (FDE) as the evaluation indicators. Compared with baseline methods, the proposed model reduces the evaluation indicators to 4.97 and 8.92 respectively.

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.

The development of the 5G network is envisioned to offer various types of services like virtual reality/augmented reality and autonomous vehicles applications with low-latency requirements in Internet-of-Things (IoT) networks. Mobile-edge computing (MEC) has become a promising solution for enhancing the computation capacity of mobile devices at the edge of the network in a 5G wireless network. Additionally, multiple radio access technologies (multi-RATs) have been verified with the potential in lowering the transmission latency and energy consumption, while improving the Quality of Services (QoS). Benefiting from the cooperation of multi-RATs, large latency-sensitive computing service tasks (L2SC) can be offloaded by different RATs simultaneously, which has great practical significance for data partitioned oriented applications with large task sizes. In this article, to enhance the L2SC offloading services for satisfying low-latency requirements with low energy consumption, we investigate the energy-latency tradeoff problem for partial task offloading in the MEC-enhanced multi-RAT network, considering the limitation of energy and computing in capability-constrained end devices in IoT networks. Specifically, we formulated the L2SC task computation offloading problem to minimize the weighted sum of the latency cost and the energy consumption by jointly optimizing the local computing frequency, task splitting, and transmit power, while guaranteeing the stringent latency requirement and the residual energy constraint. Due to the nonsmoothness and nonconvexity of the formulated problem with high complexity, we convert the tradeoff problem into a smooth biconvex problem and propose an alternate convex search-based algorithm, which can greatly reduce the computational complexity. Numerical simulation results show the effectiveness of the proposed algorithm with various performance parameters.

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.

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.

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.

This paper considers a cooperative device-to-device (D2D) system with non-orthogonal multiple access (NOMA). We assume that the base station (BS) can simultaneously communicate with all users to satisfy the full information transmission requirement. In order to characterize the impact of the weak channel and different decoding schemes, two novel decoding strategies are introduced: single signal decoding scheme and maximum ratio combining (MRC) decoding scheme, respectively. With the single signal decoding scheme, the users decode the received signals immediately after the receptions from the BS. On the other hand, the MRC decoding scheme jointly decodes the received signals via MRC until the corresponding phase comes and the users jointly decode the received signals by employing MRC. Considering Rayleigh fading channels, the ergodic sum-rate (SR), outage probability and outage capacity of the proposed D2D-NOMA system are analyzed. Moreover, approximate expressions for the ergodic SR are also provided with a negligible performance loss. Numerical results demonstrate that the ergodic SR and outage probability of the proposed D2D-NOMA scheme overwhelm that of the conventional NOMA schemes. Furthermore, it is also revealed that the system performance including the ergodic SR and outage probability are limited by the weak channel for both the single signal decoding scheme and conventional NOMA schemes, but not for the MRC decoding scheme.

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.

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.

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.

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.

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.

In smart ocean, multiple unmanned surface vehicles (USVs) are deployed, which generally perform multiple monitoring missions with different requirements of transmission performance. For the monitoring mission, the transmission latency is quite important for marine IoT systems to achieve the ubiquitous situation awareness. However, it is quite challenging due to the location-depended path loss and battery-powered sensors. To address this issue, this paper adopts the Age of Information (AoI) to mathematically express the impact of transmission delay on state estimation, and proposes a mothership assisted cooperative transmission scheme to enhance the estimation performance with limited energy. Moreover, the locations of mother-ships is optimized to minimize the mean squared 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 could achieve smaller the estimation error.

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.

With the significant advance of Internet of Vehicles (IoV), a massive number of vehicular users will connect with the Internet via 5G and the beyond 5G (B5G) networks, which will further worse the spectrum scarcity problem in 5G/B5G networks. Therefore, how to enable dynamical and efficient spectrum resource sharing for IoV users is imperative. To this end, we investigate an evolutionary game enabled spectrum sharing blockchain framework for IoV. Specifically, We use alliance nodes and blockchain framework to assist in the completion of multi-WSP spectrum resource allocation, sharing, and the allocation results storage. We first propose an evolutionary game scheme to allocate the spectrum resource among different WSPs. To reflect the vehicle users' communication demand more appropriately, the vehicle mobility is taken into consideration when defining the payoff of the vehicle users. After completing the spectrum allocation, we illustrate the process of allocation results recording and block generation in our established blockchain verification system, where the transactions ledger of spectrum allocation will be stored in each alliance node in a distributed way. Numerical results exhibit the fast convergence speed of the spectrum allocation approach. Moreover, simulations results from our established hyperledger platform validate the effectiveness and implementability of the investigated spectrum sharing blockchain framework.

With the ubiquitous deployment of mobile sensors and smart devices, the scope of Internet of things (IoT) has extended to the space of mobile networks, where IoT terminals are moving around instead of being fixed in buildings, ground infrastructures, etc. In this paper, we consider such mobile Internet of things (MIoT), and propose an autonomous rate control (RC) scheme for the uplink transmission from MIoT terminals to access stations. A deep reinforcement learning (DRL) based approach is designed to capture the channel variations of the link and to improve the effectiveness of the rate selection for each egress frame. Extensive simulations are conducted for MIoT terminals including vehicles and UAVs and show significant throughput performance improvement comparing with traditional methods, as well as the robustness and scalability of the DRL-RC algorithm. The proposed DRL-RC can provide inspirations for efficient and scalable link adaptation schemes for MIoT terminals.

With the rapid development of network, network monitoring is a significance means to ensure network security and reliability. In-band network telemetry (INT) can collect items in line-rate, and support large traffic volumes and rates network telemetry. However, existing INT monitoring schemes are quite limited in flexibly expanding the execution monitoring tasks. In this paper, we propose Fast-INT, an efficient network monitoring framework combined with learning. The goal of Fast-INT is to design a light-weight INT network collection framework by quickly implementing dynamic and scalable collection of network status information. In our approach, an INT scheduling algorithm based on reinforcement learning is designed to dynamically deploy and adjust INT monitoring tasks when dealing with network inner change event. Particularly, Fast-INT can implement specific INT monitoring tasks on target point to shorten the time of monitoring and make the network monitoring more efficient. The evaluate results show that Fast-INT has a good performance on network monitoring and achieves the goal of intelligently deploying network monitoring tasks.

The implementationof computation offloading is a challenging issue in the remote areas where traditional edge infrastructures are sparsely deployed. In this study, the authors propose a unmanned aerial vehicle (UAV)‐enabled edge computing framework, where a group of UAVs fly around to provide the near‐users edge computing service. They study the computation migration problem for the complex missions, which can be decomposed as some typical task‐flows considering the inter‐dependency of tasks. Each time a task appears, it should be allocated to a proper UAV for execution, which is defined as the computation migration or task migration. Since the UAV‐ground communication data rate is strongly associated with the UAV location, selecting a proper UAV to execute each task will largely benefit the missions response time. They formulate the computation migration decision making problem as a Markov decision process, in which the state contains the extracted observations from the environment. To cope with the dynamics of the environment, they propose an advantage actor–critic reinforcement learning approach to learn the near‐optimal policy on‐the‐fly. Simulation results show that the proposed approach has a desirable convergence property, and can significantly reduce the average response time of missions compared with the benchmark greedy method.

