ABSTRACT Integrated sensing and communication (ISAC) is a pivotal technology for sixth‐generation (6G) networks. Intelligent reflecting surfaces (IRS), particularly the simultaneous transmitting and reflecting IRS (STAR‐RIS), enable dynamic channel control to enhance ISAC performance. However, conventional passive STAR‐RIS is constrained by limited signal gain. While the emerging active STAR‐RIS addresses this limitation via signal amplification, it introduces heightened power consumption and security risks, especially when sensing targets act as potential eavesdroppers. This paper investigates the secure transmission problem in an active STAR‐RIS‐aided ISAC downlink system. Our objective is to maximize the sum secrecy rate by jointly optimizing the base station beamforming vectors, the sensing signal covariance matrix and the coefficients of the active STAR‐RIS for both reflection and transmission, while satisfying practical constraints on power and sensing performance. To solve this non‐convex problem, we propose an efficient two‐layer alternating optimization algorithm that decomposes it into tractable subproblems. These subproblems are solved using semidefinite relaxation and a novel eigenvalue‐penalty‐based method. Numerical simulations demonstrate that the proposed active STAR‐RIS scheme significantly outperforms baseline architectures (passive STAR‐RIS, active RIS and passive RIS) in achieving a superior balance between communication security and sensing capability.

WiFi-based indoor localization is essential for asset tracking, healthcare monitoring, and smart buildings. However, existing systems face challenges such as RSS variability, environmental noise, and difficulty in detecting floor and building levels, compounded by limited labeled data and the high costs of collecting received signal strength (RSS). This paper introduces quantum stochastic contrastive learning (QSCL), a novel framework grounded in rigorous theoretical foundations. We present four theorems and one lemma that establish bounded probabilistic augmentation, diversity of the strong view, the suitability of the symmetric contrastive objective under heterogeneous augmentation channels, and expected similarity stability under zero-mean perturbations, supported by formal proofs. Leveraging these foundations, QSCL uses quantum computing (QC) to generate strong data augmentations via stochastic perturbations, thereby enhancing data diversity, while classical weak augmentations provide subtle variations for robust feature learning. We propose a spatio-temporal encoder (STE) that integrates convolutional layers with channel and spatial attention modules (CBAM-style) to capture spatial and temporal dependencies in sequential data. Furthermore, a symmetric cross-view contrastive loss is introduced to capture forward and reverse relationships between augmented views, ensuring robust representations. Comprehensive evaluations on the UJIIndoorLoc and UTSIndoorLoc datasets validate QSCL, demonstrating superior performance with limited labeled data and resilience to quantum and measurement noise. The proposed framework significantly improves localization accuracy, floor and building detection, and generalizability in challenging indoor environments.

Modern supply chains must balance high-speed logistics with environmental impact and security constraints, prompting a surge of interest in AI-enabled Internet of Things (AIoT) solutions for global commerce. However, conventional supply chain optimization models often overlook crucial sustainability goals and cyber vulnerabilities, leaving systems susceptible to both ecological harm and malicious attacks.To tackle these challenges simultaneously, this work integrates a quantum-inspired reinforcement learning framework that unifies carbon footprint reduction, inventory management, and cryptographic-like security measures. We design a quantum-inspired reinforcement learning framework that couples a controllable spin-chain analogy with real-time AIoT signals and optimizes a multi-objective reward unifying fidelity, security, and carbon costs. The approach learns robust policies with stabilized training via value-based and ensemble updates, supported by window-normalized reward components to ensure commensurate scaling. In simulation, the method exhibits smooth convergence, strong late-episode performance, and graceful degradation under representative noise channels, outperforming standard learned and model-based references, highlighting its robust handling of real-time sustainability and risk demands. These findings reinforce the potential for quantum-inspired AIoT frameworks to drive secure, eco-conscious supply chain operations at scale, laying the groundwork for globally connected infrastructures that responsibly meet both consumer and environmental needs.

LoRaWAN has become a leading Low Power Wide Area Network (LPWAN) technology for Industrial Internet of Things (IIoT) applications, offering long range communication with low energy consumption. A fundamental challenge lies in selecting the appropriate Spreading Factor (SF) for each device, since this directly influences coverage, packet success ratio (PSR), and airtime. The default Adaptive Data Rate (ADR) mechanism is static and fails to adapt under dynamic network conditions such as mobility. This paper proposes a lightweight linear upper confidence bandit (LinUCB–SF) based reinforcement learning approach for adaptive SF selection. Each end device uses locally observable features to autonomously select its SF, balancing exploration and exploitation. The method is implemented and validated in ns-3 simulations across a range of deployment densities. Results show that our proposed LinUCB-SF algorithm reduces energy consumption by 19.3% in mobile scenarios and 37.8% in static scenarios, while improving PSR by 9.1% and 9.0%, respectively, compared to the EXP3 baseline.

This paper addresses the challenge of energy-constrained maritime monitoring networks by proposing an unmanned aerial vehicle (UAV)-enabled integrated sensing, communication, powering and backhaul transmission scheme with a tailored time-division duplex frame structure. Within each time slot, the UAV sequentially implements sensing, wireless charging and uplink receiving with buoys, and lastly forwards part of collected data to the central ship via backhaul links. Considering the tight coupling among these functions, we jointly optimize time allocation, UAV trajectory, UAV-buoy association, and power scheduling to maximize the performance of data collection, with the practical consideration of sea clutter effects during UAV sensing. A novel optimization framework combining alternating optimization, quadratic transform and augmented first-order Taylor approximation is developed, which demonstrates good convergence behavior and robustness. Simulation results show that under sensing quality-of-service constraint, buoys are able to achieve an average data rate over 22 bps/Hz using around 2 mW harvested power per active time slot, validating the scheme’s effectiveness for open-sea monitoring. Additionally, it is found that under the influence of sea clutters, the optimal UAV trajectory always keeps a certain distance with buoys to strike a balance between sensing and other multi-functional transmissions.