Recent development in renewable energy-enabled electric vehicles (EVs) has posed challenges to the stability and efficiency of the vehicular energy network (VEN), which is a concrete implementation of Internet of Things (IoT) in energy and vehicular networks. In this article, we study a VEN with time-varying point-to-point traffic flow and adjustable energy storage capacity at stations. The goal is to jointly optimize the routing and dynamic storage allocation of renewable energy so as to maximize the efficiency of plant-to-station energy transferring. We first adopt a time-expanded topology graph to describe the scenario and model it as a maximum flow problem. Next, we incorporate routing in our methodology and derive a joint energy storage capacity and route planning method based on linear programming. Then, the problem is extended to a more general case, where the traffic pattern of the VEN is unknown. We apply the long short-term memory model, a deep learning method, to predict the traffic pattern and utilize the concept of reinforcement learning to iteratively improve the prediction accuracy. To evaluate the performance, we implement our method first on regular buses, then extend to EVs based on the real trace data from the PeMS system in California. The simulation results show that the joint optimization can achieve near-optimal performance and performs well even in case of high rate of missing traffic information, with the help of deep reinforcement learning.

The ever-increasing growth in maritime activities with large amounts of Maritime Internet-of-Things (M-IoT) devices and the exploration of ocean network leads to a great challenge for dealing with a massive amount of maritime data in a cost-effective and energy-efficient way. However, the resources-constrained maritime users cannot meet the high requirements of transmission delay and energy consumption, due to the excessive traffic and limited resources in maritime networks. To solve this problem, mobile edge computing is taken as a promising paradigm to help mobile devices from edge servers via computation offloading considering the different quality of service (QoS) with the complex ocean environments, resulting in energy saving and increased transmission latency. To investigate the tradeoff between latency and energy consumption in low-cost large-scale maritime communication, we formulate the offloading optimization problem and propose a two-stage joint optimal offloading algorithm, optimizing computation and communication resource allocation under limited energy and sensitive latency. At the first stage, the maritime users make the decision on whether to offload a computation considering their demands and environments. Then, the channel allocation and power allocation problems were proposed to optimize the offloading policy which coordinates with the center cloud servers at the second stage, considering the dynamic tradeoff of latency and energy consumption. Finally, numerical simulation results show the effectiveness of the proposed algorithm.

Unmanned aerial vehicle (UAV)-assisted communication is envisioned as a potential solution to the data traffic explosion in the massive machine-type communications (mMTC) scenario. In this article, we investigate the UAV-assisted cellular networks, where a UAV acts as a flying relay to offload part of the data traffic from the overloaded cell to another. We utilize the practical spatial distribution of data traffic and a convincing air-to-ground channel model. The quality of service (QoS) is defined as a UAV utility function which is designed based on a packet loss ratio (PLR)-related users' cost function to represent the performance improvements brought by the UAV. We formulate a joint optimization problem to maximize the UAV utility function and then decompose it into the subproblems about the access point selection and the UAV path planning, which influence the PLR by influencing the packet collision rate and channel state. Since the access point selection subproblem is NP-hard, a game-theory-based distributed algorithm is proposed, instructing the users to select the base station (BS) or the UAV as the access point autonomously. To achieve the most superior channel state, we solve the UAV path planning subproblem by a deep reinforcement learning (DRL)-based approach, instructing the UAV to take the optimal action in each position. The simulation results show that the proposed access point selection scheme can significantly reduce the average cost of users and the proposed UAV path planning method can achieve a path with smaller average channel pathloss compared with other approaches.

Internet-of-Vehicles (IoV) connects vehicles, sensors, pedestrians, mobile devices, and the Internet with advanced communication and networking technologies, which can enhance road safety, improve road traffic management, and support immerse user experience. However, the increasing number of vehicles and other IoV devices, high vehicle mobility, and diverse service requirements render the operation and management of IoV intractable. Software-defined networking (SDN) and network function virtualization (NFV) technologies offer potential solutions to achieve flexible and automated network management, global network optimization, and efficient network resource orchestration with cost-effectiveness and are envisioned as a key enabler to future IoV. In this article, we provide an overview of SDN/NFV-enabled IoV, in which SDN/NFV technologies are leveraged to enhance the performance of IoV and enable diverse IoV scenarios and applications. In particular, the IoV and SDN/NFV technologies are first introduced. Then, the state-of-the-art research works are surveyed comprehensively, which is categorized into topics according to the role that the SDN/NFV technologies play in IoV, i.e., enhancing the performance of data communication, computing, and caching, respectively. Some open research issues are discussed for future directions.

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.

Accidents often occur at sea, so effective maritime search and rescue is essential. In the current process of sea search and rescue, the operation efficiency of large search and rescue equipment is low and it cannot provide stable communication link. In this article, unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) are used to form a cognitive mobile computing network for co-operative search and rescue, and reinforcement learning (RL) is used to plan search path and improve communication throughput. Based on the scene of marine search and rescue, the grid method is used to model the search and rescue area. Meanwhile, an intragroup communication architecture based on UAVs and USVs is designed to assist intragroup communication by recognizing the link channel state between UAVs. Search and rescue path planning is carried out through the strategy iteration of Markov decision process (MDP). Furthermore, distributed RL is used to recognize the channel state and perform mobile computing, so as to optimize the data throughput in the communication group. The simulation results show that we have successfully completed the path planning task. Compared with conventional methods, RL based on different reward functions has better throughput performance under the same number of UAVs auxiliary communications.

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.

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+.

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.

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.

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 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.

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.

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.

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.

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.

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.

The infrastructure to vehicle (I2V) communication boosts a large number of prevailing vehicular services, which can provide vehicles with external information, storage, and computing power located at both mobile edge server (MES) and remote cloud. However, vehicle distribution is imbalanced due to the spatial inhomogeneity and temporal dynamics. As a consequence, the communication load for MES is imbalanced and vehicles may suffer from poor I2V communications where the MES is overloaded. In this paper, we propose a novel proactively load balancing approach that enables efficient cooperation among MESs, which is referred to as end-to-end load balancer (E2LB). E2LB schedules the cached data among MESs based on the predicted road traffic situation. First, a convolutional neural network (CNN) is applied to efficiently learn the spatio-temporal correlation in order to predict the road traffic situation. Then, we formulate the load balancing problem as a nonlinear programming (NLP) problem and a novel framework based on CNN is adopted to approximate the NLP optimization. Finally, we connect the above neural networks into an end-to-end neural network to jointly optimize the performance, where the input is the historical traffic situation while the output is the balanced scheduling solution. E2LB can guarantee the real-time scheduling, since the calling of a well-trained neural network only requires a small number of simple operations. Experiments on the trajectories of taxis and buses in Beijing demonstrate the efficiency and effectiveness of E2LB.

The deployment of drone small cells has emerged as a promising solution to agile provisioning of Internet backbone access for Internet of Things devices, and many other types of users/devices. In this paper, we consider the problem of deploying a set of drone cells operating on multiple channels in a target area to provide access to the backbone/core network, which is formulated as a combinatorial network utility maximization problem. Since an offline and centralized solution to such a problem is not feasible, a low-complexity and distributed online algorithm is highly desired. Therefore, we propose a measurement-aided dynamic planning (MAD-P) algorithm, where the dispatched drones perform position and channel configurations autonomously on the fly based on the real-time measurement of network throughput to solve the problem in a distributed fashion during flight with minimal centralized control. We prove that the proposed MAD-P algorithm is asymptotically optimal, and investigate how long it takes for the convergence to stationarity under the MAD-P algorithm by giving a mixing time analysis. We also derive an upper bound of the performance gap in presence of measurement errors. Simulation results are provided to validate our analytic results and demonstrate the effectiveness of our algorithm.