Large-scale deep learning models rely on wireless networks for distributed training approaches, which are essential to meet the immense computational and data demands. However, the stochastic nature of wireless environments introduces significant challenges such as variable delays, noise interference, and packet loss, which lead to degraded gradient synchronization and hinder model convergence. In this work, we propose a novel communication-aware distributed training (CADT) framework that integrates real-time channel state information (CSI) feedback into the gradient aggregation process. Unlike conventional methods that assume static or ideal communication conditions, CADT dynamically reweights gradients from each node based on instantaneous channel quality, enabling robust aggregation under adverse wireless conditions. By dynamically adjusting the contribution of each node based on instantaneous channel conditions, CADT effectively compensates for wireless impairments, thereby ensuring more reliable gradient aggregation and significantly improving both convergence speed and final model accuracy. Extensive experiments on CIFAR-10, CIFAR-100, ImageNet, and SVHN using Vision Transformer and ResNet-50 demonstrate that CADT outperforms baseline methods in terms of convergence, accuracy, and communication efficiency. In addition, we provide a rigorous theoretical analysis that establishes convergence guarantees under realistic wireless conditions, thereby advancing the theoretical foundation of distributed optimization in non-ideal communication environments.Our framework offers a practical solution for real-world scenarios such as edge computing, where communication constraints and environmental variability are dominant factors.

Coexistence design that enables the emerging candidate waveforms to interoperate with legacy orthogonal frequency division multiplexing (OFDM) is essential for sixth-generation standardization. This paper focuses on orthogonal time frequency space (OTFS)—a representative waveform for high-mobility scenarios—and proposes a novel coexistence paradigm between OTFS and OFDM, termed cross-domain division multiplexing (XDM), utilizing the different signal representations across multiple domains. XDM is formulated through sparse mapping in the delay-Doppler domain and periodicity-based partial superposition in the time-frequency domain. We prove that XDM holds several favorable invariant properties, which enable tractable interference characterization and suppression even under dynamic channels. We also show that XDM supports flexible numerologies and provide some standardization-oriented practice examples. For practical implementation, XDM transceivers are designed for near interference-free coexistence leveraging the invariant properties. We first develop a cross-domain waveform-level interference cancellation algorithm with ultra-low complexity for single OTFS user coexisting with OFDM. More generally, a domain-transform embedded factor-graph is constructed for multiple OTFS users, over which a cross-domain expectation propagation algorithm is proposed to achieve almost lossless recovery of the coexisting signals. Through a novel variance transfer technique, we prove that the detection errors of coexisted OTFS and OFDM respectively converge to their counterparts under interference-free transmission. Simulations show that XDM exhibits BER loss about 0.5 dB compared to interference-free transmission under various channel conditions and system configurations.

Deep autoencoder (DAE) frameworks have demonstrated their effectiveness in reducing channel state information (CSI) feedback overhead in massive multiple-input multiple-output (mMIMO) orthogonal frequency division multiplexing (OFDM) systems. However, existing CSI feedback models struggle to adapt to dynamic environments caused by user mobility, requiring retraining when encountering new CSI distributions. Moreover, returning to previously encountered environments often leads to performance degradation due to catastrophic forgetting. Continual learning involves enabling models to incorporate new information while maintaining performance on previously learned tasks. To address these challenges, we propose a generative adversarial network (GAN)-based learning approach for CSI feedback. By using a GAN generator as a memory unit, our method preserves knowledge from past environments and ensures consistently high performance across diverse scenarios without forgetting. Simulation results show that the proposed approach enhances the generalization capability of the DAE framework while maintaining low memory overhead. Furthermore, it can be seamlessly integrated with other advanced CSI feedback models, highlighting its robustness and adaptability.

Owing to the on-demand deployment, low cost, and flexibility, unmanned aerial vehicles (UAVs) are capable of performing tasks such as data monitoring, collection, and sharing. However, the openness of UAV wireless networks makes data susceptible to security threats such as theft, tampering, and forgery during collaborative data sharing and mission execution. Additionally, the limited resources and high mobility of UAVs further exacerbate challenges related to data security and reliability. To address these issues, this paper proposes a blockchain-based secure data sharing scheme for cloud-assisted multi-UAV networks. Specifically, we leverage cloud-based infrastructure to undertake the storage of massive data, significantly offloading the computational and storage burdens on the UAVs. Simultaneously, blockchain is integrated to establish immutable and traceable trust for the shared data, ensuring strong security, integrity, and retrievability under the constraints of UAV resources. On this basis, a certificateless searchable encryption algorithm is employed to eliminates traditional certificates management overhead and enables lightweight, distributed, and efficient search based on predefined keywords. Furthermore, we introduce an access control list based on geofencing to specify the data sharing permissions of UAVs, strictly limiting the data access permissions within specific area, thereby reducing the misuse or abuse of data. This scheme is proven to achieve ciphertext and trapdoor indistinguishability against keyword guessing attacks. The performance analysis indicates that the efficiency advantages of the proposed scheme become increasingly prominent as the number of UAVs increases.

With the development of surveillance cameras, more bandwidth is required to transmit surveillance videos. Since surveillance videos contain a large amount of redundant information, it causes a waste of bandwidth. Meanwhile, previous video compression methods with the fixed compression standards are unable to handle asymmetric information effectively. To address these problems, we propose Task-driven Semantic Communication with Unsupervised Semantic Segmentation (TSCUSS) for surveillance video assisted by Generative Artificial Intelligence (GAI), to improve efficiency. First, at the transmitter, we segment the videos into the foreground semantic and background models. Second, in the transmission side, we transmit the extracted semantic information in two-stage semantic communication, which greatly reduces redundant information. Third, at the receiver, we merge the foreground and background semantic models through the diffusion model to recover the original semantic content. Finally, our experiment shows that our method not only achieves 78.34% average video compression rate and improves bandwidth utilization, but also dominates in both semantic segmentation accuracy and generative foreground background merge similarity.