Software-defined networking (SDN) enables routing control to program in the logically centralized controllers. It is expected to improve the routing efficiency even in highly dynamic situations. In this article, we make an in-depth observation of practical Internet datasets and investigate the relationship between betweenness centrality and network throughput . Furthermore, we propose a new routing observation factor, differential ratio of betweenness centrality (DRBC), to denote the varying amplitude of betweenness centrality to node degree. We reveal an interesting phenomenon that DRBC is proportional to the routing efficiency when the maximum betweenness centrality varies in a small range. Based on this, a DRBC-based routing scheme is proposed to improve routing efficiency. The experimental results verify that DRBC-based routing can improve the network throughput and accelerate the routing optimization.

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.

The flourishing vehicular applications require vehicles to download huge amount of Internet data, which consumes significant backhaul bandwidth and considerable time for data content delivery. Caching the popular data at network edge station can alleviate the congestion at backhaul network and reduce the data delivery delay. In this paper, we propose a dynamic edge caching policy for Heterogeneous Vehicular Network via Reinforcement Learning on adaptive traffic intensity and hot content popularity. We aim to enhance the download rate of vehicles adapting to dynamic vehicle velocity and hot file pool by caching the popular content on heterogeneous network edge stations. The proposed policy makes use of real time information and jointly considers download rate for each file and utility for edge stations to improve overall download performance. Simulation results show that our proposed policy can achieve better download rate than random and fixed caching policies.

In this paper, we investigate the resource allocation for mobile edge computation offloading in non-orthogonal multiple access (NOMA) cellular networks. Leveraging NOMA, the massive connectivity can be supported to enable multiple cellular users to simultaneously upload their computation-intensive tasks on the same orthogonal resources, which improves spectral efficiency and reduces transmission delay. However, the co-channel interference in non- orthogonal spectrum sharing may potentially degrade the achievable rate of offloading computation tasks. Moreover, the overall delay of all cellular users in finishing computation offloading will increase if the computation resources at the edge server are not properly allocated. To minimize the maximum overall delay of all users, we formulate an optimization problem that jointly allocates communication resources and computation resources. Due to the non-convexity of the primal problem, we divide it into three subproblems. By exploiting their specific structures, an efficient algorithm is designed to obtain the suboptimal solution with low computational complexity. Simulation results are presented to demonstrate that our proposed algorithm can effectively reduce the overall delay of cellular users and fully exploit the benefit of NOMA on spectral efficiency, especially when the number of users is large.

Information-Centric Networking (ICN) provides great potential to promote the development of the Internet of Things (IoT) due to its multicast nature and mobility support. However, the stateful forwarding peculiarity introduces new varietal attacks named Interest Flooding Attacks (IFA), which is stealthy but destructive for the resource-limited IoT devices. In this paper, we propose a lightweight BLoom-filter based Attack Mitigating (BLAM) mechanism to reduce the detecting memory cost, while guaranteeing both the detecting accuracy and delay. Specifically, each IoT node employs a small Bloom filter to check attack behaviors instead of the traditional memory-consuming operations, i.e., recording malicious requests. Bloom filter values by hashing the published data names with a set of hash functions, are encapsulated and distributed via a new message named Ba-NACK. Based on this design, two specific schemes are further proposed for the attack detecting and Bloom filter updating. We formulate the memory cost minimum problem and theoretically analyze that BLAM can reduce the memory cost. We also implement BLAM in a realistic network testbed to evaluate its performance. The results show that BLAM reduces the memory cost by 78.6%, and reduces the delay from millisecond to microsecond with slight sacrifice of the accuracy by 0.4% compared with other state-of-the-art mechanisms.

In industrial automation, monitoring information is critical and expected to be received with ultra- high reliability and low latency. On the other hand, different industrial monitoring applications usually have diverse requirements on the transmission quality. This paper investigates the deadline aware reliable transmission in mission-critical machine-type communications (MTCs) to satisfy the service requirements for different monitoring applications in terms of reliability and latency. Specifically, a hybrid non-orthogonal multiple access (NOMA) framework is firstly introduced for improving spectrum utilization as well as meeting the diverse requirements of different applications. Under this framework, a NOMA-assisted small-packet transmission scheme is proposed for mission- critical MTCs, and the related performance is then mathematically formulated as a constrained optimization problem with the objective to maximize the network-wide revenue. The formulated non- trivial problem is effectively solved by considering the diverse requirements of different applications. Simulation results are provided to demonstrate both the impact of small packet size and the superiority of NOMA technique on the network-wide revenue improvement.

In 5G networks, self-organizing network (SON) is envisioned to reduce the network operating complexity and costs by providing SON functions, especially in ultra- dense small cell networks. However, diverse SON functions have different time scales and inconsistent targets, which leads to the operation conflicts and network performance degradation. In this paper, we devise a multiple time-scale coordination management scheme (MTCS) to guarantee efficient and stable network operations for densely deployed SONs, where different SON functions have their own specific time scales. Specifically, we propose a novel analysis model , named M time-scale Markov decision process (MMDP), where SON decisions made in each time-scale considers the impacts of SON decisions in other M-1 time scales on the network. Then the proposed scheme with two functions of mobility load balancing (MLB) and energy saving management (ESM) is evaluated in terms of the designed network utility. Simulation results demonstrate that the proposed SON function coordination scheme significantly improves the network utility, while guaranteeing the stability of cooperative operations in wireless networks.

Small cell networks (SCNs) are envisioned as a promising solution to increase the network capacity and coverage. The densely deployments of SCNs in 5G networks pose new challenges for energy-efficient network management. Energy harvesting technique is put forward as a relatively new energy saving concept. However, due to the opportunistic nature of energy harvesting, the uncertainty and complexity will be introduced in energy harvesting SCNs (EH-SCNs) network management. In this paper, we study the self- organized cell operation management problem with different quality of service (QoS) requirements of users, in which the EH-SCNs needs to perform cell activation operation in a distributed manner with the uncertainty of harvested energy. With the assumption of Markovian energy harvesting process, multi-armed bandit game (MAB) based Thompson Sampling algorithm is developed to solve the small cell activation problem with a self-organized manner in EH-SCNs. Simulation results show that our proposed approach is particularly suitable to manage the large-scale EH-SCNs more efficiently under uncertain environment with incomplete information.

By employing the exponentially increasing communication and computing capabilities of vehicles brought by the development of connected and autonomous vehicles, vehicular cloud computing (VCC) can improve the overall computational efficiency by offloading the computing tasks from the edge or remote cloud. In this paper, we study the computation migration problem in VCC, where a vehicle transfers unfinished computing missions to other vehicles before leaving a network edge to avoid mission failures. Specifically, we consider a computing mission offloaded from edge cloud to the vehicular cloud. The mission has a linear logical topology, i.e., consisting of tasks which should be executed sequentially. The migration problem is formulated as a sequential decision making problem aiming to minimize the overall response time. Considering the vehicular mobility, communication time, and heterogeneous vehicular computing capabilities, the problem is difficult to model and solve. We thus propose a novel on-policy reinforcement learning based computation migration scheme, which learns on-the-fly the optimal policy of the dynamic environment. Numerical results demonstrate that the proposed scheme can adapt to the uncertain and changing environment, and guarantee low computing latency.