Relay-based communication has become a key approach to meeting the growing demands for low latency and high reliability links in intelligent transportation and vehicle-to-everything (V2X) systems. In complex urban environments such as roads, tunnels, and dense high-rise building areas, traditional vehicle-to-vehicle (V2V) relay links are severely impeded by deep fading and multiple interference sources, which significantly degrade end-to-end performance. To enhance relay-based transmission under such harsh conditions, this paper investigates a dual reconfigurable intelligent surface (RIS)-assisted decode-and-forward (DF) relay V2V system as a representative relay-enhanced architecture. By combining two reconfigurable intelligent surfaces with a DF relay, the proposed scheme strengthens both hops of the relay link, effectively alleviating the performance bottlenecks commonly encountered in single-RIS or traditional relay schemes. The system adopts a Nakagami-mfading channel model and explicitly considers the aggregated interference at the relay and destination nodes. Based on this, we derive analytical expressions for the end-to-end outage probability and average channel capacity, employing Fox’s H function and the Gaussian-Laguerre quadrature method for precise evaluation. Additionally, an adaptive RIS reflection unit allocation algorithm is proposed to jointly optimize the total number of RIS units and their two-stage distribution under reliability constraints, thereby enhancing the efficiency of relay-based communication while reducing hardware deployment costs.

Efficiently running services in the worldwide scattered spectrum bands is increasingly difficult as those bands get ever more congested. Unlicensed spectrum offers a promising solution but effective coexistence among access technologies is required to mitigate interference and ensure the always more stringent Quality of Service required by new services. Besides, the increasing number and complexity of security threats further challenge system reliability. To address these issues, this paper proposes a mechanism to enhance security and reliability , and a blockchain-based framework for spectrum sensing and sharing in Wi-Fi and Long Term Evolution-Unlicensed (LTE-U) coexistence networks. Performance under different channel fading models is analyzed, and obtained results show the effectiveness of the proposed scheme, improved sensing accuracy, and resistance to malicious behavior in both low- and high-density scenarios.

Efficient orchestration in the edge–cloud continuum is essential for reducing energy consumption and meeting latency requirements in large-scale IoT systems. This article presents a hybrid deep reinforcement learning (DRL) and federated learning (FL) framework that dynamically allocates computation across IoT, edge, fog, and cloud layers. The DRL agent learns energy-efficient scheduling strategies through a latency-aware reward design, while FL enables decentralized model training without exposing raw data. Experimental evaluation demonstrates up to 31.6% lower energy consumption and 28.4% latency reduction compared to existing heuristics. Results also show rapid learning convergence within 200 episodes, indicating strong adaptability under changing network and workload conditions. These findings confirm the effectiveness of the proposed framework in improving energy efficiency, latency performance, and scalability for next-generation IoT deployments.

With the development of real-time video conferences, interactive multimedia services have proliferated, leading to a surge in traffic. Interactivity becomes one of the main features on future multimedia services, which brings a new challenge to Computer Vision (CV) for communications. In addition, many directions for CV in video, like recognition, understanding, saliency segmentation, coding, and so on, do not satisfy the demands of the multiple tasks of interactivity without integration. Meanwhile, with the rapid development of the foundation models, we apply task-oriented semantic communications to handle them. Therefore, we propose a novel framework, called Real-Time Video Conference with Foundation Model (RTVCFM), to satisfy the requirement of interactivity in the multimedia service. Firstly, at the transmitter, we perform the causal understanding and spatiotemporal decoupling on interactive videos, with the Video Time-Aware Large Language Model (VTimeLLM), Iterated Integrated Attributions (IIA) and Segment Anything Model 2 (SAM2), to accomplish the video semantic segmentation. Secondly, in the transmission, we propose a two-stage semantic transmission optimization driven by Channel State Information (CSI), which is also suitable for the weights of asymmetric semantic information in real-time video, so that we achieve a low bit rate and high semantic fidelity in the video transmission. Thirdly, at the receiver, RTVCFM provides multidimensional fusion with the whole semantic segmentation by using the Diffusion Model for Foreground Background Fusion (DMFBF), and then we reconstruct the video streams. Finally, the simulation result demonstrates that RTVCFM can achieve a compression ratio as high as 95.6%, while it guarantees high semantic similarity of 98.73% in Multi-Scale Structural Similarity Index Measure (MS-SSIM) and 98.35% in Structural Similarity (SSIM), which shows that the reconstructed video is relatively similar to the original video.

The literature repeatedly reports that the unique nature of individual brainwave patterns makes them suitable for identification and authentication, because they are difficult to replicate or forge. Therefore, many researchers have utilized brainwaves for authentication by training traditional deep learning and machine learning models. However, the internal decision processes of these black-box models have not been evaluated in terms of biases, overfitting, large training data requirements, and handling complex data structures, which keep them in a fuzzy state. To address these limitations, a smart system is needed to be develop that could be capable of making the authentication process user-friendly, robust, and reliable. In this paper, we present a deep reinforcement learning-based biometric authentication framework known as "BrainAuth" for personal identification using the gamma ($\gamma$) and beta ($\beta$) brainwaves. This approach improves the accuracy of authentication by using the (i) Dyna framework and a dual estimation technique. Both these technique helps to maintain the integrity of brainwave patterns, which are needed for authentication and understanding of spoofing activities. (ii) We also introduce a layered structure architecture in the proposed model to reduce the time needed for exploration using two deep neural networks. These networks work together to handle the complex data while making decisions in delay sensitive environment. (iii) We evaluate the model on seen and unseen data to verify its robustness. During analysis, the model achieved an equal error rate (EER) of $\approx$ 0.07% for seen data and $\approx$ 0.15% for unseen data, respectively. Furthermore, the analysis metrics such as true positive (TP), false positive (FP), true negative (TN), and false negative (FN) followed by false acceptance rate (FAR), false rejection rate (FRR), true acceptance rate (TAR) revealed significant improvements compared to existing schemes.

The real-time processing of large-scale, heterogeneous tasks—including CPU-only, general-purpose GPU, and specialized GPU tasks—poses significant challenges in Internet of Things (IoT) systems, driven by severe GPU resource fragmentation, inefficient CPU and memory resource utilization on edge servers. These issues often compromise system processing performance and server stability. To address these issues, we formulate a multi-stage mixed-integer nonlinear programming (MINLP) model, to jointly optimize GPU fragmentation rate and system processing capability. We then introduce a novel deviation-based Lyapunov optimization framework that explicitly maintains memory utilization around a predefined optimal threshold, effectively balancing resource usage and system stability. Finally, to achieve real-time decision-making for massive tasks in dynamic systems with randomly arriving tasks, we propose the MA-LHTO algorithm, a multi-agent deep reinforcement learning approach that incorporates a multi-head architecture, entropy-based exploration, and a parameter reset mechanism. Experimental results confirm that our algorithm significantly improves resource utilization, and exhibits good performance under various working conditions.

Wireless power transfer enhanced mobile edge computing (WPT-MEC) has emerged as a key technology to support low-latency and energy-efficient computation in wireless networks. With increasing network density, multi-base-station architectures emerge where wireless devices (WDs) offload tasks to distributed base stations (BSs), creating challenges in maintaining quality-of-service fairness during complex resource coordination in multi-BS WPT-MEC networks. To address these challenges, we investigate a non-orthogonal multiple access (NOMA)-enhanced WPT-MEC network comprising multiple WDs and BSs with finite computational capacities. For ensuring fairness, we formulate a max-min problem to maximize the minimum task computation amount by jointly optimizing offloading decisions, NOMA decoding orders, offloading powers and time resource allocation, which results in a challenging mixed integer, sequence and nonlinear programming (MISNLP). To tackle this problem, we propose a two-stage distributed multi-agent algorithm. In the first stage, each BS agent generates offloading preferences based on partial observations, guiding WDs' offloading decisions. In the second stage, given these offloading decisions, we develop an efficient convex-based algorithm to solve the per-BS resource allocation subproblem, jointly optimizing NOMA decoding order, offloading powers and time resource allocation. For effective training, we leverage off-policy training and the centralized training with decentralized execution (CTDE) paradigm with two key innovations: (1) a convex-based critic that evaluates the joint action without bias, and (2) a counterfactual baseline that isolates individual agent credit assignment. The proposed C3MA algorithm achieves six times faster convergence and at least 20% performance improvement when serving more than 20 WDs, compared with existing multi-agent schemes, while maintaining a near-optimal Jain's fairness index of 0.97. Moreover, it sustains an ultra-low execution delay below 5 milliseconds even with 40 WDs, confirming its efficiency and scalability.

The development of Internet-of-Things (IoT) services demands intelligent and adaptive mechanisms for online resource allocation under dynamic and uncertain environments. Intent-Based Networking (IBN) has emerged as a promising paradigm to align system behavior with high-level user intents. However, realizing intent-aware allocation in real time remains challenging due to uncertain resource availability and incomplete future information. This paper presents a modular framework that integrates semantic intent parsing, machine-learned resource prediction, and robust online decision-making. We propose IBN-ONMP, an IBN-based online resource allocation algorithm that leverages machine-learned predictions and adapts safety margins based on feedback to ensure feasibility and performance under uncertainty. We formally define the problem, establish theoretical guarantees including regret and competitive ratio bounds, and validate the approach on real-world and simulated datasets. Experimental results demonstrate that IBN-ONMP achieves high utility and robust performance across varying prediction error levels, which is consistent with theoretical analysis.

The regional edge cloud data centers support numerous latency-sensitive applications, including autonomous driving, Augmented Reality/Virtual Reality (AR/VR), smart grids. However, dynamic workloads often trigger spurious Virtual Machine (VM) migrations that degrade real-time service guarantees. To address this challenge, we propose a lightweight, proactive VM scheduling framework based on a hierarchical structure (HLFVM). By combining logical region partitioning with low-complexity migration algorithms, it enables rapid localized migration decisions. First, by leveraging the Enhanced Harris Hawk Optimization (EHHO) to optimize the parameters of the Long Short Term Memory (LSTM) model, we propose a Load Forecast method based on the EHHO-LSTM (LFEL) model. This algorithm accurately predicts multiple resource loads on PMs and reduces the lag in migration decision-making. Then, we propose the zone-aware LFEL-based VM Migration (LFVM) algorithm, which includes PM status classification and migration selection mechanism. The migration selection mechanism chooses the VM destinations according to the cost function to expedite the migration decision. Numerous experiments have shown that the execution time of the LFVM algorithm is reduced by at least 70.4% compared to traditional algorithms, while VM migration time is improved by 5.7%. Concurrently, it achieves superior control over energy consumption and enhances resource utilization.

With great potential in providing global coverage and real-time service, recently, direct satellite-to-device (DS2D) communication has attracted considerable attention. However, how to effectively utilize the costly satellite resources to satisfy the on-demand massive connectivity remains a huge challenge. This paper proposes a cost-effective hetero-granular resource allocation framework that combines the advantages of ground-based scheduling and spaceborne scheduling. In specific, we aim to optimize both the cell-level and user-level scheduling in a beam-hopping system. The formulated optimization problem is first decomposed into a coarse-grained scheduling problem among cells using historical demand information on the ground station, and a fine-grained scheduling problem among users using real-time service demands on satellite. We solve the mixed-integer non-linear programming problem of coarse-grained scheduling with cross-entropy and quantum particle swarm optimization algorithms to find the global optimum, exploiting the adequate ground-based computational resources. The fine-grained scheduling problem is solved with a generalized-benders-decomposition-based algorithm to accommodate the limited spaceborne resources, which decouples power and bandwidth allocation based on a closed-form solution of optimal dual variables in the primal power allocation problem. Simulation results demonstrate that the proposed method effectively reduces the length of the waiting queue by up to 25.05% compared to the existing methods.

Symbiotic radio (SR) has emerged as a spectrum and energy-efficient paradigm to support massive Internet of Things connections. This paper investigates secure transmission in a multi-antenna artificial noise (AN)-assisted SR network under both parasitic SR (PSR) and commensal SR (CSR) setups, with a particular focus on the challenges posed by the presence of a passive eavesdropper. Specifically, the transmitter allocates part of its power for AN generation to disrupt the eavesdropper deliberately without affecting the legitimate receiver. To evaluate the secrecy performance in both setups, new approximate closed-form expressions for the secrecy outage probability in primary and backscatter links are derived using the Gauss-Chebyshev quadrature method. The secrecy diversity orders of the system are studied by analyzing the asymptotic behaviours in the high signal-to-noise ratio regime. Furthermore, the secrecy performance under imperfect channel state information is investigated to evaluate the robustness of the proposed scheme in practical scenarios. Monte Carlo simulations are performed to validate the correctness and effectiveness of the analytical results, which demonstrate that the CSR setup provides stronger secrecy performance than the PSR setup in primary signal decoding.