Offloading vehicular data traffic from cellular networks to roadside WiFi networks is a very interesting issue since it can not only alleviate the traffic congestion for cellular networks, but also reduce the communication cost for vehicle users. In this paper, we study the vehicle-to-vehicle (V2V) assisted WiFi offloading, where nearby vehicles that associate to different access points (APs) can use their idle WiFi resource to offload part of peer's data traffic. We also consider the Internet access delay introduced by the network detection, user authentication and network address assignment between the vehicle and the AP prior to actual data transmission, which has impact on the WiFi cell sojourn duration of the vehicle. The offloading efficiency, which is the traffic offloaded from cellular network, is analyzed by modeling an M/G/1/K queueing process under various conditions. The accuracy of our analysis is validated through the conducted simulation.

This paper considers the multi-loop wireless control system (WCS), where control command is delivered from the remote controller to multiple actuators over shared wireless channels. However, different system dynamics of multiple loops make each loop usually have different demands on the success probability of receiving control commands. Thus, the control performance of overall system is affected by both the transmission reliability and the dynamics of each loop. In this paper, we propose a demand-driven and energy-efficient transmission strategy to adaptive to wireless channels and system dynamics. In order to improve the control performance without burdening the scarce spectrum resources, the remote controller is equipped with multiple antennas, and the transmit beamforming design with power control is adopted to improve the success probability of control commands. In particular, we firstly characterize the control performance of each loop with a pre-defined Lyapunov function, which would decrease exponentially in expectation if the packet loss rate meets the stability condition of each loop. Then, a control stability constrained optimization problem is formulated to minimize the overall cost including energy consumption and linear quadratic Gaussian control cost. The non-trivial probabilistic constraint is effectively handled with the differential accumulation and difference-convex methods. Finally, simulation results verify that the proposed strategy has superiority on reducing control cost and energy consumption without considerations of system dynamics or joint design of transmit beamforming and power control.

Pre-caching popular videos at the local storage of base stations (BSs) can significantly alleviate the tremendous backhaul burden. In this paper, we consider a cache-enabled cloud radio access network (C-RAN) scenario, where multiple BSs cooperatively serve multiple users. Each BS has a local storage and connects to the central processor (CP) via a backhaul link. Since multiple users may simultaneously submit the same request, the multicasting is also exploited to further offload the wireless traffic. The joint BS clustering and beamforming are optimized to maximize the weighted sum quality of experience (QoE) subject to the transmission power constraint and the backhaul capacity constraint. To solve this mixed-integer nonlinear programming, we first equivalently reformulate it as a sparse beamforming problem. Then, the reweighted ℓ1-norm technique is adopted to approximate the non-convex backhaul constraint and the successive convex approximation (SCA) method is applied to deal with the non-convex QoE objective. Simulation results show that cache strategies have great impact on the QoE performance and our proposed scheme significantly outperforms the traditional rate maximization scheme.

Wireless Body Area Network (WBAN) technology jumps into popularity owing to its real-time ability and high reliability in health monitoring. The accompanying interference problem must be highly concerned in coexisting densely deployed WBANs since the inter-WBAN interference results in high delay and low reliability data transmissions, especially with the movement of human body. In the paper, we analyze the dynamic interference with human mobility in multiple coexisting WBANs with the consideration of different distances between inter-WBANs and varying number of coexisting WBANs. Moreover, we investigate the influence of inter- WBAN interference on the performance of normalized throughput and average access delay of different traffic types. The results show that the interference generated by mobile neighbour WBANs extremely decreases the throughput of the target WBAN and increases the average packet delay 1.76 times of emergency data compared with the target WBAN without interference. The dynamic interference analysis provides insights on the practical WBAN management and interference mitigation protocol design, especially for the deeply deployed coexisting WBAN scenarios.

Connected and autonomous vehicles (CAVs) are envisioned as a promising solution integrating the powerful AI and communication technologies to realize fully self-driving. However, there is few vehicular dataset open to study AI assisted self-driving. To make effectively use of AI technologies to optimize self-driving maneuver, we develop an open VeData platform to share the collected datasets. We also develop a Vehicular network Data harvester (VeData), which can collect various vehicular data at an arbitrary frequency. Based on this, we have incrementally collected diversified first-hand data in many different vehicular scenarios, including driving-in-campus, driving-around-campus, driving-in-downtown, and driving-on-highway. More datasets will be collected and shared to promote the research of AI assisted CAVs.

Vehicular ad hoc network (VANET) has been widely recognized as a promising solution to enhance driving safety, by keeping vehicles well aware of the nearby environment through frequent beacon message exchanging. Due to the dynamic of transportation traffic, especially for those scenarios where the density of vehicles is high, the naive beaconing scheme where vehicles send beacon messages at a fixed rate with a fixed transmission power can cause severe channel congestion. In this paper, we investigate the risk of rear-end collision model and define a danger coefficient ρ to characterize the danger threat of each vehicle being in a rear-end collision. We then propose a fully-distributed beacon congestion control scheme, referred to as ABC, which guarantees each vehicle to actively adapt a minimal but sufficient beacon rate to avoid a rear-end collision based on individual estimates of ρ. In essence, ABC adopts a TDMA-based MAC protocol and solves a NP-hard optimal distributed beacon rate adapting (DBRA) problem with a greedy heuristic algorithm, in which a vehicle with a higher ρ will be assigned with a higher beacon rate while keeping the total required beacon demand lower than the channel capacity. We conduct extensive simulations to demonstrate the efficiency of ABC design in different traffic density and a large variety of underlying road topologies.

The ultra-dense edge networks with mmWave and beamforming are envisioned as a potential solution to satisfy the high rate and capacity requirements in 5G networks. In IEEE 802.11 ad, which is the first beamforming-enabled WLAN standard, all stations (STs) contend for beamforming (BF) training opportunities in associated beamforming training (A-BFT) slots. However, due to limited number of A-BFT slots, BF training suffers from a severe collision issue, especially in dense networks, which results in a low channel utilization in the A-BFT stage. To achieve the maximum channel utilization, it is of significance to allocate A-BFT slots efficiently. Therefore, in this paper, we propose an analytical model to analyze IEEE 802.11 ad medium access control (MAC) protocol in BF training stage. In particular, we analyze the successful transmission probability and channel utilization of IEEE 802.11 ad MAC protocol in the dense network. Based on theoretical analysis, we provide the optimal number of A-BFT slots. In addition, theoretical analysis indicates that the maximum channel utilization in the A-BFT stage is barely e−1which is the same as that of slotted ALOHA protocol. Simulation results are provided to validate the accuracy of the analytical model and theoretical analysis.

The wide application of Internet of Things (IoT) in industrial automation encourages the emergence of a new paradigm of industrial IoT systems, wireless control system (WCS), where the system and/or control information is delivered over wireless channels. In practical systems, WCSs would consist of multiple control-loops in general, the resource competition among which would seriously increase mutual interferences and transmission collisions, making it is difficult to provide the required transmission reliability for the control strategy. To address this issue, we design the control strategy together with the hybrid cooperative transmission scheme for multiloop WCSs in a proactive way. We first define the overall system cost function to explore the impacts of standard linear quadratic regulator control cost and wireless transmission reliability on the control performance. In order to further minimize the overall system cost while guaranteeing the control stability, we then propose a control performance aware cooperative transmission scheme, which is formulated as a constrained optimization problem. Decomposition method and heuristic algorithms are designed based on the feature of network structure to solve the formulated mixed integer nonlinear programming problem efficiently. Finally, simulation results demonstrate that by using the proposed strategy, the overall system cost is significantly reduced, decreasing by 78% and 82% compared to the cases without considerations of system dynamics and without cooperative transmission, respectively.

Under the IEEE 802.11p-based dedicated short range communication modules, vehicular safety applications rely on periodical broadcasts of safety beacons by each vehicle. However, the channel can be easily congested by high-frequency periodic beacons when the vehicle density becomes heavy. In this paper, through real-trace-based empirical study on vehicle-to-vehicle communication, we find that nonline-of-sight (NLoS) condition is the key factor on link performance degradation and blindly sending more packets in harsh NLoS conditions can hardly succeed but increase interferences to neighboring vehicles. Inspired by this, we propose a distributed beacon congestion control (DBCC) scheme to control beacon activities with considering link conditions, i.e., vehicles with more neighbors and better conditions of links with its neighbors, will be assigned with higher beacon rates. In DBCC, we first utilize two machine learning methods, i.e., naive Bayes and support vector machines, to train the features and output a classifier model which conducts online NLoS link condition prediction. With link status information, we then formulate a link-weighted safety benefit maximization (L-SBM) problem of the rate-adaptation under a TDMA broadcast MAC, which is proved to be NP-hard. A greedy heuristic algorithm for L-SBM is then proposed and the performance of the algorithm is evaluated. Extensive trace-driven simulations demonstrate the efficiency of DBCC design; particularly, the rate of beacon transmissions can be effectively controlled without exceeding the resource limit and the rate of transmission/reception collisions are greatly reduced.

The wide application of Internet of Things (IoT) in industrial automation leads to the emergence of a new paradigm of industrial IoT systems, namely wireless control system, where control commands are transmitted from the remote controller to multiple actuators over shared wireless channels. Considering system stability, distinct subsystems usually have different requirements on the transmission quality of control commands due to different system dynamics. In this paper, we aim to simultaneously guarantee the stability of all subsystems and minimize the weighted sum of control cost and transmission cost. To this end, the maximum tolerated packet loss rate of each subsystem is first characterized by a pre-defined Lyapunov function. Then, based on channel conditions and system dynamics, a beamforming-assisted hierarchical coordinated transmission strategy is proposed to alleviate the impact of unreliable transmission on the control performance. The control performance and energy efficiency are further optimized by formulating an overall cost minimization problem constrained by the system stability. Both the differential accumulation and the difference-convex methods are employed to effectively deal with the constraint that is expressed in an implicit probabilistic form. Finally, simulation results demonstrate that the proposed strategy has the advantages of reducing control cost and energy consumption.

Intelligent wireless body area networks (WBANs) have entered into an incredible explosive popularization stage. WBAN technologies facilitate real-time and reliable health monitoring in e-healthcare and creative applications in other fields. However, due to the limited space and medical resources, deeply deployed WBANs are suffering severe interference problems. The interference affects the reliability and timeliness of data transmissions, and the impacts of interference become more serious in mobile WBANs because of the uncertainty of human movement. In this paper, we analyze the dynamic interference taking human mobility into consideration. The dynamic interference is investigated in different situations for WBANs coexistence. To guarantee the performance of different traffic types, a health critical index is proposed to ensure the transmission privilege of emergency data for intra- and inter-WBANs. Furthermore, the performance of the target WBAN, i.e., normalized throughput and average access delay, under different interference intensity are evaluated using a developed three-dimensional Markov chain model. Extensive numerical results show that the interference generated by mobile neighbor WBANs results in 70% throughput decrease for general medical data and doubles the packet delay experienced by the target WBAN for emergency data compared with single WBAN. The evaluation results greatly benefit the network design and management as well as the interference mitigation protocols design.

Vehicle‐assisted data offloading is envisioned to significantly alleviate the problem of explosive growth of mobile data traffic. However, due to the high mobility of vehicles and the frequent disruption of communication links, it is very challenging to efficiently optimize collaborative offloading from a group of vehicles. In this paper, we leverage the concept of Software‐Defined Networking (SDN) and propose a software‐defined collaborative offloading (SDCO) solution for heterogeneous vehicular networks. In particular, SDCO can efficiently manage the offloading nodes and paths based on a centralized offloading controller. The offloading controller is equipped with two specific functions: the hybrid awareness path collaboration (HPC) and the graph‐based source collaboration (GSC). HPC is in charge of selecting the suitable paths based on the round‐trip time, packet loss rate, and path bandwidth, while GSC optimizes the offloading nodes according to the minimum vertex cover for effective offloading. Simulation results are provided to demonstrate that SDCO can achieve better offloading efficiency compared to the state‐of‐the‐art solutions.

Data delivery in vehicular networks (VANETs) is a challenging task due to the high mobility and constant topological changes. In common routing protocols, multihop V2V communications suffer from higher network delay and lower packet delivery ratio (PDR), and excessive dependence on GPS may pose threat on individual privacy. In this paper, we propose a novel data delivery scheme for vehicular networks in urban environments, which can improve the routing performance without relying on GPS. A fuzzy‐rule‐based wireless transmission approach is designed to optimize the relay selection considering multiple factors comprehensively, including vehicle speed, driving direction, hop count, and connection time. Wireless V2V transmission and wired transmissions among RSUs are both utilized, since wired transmissions can reduce the delay and improve the reliability. Each RSU is equipped with a machine learning system (MLS) to make the selected relay link more reliably without GPS through predicting vehicle speed at next moment. Experiments show the validity and rationality of the proposed method.

Clustering has been extensively used in Vehicular Ad- hoc NETworks (VANETs) for routing optimization and radio resource management, and continues to be considered to facilitate data dissemination in heterogeneous vehicular networks with the ever- increasing data traffic demands. Most of the existing clustering mechanisms in VANETs operate in a distributed mode. However, there is redundant control overhead and transmission decisions, such as cluster maintenance, parameter tuning and forwarding scheduling, which are costly in distributed modes. In this paper, a centralized clustering based hybrid vehicular networking architecture (CC-HVNA) is proposed, in which the collaborative control between IEEE 802.11p and LTE is realized to achieve clustering and to coordinate message delivery. In CC-HVNA, a volatile node state SN is set to reflect ever-changing network topology and to update clusters. Location-based Vehicle to Infrastructure (V2I) communications are utilized to gather regional information so as to perform centralized clusters partition and maintain cluster info table in infrastructures. We leverage a control center to integrate cluster info from the Evolved Node (eNodeB) and Road Side Units (RSUs). Owing to the possession of global cluster info, cluster changes can be detected and targeted data dissemination can be supported according to content-oriented service. The performance evaluation demonstrates that the proposed CC-HVNA clustering scheme can achieve a significant improvement of safety data dissemination.