In this paper, we propose a task-oriented semantic communication system based on the cloud-network-end (C-N-E) architecture to improve the energy efficiency of image transmission. Within the system, a cloud server provides storage and computation resources for image data collected by multiple cameras. The semantic information of an image is modeled as a scene graph, enabling the analysis of end-user interests. To reduce communication overhead, only useful semantic information relevant to user interests is transmitted. Considering the delay constraint, we formulate an optimization problem to minimize the total energy consumption by jointly selecting semantic information and allocating computation and communication resources. To solve this problem efficiently, an iterative algorithm based on optimal matching and sequential convex approximation is developed. Comparative simulations validate the efficacy of our algorithm.

In fully decoupled networks with separate uplink/downlink (UL/DL) base station (BS) deployments and high user mobility, ensuring efficient UL/DL user association remains a critical challenge. The dynamic environment and complex channel conditions necessitate state perception for optimal association strategies, while the densification of nodes demands scalable solutions to handle increased combinatorial complexity in UL and DL transmissions. This paper introduces a novel framework leveraging unmanned aerial vehicle (UAV) sensing-assisted to predict user mobility and channel dynamics, combined with multiple association mechanism to address dense node interactions. Accordingly, a joint optimization problem is formulated, where Kriging-based prediction is adopted to assist the user association for both UL and DL. To solve it, a hierarchical matching game is developed to decompose the joint problem into decoupled UL and DL games. Particularly, a low-complexity Kriging prediction-based hierarchical matching algorithm is designed to obtain the solution. Simulation results in dynamic network scenarios demonstrate that the effectiveness of proposed approach and the superiority is validated by comparisons.

This paper proposes a quantum-enabled gradient-based coverage optimization (QEGCO) framework for dynamic antenna configuration in future wireless networks. The optimization task of adjusting antenna azimuth and tilt to maximize soft coverage is formulated using a differentiable surrogate objective, enabling the application of gradient-based methods. A parameterized quantum circuit (PQC) encodes the control variables, and the parameter-shift rule is employed to compute exact gradients efficiently, independent of spatial sampling resolution. Entanglement is introduced via CNOT gates to enhance circuit expressivity and model interactions between antennas. Simulation results demonstrate that QEGCO achieves faster convergence, higher final coverage ratios, and superior computational scalability compared to classical stochastic gradient descent (SGD) and finite gradient descent (FGD) baselines. Complexity analysis highlights that QEGCO reduces computational complexity from ${\mathcal{O}}(2\cdot a\cdot T)$ in classical methods to ${\mathcal{O}}(2\cdot a\cdot\log (T))$, where a is the number of antennas, and T is the total time required for computation of gradients and coverage metrics. These findings illustrate the potential of quantum-assisted optimization techniques for scalable and efficient wireless network self-organization. Future directions include extending QEGCO to cooperative multi-cell networks and implementing hardware-efficient quantum circuits for near-term quantum devices.

As an emerging communication technology, fluid antenna (FA) offers remarkable diversity and multiplexing gains due to its port mobility, which significantly reduces transmission delays in communication processes. This capability makes FA a promising solution for enhancing mobile edge computing (MEC) by optimizing communication delay. This paper establishes an FA-aided MEC offloading architecture and proposes a game theory-assisted multi-agent deep reinforcement learning (DRL) scheme to minimize the system delay of MEC. We aim to address the joint optimization problem of FA port selection, beamforming, user transmit power design, and MEC server computation resource allocation. However, the dynamic nature of FA ports and the variability of the associated large number of parameters introduce significant challenges, such as non-convexity and high dimension, in the optimization problem. In this paper, we employ game theory to reduce the dimension of the optimization variables by modeling the power control problem among multiple users as a non-cooperative game. Therefore, we propose a multi-agent deep deterministic policy gradient (MADDPG) algorithm, featuring two types of agents that collaboratively solve the problem. Simulation results validate the effectiveness of the proposed scheme, achieving 19.1-65.8% lower delays than benchmarks in MEC efficiency across all scenarios.

Digital Twin (DT) and mobile edge computing are two promising solutions for achieving latency-sensitive and computing-intensive applications in Industrial Internet of Things (IIoT). However, existing task offloading schemes with DT empowerment are faced with challenges, such as the spatio-temporal heterogeneity of edge server (ES) resources, resource-constrained ESs, and the explosive growth of data in emerging applications. This paper investigates the issues of task offloading and resource allocation under the assistance of DT and multiple ESs parallel collaboration. One novel scheme, abbreviated as Mes-PCORA, is proposed. With comprehensive information within the digital space, the Mes-PCORA scheme dynamically adjusts task allocation ratios across multiple ESs to achieve collaborative task offloading. The offloading request is formulated as a non-convex problem. To make the non-convex problem solvable in polynomial time, the original problem transformed into a bilevel optimization problem. Then, a bilevel iterative optimization approach is proposed. Specifically, the upper-level optimization problem is formulated as a multi-agent Markov Decision Process, and a deep reinforcement learning-based resource allocation algorithm is designed to solve it. Subsequently, for the lower-level optimization problem, it is solved by the interior point method. Numerical results demonstrate that the proposed scheme reduces the average task completion latency by 27.16%–63.44% and decreases the task offloading failure rate by 35.83%–73.95%, compared to state-of-the-art baselines.

In response to the rapid growth of data in cyberspace and the resulting challenge to storage capacity, this paper proposes a new off-chain storage scheme for blockchain based on erasure codes. The scheme allows for the application of different coding methods tailored to various scenarios. To validate its feasibility, an off-chain distributed storage test system built on the proposed framework is implemented. The test results demonstrate that the proposed scheme ensures blockchain data integrity from local and global perspectives reducing the system's repair bandwidth. This novel off-chain storage approach addresses blockchain's storage limitations and has the potential to enhance the overall robustness and reliability of the system.

The rise of smart applications in wireless devices increasingly relies on mobile edge computing (MEC), where longterm system energy efficiency holds crucial significance for both green computing and application vendors. This paper focuses on long-term energy efficiency in a wireless power transferenabled MEC system. This system faces the challenges of timevarying channel states and stochastic task arrivals. We first formulate this problem to simultaneously optimize offloading, power transfer duration, and energy consumption, while ensuring device queue stability. We then introduce a novel algorithm based on Lyapunov-guided deep reinforcement learning, referred to as LyCNN-DRL. This approach efficiently handles the mixed integer non-linear programming problem by transforming it into a deterministic per-slot problem for online optimization, without needing prior knowledge of future conditions. Specifically, we tackle the problem by dividing it into resource allocation and binary offloading components, applying a convolutional neural network model for near-optimal offloading decisions, and obtaining the optimal solution for resource allocation. Simulation results show that LyCNN-DRL outperforms baseline algorithms, stabilizing MEC network task queues. Furthermore, we quantitatively derive the trade-off between energy efficiency and queue length, represented as$[O(1/V), O(V)]$with the variable$V$.

Mobile Edge Computing (MEC) is crucial for enabling computation-intensive applications in Internet of Things (IoT) networks where low-latency and energy-efficient processing are essential. The integration of multiple Reconfigurable Intelligent Surfaces (RIS) enhances the communication efficiency between IoT devices and MEC servers by dynamically adjusting signal propagation. This paper addresses an energy minimization problem in a multi-RIS-assisted MEC system for IoT network, aiming to reduce the total energy consumption while meeting latency and resource constraints. Our proposed framework innovatively combines multi-RIS path selection with edge computing task offloading, and incorporates a dynamic RIS activation strategy for multi-user scenarios. To tackle this mixed-integer non-linear programming problem, we develop an effective decomposition algorithm based on Block Coordinate Descent (BCD). The problem is iteratively solved through three subproblems: RIS phase shift optimization, path selection and RIS activation, offloading ratio and power allocation. Simulation results show that our approach achieves up to 26% energy saving compared to the single RIS scheme, demonstrating the significant potential of multi-RIS integration in energy-efficient IoT-MEC systems.

The exponential growth in the number of handheld devices and other user equipment belonging to power class 3 as per 3GPP specifications is projected to reach 64 billion by the end of 2025. This has led to a drastic increase in the power consumption of radio units. This increase in power consumption is further enabled by the increasing demand for higher data rates and reduced latency for emerging 6G communications. Furthermore, with the expected latency of 1ms in 6G communications, the user equipment demands also change accordingly. This leads to a rapid change in requirements and therefore fluctuations in power consumption. Addressing this problem, this paper presents a novel Quantum Optimised Priority Resource Allocation (QO-PRA) algorithm designed to reduce dynamic power consumption in wireless communications. QO-PRA integrates heuristic-based multi-dimensional knapsack problem framework with quantum algorithms such as the Variational Quantum Eigensolver to optimise its parameters. This allow the QO-PRA algorithm to reduce the power fluctuations in radio units over time. Furthermore, reduced power fluctuations in radio units allow the wireless system to be more efficient, reliable and predictable. Benchmarking the QO-PRA against another widely used scheduling algorithm such as round-robin demonstrates a significant improvement in smoothening power fluctuations over time while maintaining a lower average power and average power per user per second. Furthermore, it achieves a 81.65% lower standard deviation of the first derivatives of power consumption compared to the round-robin algorithms which indicates a smoother power consumption curve.

With the surge in wireless data traffic, integrating millimeter-wave (mmWave) technology into vehicular networks enables high-speed communication. Meanwhile, the rising demand for secure wireless communication drives the use of reconfigurable intelligent surfaces (RIS) to enhance physical layer security (PLS) through intelligent channel control. This paper investigates PLS approaches in multi-RIS-assisted mmWave vehicular communication under stochastic geometry architecture. Taking the dynamically changing and random nature of vehicular network topologies into account, we propose a vehicular network association scheme for a typical vehicle. In this scheme when the quality of the direct link deteriorates due to obstacles or other factors, RIS-assisted communication ensures a more stable connection. By leveraging stochastic geometry theory, a tractable analytical framework is established to evaluate the secrecy performance of the downlink transmission comprehensively. Specifically, the closed-form expressions of connection outage probability (COP) and secrecy outage probability (SOP) are derived. Simulation results demonstrate that introducing RIS into vehicular networks and utilizing the proposed association scheme can significantly improve the security of vehicular networks.

Integrating vision and touch is key to understanding the physical world, but it faces two main challenges: effective multimodal fusion and high-fidelity tactile representation. This paper proposes a multimodal semantic communication framework based on foundation models through visual-tactile fusion. First, a multimodal enhancement fusion network extracts deep features from video to improve tactile recognition and semantic understanding. Second, a CLIP-driven framework, grounded in a tactile knowledge base, enhances the accuracy of tactile information transmission. An end-to-end model with joint source-channel coding further improves transmission efficiency. Finally, we introduce a tactile generative reconstruction method using ImageBind, which ensures high similarity in both visual features and pressure distribution. Experimental results confirm the effectiveness of our approach in semantic tactile reconstruction. Overall, the proposed method enables efficient, low-bit-rate communication with high semantic fidelity, offering a promising solution for visual-tactile fusion in real-world applications.

Federated learning (FL) is a promising approach that effectively and securely harnesses the vast amounts of data generated by the rapid proliferation of internet-connected devices. In FL, the transmission of model parameters over wireless channels plays a pivotal role in determining system performance. To optimize the wireless environment and boost communication efficiency, we present a novel FL beamforming design scheme that integrates multi-sector beyond diagonal reconfigurable intelligent surfaces (BD-RIS) with over-the-air computation (AirComp). The scheme leverages the waveform superposition property of wireless signals, using AirComp to rapidly aggregate the global model in FL. Additionally, the scheme utilizes BD- RIS to flexibly manip-ulate communication beams, improving user channel conditions and further reducing model aggregation errors. Specifically, we evaluate the impact of this design on FL systems and derive an upper limit on the gap between training loss and optimal loss. To minimize this gap, we formulate a joint optimization problem of BD- RIS passive beamforming and base station receive beamforming, and we propose an optimization algorithm based on successive convex approximation (SCA) and block coordinate descent (BCD) to solve it. Simulation results confirm that our de-sign significantly enhances user channel conditions and improves FL performance, with the benefits becoming more pronounced as the number of BD- RIS reflecting elements increases.