In this paper, we investigate multi-message authentication to combat adversaries with infinite computational capacity. An authentication framework over a wiretap channel (W_1, W_2) is proposed to achieve information-theoretic security with the same key. The proposed framework bridges the two research areas in physical (PHY) layer security: secure transmission and message authentication. Specifically, the sender Alice first transmits message M to the receiver Bob over (W_1, W_2) with an error correction code; then Alice employs a hash function (i.e., ε-AWU_2 hash functions) to generate a message tag S of message M using key K, and encodes S to a codeword X^n by leveraging an existing strongly secure channel coding with exponentially small (in code length n) average probability of error; finally, Alice sends X^n over (W_1, W_2) to Bob who authenticates the received messages. We develop a theorem regarding the requirements/conditions for the authentication framework to be information-theoretic secure for authenticating a polynomial number of messages. Based on this theorem, we propose and implement an efficient and feasible 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 experiments, it is demonstrated that the proposed protocol can achieve low time cost, high authentication rate, and low authentication error rate.

To improve the safety, traffic efficiency, and fairness among vehicles at intersections, it is urgent to study intelligent collaborative strategies and make intersections smarter. In this paper, a 3-Level Buffer (TLB) based Virtual Traffic Light (VTL) scheme, named TLB-VTL, is proposed for intelligent collaborative intersections. The intersection is divided into three adaptive areas according to the traffic flow of each lane, and the sequence of each timing cycle is calculated in realtime according to the flow in TLB around an intersection. To ensure fairness, the difference in probability of each lane to pass an intersection is restricted to a lower level. The VTL is realized based on communications of vehicle-to-vehicle (V2V), vehicle-to-roadside (V2R), and vehicle-to- infrastructure (V2I), which could improve the safety and fairness without involving traffic lights. Moreover, a Cooperative Collision Avoidance Predictive control (CCAP) algorithm is proposed, which can assist vehicles to go across the next intersection without stopping through predicting the time conflict and generating an efficient traffic schedule for the entire road network. The simulation results indicate that the proposed TLB-VTL algorithm improves the fairness by 331%, decreases the average delay by 88%, and improves the ability to solve congestion by 12% compared with the traditional traffic light algorithm. Besides, the CCAP algorithm increases the traffic fluency by 45% at the intersection.

In this paper, we propose a secure incentive scheme to achieve fair and reliable cooperative (SIRC) downloading in highway vehicular ad hoc networks (VANETs). SIRC can stimulate vehicle users to help download-and-forward packets for each other and consists of cooperative downloading and forwarding phase. During the cooperative downloading phase, SIRC utilizes “virtual checks” associated with the designated verifier signature to ensure fair and secure cooperation. Meanwhile, to minimize the payment risk of the client vehicle, partial prepayment strategy is adopted, i.e., the vehicles involved in downloading packets can only obtain part of the check before the client vehicle confirms the packet reception. During the cooperative forwarding phase, a profit-sharing model associated with an aggregating Camenisch-Lysyanskaya (CL) signature can stimulate cooperation and reduce the authentication overhead. In addition, we develop a reputation system to encourage cooperation and punish malicious vehicles. The aggregating CL signature and the symmetric cryptosystem are applied to resist various attacks, including injection/removing attack, free riding attack, submission refusal attack, and denial of service attacks. Extensive simulation results are given to show that the proposed SIRC can achieve a high download success rate and low average download delay with moderate cryptographic computation and communication overhead.

The TV white spaces (TVWS) enabled infostation has received significant attention due to its wide area coverage for cost-effective and media-rich content dissemination. In this paper, we engineer WhiteFi infostation, which is dedicated for Internet-based vehicular media streaming by leveraging geolocation database. After demonstrating the empirical observations of unique TVWS features and analyzing the real-world TVWS data collected from geolocation database, we first propose an optimal TVWS network planning to deploy WhiteFi infostation with the objective of maximizing network-wide throughput. The proposed TVWS network planning jointly considers the multi-radio configuration and the channel-power tradeoff, which can be realized by decentralized Markov approximation. Furthermore, we introduce a location-aware contention-free multi-polling access scheduling scheme for vehicular media streaming, which considered both the realistic vehicular applications and dynamics of wireless channel conditions. Through extensive simulations with real-world empirical TVWS data and urban vehicular traces, we demonstrate that our WhiteFi infostation solution can well support both the delay-sensitive and delay-tolerant vehicular media streaming services.

Enabling high-throughput and cost-effective vehicular communications is important for many emerging vehicular applications, such as safety applications, traffic management, and mobile Internet access. However, dedicated short-range communications (DSRC), as the sole solution so far, would meet significant challenges in the foreseeable future for supporting diverse vehicular applications simply due to the spectrum scarcity. To address this issue, in this paper, we propose an adaptive vehicular data piping framework, which is assisted by a geolocation database, for the joint utilization of DSRC and TV white space (TVWS) spectrum; in this framework, three types of vehicular data pipes (DSRC, TVWS, and cellular) are considered, while the cellular data pipe is only used as a control-plane link in coordinating the dynamic DSRC and TVWS spectrum sharing happened in the data-plane operations. In order to guarantee the optimal dynamic vehicular access to the geolocation database, we first propose a log-sum-exp (LSE) approximation-based TVWS geolocation database access approach, named LSE-WS algorithm. We formulate the adaptive vehicular data piping problem for dynamic DSRC/TVWS spectrum sharing as a coalitional formation game, and it is shown that the proposed coalitional formation approach reaches the optimal and Nash-stable vehicular data pipe selection partition in a distributed way. Through extensive simulations, we demonstrate that not only the proposed LSE-WS algorithm satisfies the dynamic vehicular geolocation database access requirement but also the adaptive multi-radio vehicular data piping approach for dynamic DSRC/TVWS spectrum sharing significantly outperforms the traditional DSRC solution.

In this paper, we study opportunistic traffic offloading in a vehicular environment, where the cellular traffic of vehicular users (VUs) is offloaded through carrier-WiFi networks deployed by the mobile network operator (MNO). By jointly considering users' satisfaction, the offloading performance, and the MNO's revenue, two WiFi offloading mechanisms are proposed: auction game-based offloading (AGO) and congestion game-based offloading (CGO). Moreover, we introduce an approach to predict WiFi offloading potential and access cost and incorporate it in the offloading mechanisms. Specifically, with the AGO mechanism, the MNO employs auctions to sell WiFi access opportunities; VUs decide whether to bid according to their utilities and are capable of using WiFi if the auction is won. With the CGO mechanism, a VU calculates utility considering other VUs' strategies and makes offloading decisions accordingly. We show that the AGO mechanism can maximize social welfare and increase the MNO's revenue, whereas the CGO mechanism can achieve a better performance of average VU utility and fairness. Additionally, both AGO and CGO mechanisms can improve the overall WiFi offloading performance. Through simulations, we demonstrate that both AGO and CGO mechanisms can achieve higher average utility of VUs and lower average service delay and offload much more cellular traffic compared with existing offloading mechanisms.

In this paper, we present three network evolution models for generating fault-tolerant and energy- efficient large-scale peer-to-peer wireless sensor networks (WSNs) based on complex networks theory. Being scale-free is one of the intrinsic features of complex networks-based evolution models that generates fault- tolerant topologies. In this work, we argue that fault- tolerant topologies are not necessarily energy efficient. The three proposed energy-aware evolution models are energy-aware common neighbors (ECN), energy- aware large degree promoted (ELDP) and energy-aware large degree demoted (ELDD). ECN considers neighborhood overlap, whereas ELDP and ELDD consider topological overlap for node attachment. The ELDP model promotes the establishment of links to nodes with a large degree, whereas the ELDD model demotes this strategy. Performance evaluations demonstrate that the proposed models outperform a candidate clustering-based model, thereby providing greater energy savings and fault- tolerance. Among the proposed models, ECN is the winner in-terms of energy efficiency, ELDD performs best in- terms of fault-tolerance, and ELDP conveniently provides balance between the two.