Futures are essential instruments in financial markets. Accurately predicting futures volatility is crucial for calculating value-at-risk and comprehensively assessing financial uncertainty. However, the rapid changes in the futures market, the continuous emergence of new commodities, and the close interaction with spot markets create a complex market environment . This results in futures data having intricate characteristics of limited historical data , non-stationary, and non-linear, posing significant challenges for accurately predicting volatility. We propose a futures volatility prediction framework, Meta-Learning with Long Short-Term Transformer (Meta-LSTR) to tackle these challenges. To improve the understanding of market dynamics, we construct a Long-Short Term Transformer network. In conjunction with a de-stationary module and market-side information, the network can effectively capture multi-scale non-stationary features and non-linear temporal dependencies. To enhance the efficiency of limited data utilization, we employ a meta-learning approach to extract common knowledge across different varieties of futures. Comprehensive experiments using Chinese market data highlight the effectiveness of the Meta-LSTR model in futures volatility prediction. Compared to other state-of-the-art methods, the proposed Meta-LSTR model reduces prediction error by over 21.99%.

Low-energy carbon Internet of Things (IoT) systems are essential for sustainable development, as they reduce carbon emissions while ensuring efficient device performance. Although classical algorithms manage energy efficiency and data processing within these systems, they often face scalability and real-time processing limitations. Quantum algorithms offer a solution to these challenges by delivering faster computations and improved optimization, thereby enhancing both the performance and sustainability of low-energy carbon IoT systems. Therefore, we introduced three quantum algorithms: quantum neural networks utilizing Pennylane (QNN-P), Qiskit (QNN-Q), and hybrid quantum neural networks (QNN-H). These algorithms are applied to two low-energy carbon IoT datasets—room occupancy detection (RODD) and GPS tracker (GPSD). For the RODD dataset, QNN-P achieved the highest accuracy at 0.95, followed by QNN-H at 0.91 and QNN-Q at 0.80. Similarly, for the GPSD dataset, QNN-P attained an accuracy of 0.94, QNN-H 0.87, and QNN-Q 0.74. Furthermore, the robustness of these models is verified against six noise models. The proposed quantum algorithms demonstrate superior computational efficiency and scalability in noisy environments, making them highly suitable for future low-energy carbon IoT systems. These advancements pave the way for more sustainable and efficient IoT infrastructures, significantly minimizing energy consumption while maintaining optimal device performance.

Secure data aggregation is a powerful strategy for ensuring both data availability and privacy protection in smart grids. However, existing methods face two significant challenges: first, the substantial increase in communication and computation costs caused by malfunctioning smart meters; second, the risk of identity privacy leakage. To address these issues, we propose an efficient, fault-tolerant, and dual privacy-preserving data aggregation scheme. Our scheme effectively eliminates reliance on a trusted authority (TA) by leveraging an enhanced Paillier cryptosystem and a dual-secret sharing mechanism while ensuring robust fault tolerance. Additionally, it incorporates a pseudonym mechanism to safeguard user identity privacy. To meet the statistical requirements of modern smart grids, the scheme extends support for multidimensional data aggregation. Security analysis confirms that the proposed scheme provides dual privacy protection, ensures semantic security, and resists collusion attacks among participants. Furthermore, performance evaluations demonstrate that the proposed scheme maintains low communication and computation costs. Specifically, in fault-tolerant aggregation scenarios, its computation costs remain significantly lower than that of existing schemes, highlighting its efficiency. These results affirm the scheme’s practicality for smart grid applications.

In this article, we propose a novel integrated sensing and communication (ISAC) algorithm for massive multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems with spatial-frequency wideband (SFW) effects. To obtain high accuracy of channel state information (CSI), the proposed algorithm initially utilizes a deep neural network (DNN) for channel estimation. Then, the estimated channel is expressed as a third-order low-rank tensor model, on which the canonical polyadic (CP) decomposition is performed to obtain three factor matrices. These factor matrices hold the information pertaining to channel parameters. By fitting the constructed tensor model, channel parameters, such as Angles of Departure (AoDs), Angles of Arrival (AoAs), time delay, and complex gains, can be extracted. Ultimately, the positions of mobile station (MS) and scattering points are determined by utilizing the mapping relationship between the channel parameters and position coordinates. In contrast to existing algorithms, the proposed algorithm delivers greater precision in both channel estimation and positioning. The simulation results demonstrate that the proposed algorithm maintains outstanding ISAC performance, persisting even with diminished compression rate. Furthermore, the proposed algorithm proves effective in more complex scenarios lacking a line-of-sight (LOS) path.

Sensor and actuator deception attacks often manipulate states and control commands, which lead to system performance deterioration or even instability. This article focuses on the platoon control problem for a group of connected and automated vehicles (CAVs) subject to both deception attacks and external disturbances. First, modeling both attacks and external disturbances as lumped disturbances, and a novel adaptive fixed-time disturbance observer (AFxTDO) without requiring prior knowledge of the disturbance boundary is further constructed to estimate the lumped disturbances within given time with zero estimation errors. Then, a new global fixed-time stability result with faster convergence rate is developed, together with the given AFxTDO, a variable exponent fixed-time sliding-mode control (VFxTSMC) scheme is established, such that the platoon tracking errors can converge to a predetermined region within the given time while avoiding the singularity phenomenon, improving the convergence speed and reducing the number of controller parameters. Meanwhile, individual vehicle stability and string stability also can be guaranteed within fixed-time by the suggested control scheme. Finally, the simulation results reveal the effectiveness and superiority of the proposed algorithm.