The exponential growth of services demands on the sea drives the development of prospective maritime wideband networks. In this paper, the transmission of surveillance videos on board via a maritime wideband communication network is investigated. The latest Time Division Long Term Evolution (TD-LTE) and delay-tolerant networks (DTNs) technology are combined to construct a shore based network framework in order to provide a wide-range transmission over the sea. Accordingly, a video data store-carry-and-forward routing topology is utilized, tailoring for the intermittent network connectivity to efficiently deliver the video data. This study proposes a Two-step Time and Energy Oriented Optimal-stopping (TTEOO) algorithm leveraging backward induction method, based on the optimal stopping rules to schedule data delivery, under the constraint of end-to-end delay of video data and energy consumption of DTN throw box. Simulation results indicate that the proposed method can achieve low consumption cost and high data delivery ratio for the oversea video transmission applications.

In this paper, we investigate the network throughput and energy sustainability of green-energy-powered maritime wireless communication networks. Specifically, we study how to optimize the schedule of data traffic tasks to maximize the network throughput with Worldwide Interoperability for Microwave Access technology. To this end, we formulate it as an optimization problem to maximize the weight of the total delivered data packets, while ensuring that harvested energy can successfully support transmission tasks. The formulated energy and content-aware vessel throughput maximize problem is proved to be NP-complete. We propose a green energy and content-aware data transmission framework that incorporates the energy limitation of both infostations and delay-tolerant network throw boxes. The green energy buffer is modeled as a G/G/1 queue, and two heuristic algorithms are designed to optimize the transmission throughput and energy sustainability. Extensive simulations demonstrate that our proposed algorithms can provide simple yet efficient solutions in a maritime wireless communication network with sustainable energy.

We study user cooperation to enhance the physical layer security. Specifically, the source cooperates with friendly intermediate nodes to transmit message securely in the presence of multiple eavesdroppers. We propose a cooperative framework, whereby the source selects multiple partners and stimulates them by granting an amount of reward. First, multiple cooperative relays and jammers are selected by the source using greedy or cross-entropy based approaches. Then, the source and the partners negotiate for the payment and transmission power, which is modeled as a two-layer game. At the top layer, a buyer-seller game is utilized, where the source buys the service provided by the partners. At the bottom layer, all the partners share the reward by determining their transmission powers in a distributed way, which is formulated as a non-cooperative power selection game. By analyzing the game, the partners can determine the transmission powers for cooperation, while the source can select the best payment. To further improve the utility of the source, a set of reward allocation coefficients are introduced and optimized using particle swarm optimization approach. Simulation results are provided to demonstrate the performance of the proposed schemes.

In this paper, we present an analytical framework for offloading cellular traffic by outdoor WiFi network in the vehicular environment. Specifically, we consider a generic vehicular user with Poisson data service arrivals to download/upload data from/to the Internet through the cost-effective WiFi network (want-to) or the cellular network providing full service coverage (have-to). Under this scenario, the WiFi offloading performance, characterized by offloading effectiveness, is analyzed in terms of desired average service delay which is the average time the data services can be deferred for WiFi availability. We establish an explicit relation between offloading effectiveness and average service delay by an M/G/l/K queueing model, and the tradeoff between the two is examined. We validate our analytical framework through simulations based on a VANET simulation tool VANETMobisim and real map data sets. Our analytical framework should be valuable for providing offloading guidelines to both vehicular users and network operators.

In this paper, cooperative sensing for multi-channel cognitive radio networks (CRNs) is studied, whereby the secondary users (SUs) cooperate with each other to sense the multiple channels owned by the primary users (PUs). The objective is to better protect the primary system while satisfying the SUs' requirement on the expected access time. A general scenario is considered, where the channels present different usage characteristics and the detection performance of individual SUs varies due to the channel conditions between the PUs and SUs. With the dynamics in the channel usage characteristics and the detection capacities, each SU chooses one channel for sensing to minimize the interference to the PUs. The problem is formulated as a nonlinear integer programming problem which is NP-complete in general. To find the solution efficiently, the original problem is transformed into a variant of convex bipartite matching problem by constructing a complete bipartite graph and defining proper weight vectors. Based on the problem transformation, a channel assignment algorithm is proposed for computing in polynomial time the solution in terms of the number of SUs, the number of channels, and the maximum value of weights. Simulation results are presented to validate the performance of the proposed algorithm.

Providing various wireless connectivities for vehicles enables the communication between vehicles and their internal and external environments. Such a connected vehicle solution is expected to be the next frontier for automotive revolution and the key to the evolution to next generation intelligent transportation systems (ITSs). Moreover, connected vehicles are also the building blocks of emerging Internet of Vehicles (IoV). Extensive research activities and numerous industrial initiatives have paved the way for the coming era of connected vehicles. In this paper, we focus on wireless technologies and potential challenges to provide vehicle-to-x connectivity. In particular, we discuss the challenges and review the state-of-the-art wireless solutions for vehicle-to-sensor, vehicle-to-vehicle, vehicle-to-Internet, and vehicle-to-road infrastructure connectivities. We also identify future research issues for building connected vehicles.

In this paper, risk-aware cooperative spectrum access schemes for cognitive radio networks (CRNs) with multiple channels are proposed, whereby multiple primary users (PUs) operating over different channels choose trustworthy secondary users (SUs) as relays to improve throughput, and in return SUs gain transmission opportunities. To study the multi-channel cooperative spectrum access, cooperation over single channel is investigated first, which involves a PU selecting the suitable SU and granting a period of access time to the selected SU as a reward, considering trustworthiness of SUs. The above procedure is modeled as a Stackelberg game, through which access time allocation and power allocation are obtained. Based on the above results, cooperation over multiple channels is studied from the perspectives of the primary network and secondary network, respectively. Two schemes are proposed accordingly: the primary network-centric matching (PCM) scheme and the secondary network-centric cluster-based (SCC) scheme. In PCM scheme, cooperating SU for each channel is determined to maximize the total utility of the primary network, which is formulated as a maximum weight matching problem. In SCC scheme, SUs first form a cluster to share the channel state information (CSI), and the best SUs are selected for cooperation with PUs over different channels to obtain the maximum aggregate access time for the secondary network. Then, SUs share the obtained resource using congestion game and quadrature signalling. Numerical results demonstrate that, with the proposed schemes, PUs can achieve higher throughput, while SUs can obtain longer average access time, compared with the random channel access approach.

In this paper, dynamic spectrum access (DSA) in multi-channel cognitive radio networks (CRNs) is studied. The two fundamental issues in DSA, spectrum sensing and spectrum sharing, for a general scenario are revisited, where the channels present different usage characteristics and the detection performance of individual secondary users (SUs) varies. First, spectrum sensing is investigated, where multiple SUs are coordinated to cooperatively sense the channels owned by the primary users (PUs) for different interests. When the PUs' interests are concerned, cooperative spectrum sensing is performed to better protect the PUs while satisfying the SUs' requirement on the expected access time. For the SUs' interests, the objective is to maximize the expected available time while keeping the interference to PUs under a predefined level. With the dynamics in the channel usage characteristics and the detection capacities, the coordination problems for the above two cases are formulated as nonlinear integer programming problems accordingly, which are proved to be NP-complete. To find the solution efficiently, for the former case, the original problem is transformed into a variant of convex bipartite matching problem by constructing a complete bipartite graph and defining proper weight vectors. Based on the problem transformation, a channel selection algorithm is proposed to compute the solution. For the latter case, the deterministic optimization problem is first transformed to an associated stochastic optimization problem, which is then solved by cross-entropy (CE) method of stochastic optimization. Then, the sharing of the available channels by SUs after sensing is modeled by a channel access game, based on the framework of weighted congestion game. An algorithm for SUs to select access channels to achieve Nash equilibrium (NE) is proposed. Simulation results are presented to validate the performance of the proposed algorithms.

In this paper, we investigate uploading monitoring videos for vessels via a maritime wideband communication network. The Worldwide Interoperability for Microwave Access (WiMAX) technology is utilized to establish a shore-side network infrastructure, and a packet store-carry-and-forward routing mechanism is implemented to address the intermittent network connectivity in maritime communications. A resource allocation problem is formulated to maximize the weights of uploaded video packets, subject to the intermittent network connections and the release time and deadline of each video packet. Time-capacity mapping is applied to transform the original resource allocation problem to a two-machine non-preemptive scheduling problem. As ship routes are relatively stable, the global information in terms of release time, deadline and other time indices of video packets, as well as the schedules of vessels is known a priori. We propose two offline scheduling algorithms, namely Time-capacity mapping based two phase (TMTP) algorithm, and Interval graph theory based job relay selection (IGTJRS) algorithm. Both algorithms achieve a time complexity of O(n2). The performance of proposed algorithms is evaluated through simulation based on actual ship route traces obtained from dedicated Navigation software BLM-Ship.

In this paper, an opportunistic channel access for cognitive radio networks (CRNs) with multiple channels is proposed, whereby the secondary users (SUs) cooperate with primary users (PUs) to improve the latter's throughput and gain transmission opportunities in return. Cooperation on single channel is studied first, which is modeled by the Stackelberg game. By analyzing the game, the access time allocation of the PU and the optimal transmission power of the SU can be obtained. Then, based on the outcome of the above game, cooperation on multiple channels in the network is studied. To better exploit transmission opportunities on different channels, a cluster-based cooperation scheme (CBC) is proposed, whereby SUs first form a cluster, select best SUs to obtain the maximum sum of the access time using maximum weight matching, and then share the obtained channels fairly using congestion game and quadrature signalling. The condition for Nash Equilibrium (NE) of the congestion game is provided and an algorithm for CBC scheme is proposed. Numerical results demonstrate that, with the proposed scheme, the SUs can get more average access time and achieve higher fairness, compared with the random channel access approach.

The booming smart grid produces a large amount of data that should be transmitted to the utility control center (UCC), typically by means of the cellular network. This may pose a prohibitive transmission cost and choke the cellular network. As an effort to address this issue, we propose a vehicle assisted data delivery method to offload the cellular network, in which vehicles are utilized to carry and deliver the data from distributed locations to the UCC through the deployed roadside units. Two data forwarding schemes are developed based on the theory of optimal stopping rules to increase the data delivery probability. Simulation results are given to demonstrate that the proposed method can achieve high data delivery ratio through the roadside network so that it can efficiently offload the cellular network and reduce the communication cost.

In this paper, we propose an electronic mail system, namely VeMail, for handling messages between vehicles and Intelligent Transportation Systems (ITS), to improve the efficiency of transportation management. After elaborating the reasons of using Internet email as a basis of messaging for ITS, we describe the key components of the VeMail system, including mail server, mail client, and mail proxy. Considering the intermittent connectivity of vehicles to the mail server, we propose an optimal probabilistic message retrieval (OPMR) scheme for VeMail, in which each vehicle optimally selects an online period for email retrieval. Simulation is used to evaluate the performance and the results demonstrate that the proposed scheme outperforms the regular mail retrieval method in terms of the connection time with the mail server.

In this paper, we investigate cooperative networking in cognitive radio networks (CRNs), which targets to help the primary users (PUs) for secure communications and provide transmission opportunities to secondary users (SUs). Two cooperation schemes: relay-jammer (R-J) scheme and cluster-beamforming (C-B) scheme, are proposed. In R-J cooperation scheme, two individual SUs, a relay and a friendly jammer, are leveraged by the PU to improve communication secrecy via cooperation; In return, the PU allocates a fraction of access time for SUs' transmission. To achieve the maximum secrecy rate, joint time and power allocation is considered. In C-B cooperation scheme, the PU cooperates with a cluster of SUs, which enhance the secrecy of primary link via collaborative beamforming and gain spectrum access opportunities as a reward. With the objective of maximizing the secrecy rate, the optimal weights and time allocation are studied. Numerical results validate the proposed schemes and demonstrate that the PU can significantly enhances the secrecy through cooperation with the cooperating SUs by allocating time and transmission power optimally.

In cognitive radio networks (CRNs), secure information transfer is of paramount importance for primary users (PUs), while secondary users (SUs) mainly desire to ease the starvation for transmission opportunities. To meet such different requirements, cooperation between PUs and SUs can be leveraged and therefore create a win-win situation. In this paper, we investigate cooperative spectrum access for CRNs, which targets to improve the secure transmission of PUs via cooperating SUs that would be incented by certain transmission opportunities. Two types of cooperation schemes are proposed, whereby the PU either cooperates with two individual SUs or a cluster of SUs, which are referred to as relay-jammer (R-J) scheme and cluster-beamforming (C-B) scheme, respectively. In R-J scheme, two individual SUs act as a relay and a friendly jammer to improve the PU's secrecy; In return, the PU allocates a fraction of access time for the SUs' transmission. To achieve the maximum secrecy rate, joint time and power allocation is considered. Particularly, the cooperating relay and jammer determine the optimal transmission power, while the PU decides the optimal time allocation strategy. In C-B scheme, the PU cooperates with a cluster of SUs to enhance the secrecy of the primary link via collaborative beamforming, where three different approaches are proposed for the scenarios with one eavesdropper, with multiple eavesdroppers, and without eavesdroppers' information, respectively. To maximize the secrecy rate, the weight selection and time allocation are also studied. Simulation results are given to validate the proposed schemes and demonstrate that the PU can significantly enhance the secrecy through cooperation.

Access infrastructure, such as Wi-Fi access points and cellular base stations (BSs), plays a vital role in providing pervasive Internet services to vehicles. However, the deployment costs of different access infrastructure are highly variable. In this paper, we make an effort to investigate the capacity-cost tradeoffs for vehicular access networks, in which access infrastructure is deployed to provide a downlink data pipe to all vehicles in the network. Three alternatives of wireless access infrastructure are considered, i.e., cellular BSs, wireless mesh backbones (WMBs), and roadside access points (RAPs). We first derive a lower bound of downlink capacity for each type of access infrastructure. We then present a case study based on a perfect city grid of 400 km2with 0.4 million vehicles, in which we examine the capacity-cost tradeoffs of different deployment solutions in terms of capital expenditures (CAPEX) and operational expenditures (OPEX). The rich implications from our results provide fundamental guidance on the choice of cost-effective access infrastructure for the emerging vehicular networking.