Integrated sensing and communication (ISAC)-enabled low-altitude Internet of Things (IoT) networks hold significant potential for applications in smart cities and emergency communication systems. However, achieving secure and energy-efficient communication under complex environments, particularly in the presence of the mobile full-duplex eavesdropper (MFDE), presents significant challenges. This study investigates the optimization of secure rate energy efficiency (SREE) in ISAC-enabled low-altitude IoT networks, where the problem is further complicated by the strong coupling between unmanned aerial vehicles (UAV) trajectory design, power allocation, and artificial noise (AN) generation, leading to an optimization issue marked by significant dimensionality and a lack of convexity. To tackle this challenge, a power cost factor-based Twin Delayed Deep Deterministic Policy Gradient (CTD3) algorithm is developed, which incorporates a game-theoretic power allocation strategy into the TD3 framework to efficiently handle the high-dimensional coupled optimization problem. The algorithm reformulates part of the high-dimensional continuous optimization process into a strategy interaction problem and introduces a power cost factor into the utility function, effectively reducing the dimensionality of optimization variables and the overall computational burden. Furthermore, an adaptive dynamic sensing mechanism is introduced to enhance resource utilization while effectively countering the dynamic behavior of eavesdroppers. The effectiveness of the proposed strategy in enhancing SREE performance amidst environmental uncertainties is validated through extensive simulations, where it consistently outperforms baseline methods.

Internet of things (IoT) is crucial for the hierarchical medical system, which enables the real-time monitoring and collection of data, thereby improving patient treatment outcomes. However, achieving secure, efficient, timely, and controllable medical IoT data sharing between higher-lever hospital (HLH) and lower-level hospital (LLH) is a challenging task for the hierarchical medical system. Consortium blockchain, which is an effective way to achieve secure and trustworthy data sharing, has the potential to address these issues. In this article, we propose a novel cloud-chain sharing scheme for medical IoT data based on consortium blockchain. In this scenario, HLH and LLH establish a consortium blockchain, where medical IoT data is stored both on-chain and off-chain. On-chain data adopt a proxy re-encryption based on elliptic curve cryptography (ECC-PRE) strategy and attribute-based strategy to facilitate secure access and controlled sharing of data. Off-chain data sharing provides three different modes, namely, private data collection (PDC), direct channel (DC), and cloud storage (CS), according to the urgency of patient and the sensitivity of the data. Furthermore, a file security breakpoint resume scheme, rooted in the consortium blockchain, and a file weighting strategy are employed to enhance the efficiency and timeliness of data sharing. Finally, the security and performance of our proposed scheme are verified, and the results demonstrate that our scheme is secure, feasible, and efficient.

Energy-efficient healthcare systems are becoming increasingly critical for Industry 5.0 as the Internet of Medical Things (IoMT) expands, particularly with the integration of 5G technology. 5G-enabled IoMT systems allow real-time data collection, high-speed communication, and enhanced connectivity between medical devices and healthcare providers. However, these systems face energy consumption and data security challenges, especially with the growing number of connected devices operating in Industry 5.0 environments with limited power resources. Quantum computing integrated with machine learning (ML) algorithms, forming quantum machine learning (QML), offers exponential improvements in computational speed and efficiency through principles such as superposition and entanglement. In this paper, we propose and evaluate three QML algorithms, which are UU, variational UU, and UU-quantum neural networks (QNN) for classifying data from four different datasets: 5G-South Asia, Lumos5G 1.0, WUSTL EHMS 2020, and PS-IoT. Our comparative analysis, using various evaluation metrics, reveals that the UU-QNN method not only outperforms the other algorithms in the 5G-South Asia and WUSTL EHMS 2020 datasets, achieving 100% accuracy, but also aligns with the human-centric goals of Industry 5.0 by allowing more efficient and secure healthcare data processing. Furthermore, the robustness of the proposed quantum algorithms is verified against several noisy channels by analyzing accuracy variations in response to each noise model parameter, which contributes to the resilience aspect of Industry 5.0. These results offer promising quantum solutions for 5G-enabled IoMT healthcare systems by optimizing data classification and reducing power consumption while maintaining high levels of security even in noisy environments.

Content based video search services find extensive applications across various domains including video surveillance and object detection. In recent times, researchers have increasingly turned their attention towards enhancing the security of video search over outsourced encrypted videos. Nonetheless, prior researchers often leverage cost-expensive techniques like Homomorphic encryption or Order-preserving encryption to ensure privacy preservation. To reduce the overhead, Bloom Filter (BF)-encoded keyword search is a promising technology for retrieving encrypted videos with image queries. However, it generally suffers from serious data privacy leakage since it will reveal the inclusion relationship between “1” and “0” in the BF. Fortunately, the privacy-preserving bloom filter-based search scheme (PBKS) was recently proposed to achieve secure and effective search while protecting the values in BFs, but it still has two limitations. One is the size of a search token is very large in some cases and the other is the cloud server can infer the true value of each bit in the BF by doing a few operations. In this paper, we propose an efficient and confidentiality-preserving bloom filter-encoded video search (ECVS) scheme for retrieving encrypted videos with image queries. We first design a new CPRF (prefix-constrained pseudorandom function)-based token compression method to reduce the size of the search token and reduce the communication cost largely. Furthermore, we customize a periodic refresh mechanism to conceal the true value of each bit in the BF while avoiding excessive computational pressure on resource-limited users. Security analysis and experiments confirm the security and efficiency of our schemes.

The decision fusion rule and global optimality is studied for serial distributed detection in multihop multirelay wireless sensor networks (WSNs) under the end–edge–cloud orchestration. In particular, a multihop relay node serial distributed detection configuration is considered. Then, the optimal decision fusion rule is derived, and the detection probability and false alarm probability of the distributed detection system are given. Third, the suboptimal decision fusion rule under different conditions for the multihop relay channel is represented. Furthermore, in order to solve the high energy consumption and bandwidth limitation problems of WSNs, we consider the global optimization for serial distributed detection systems and obtain the sufficient condition. Finally, under different communication conditions, the detection performance of the system has been kept optimal when we increase the number of sensors in series. We validate the conclusions based on the numerical results. The sufficient condition for optimality detection of serial distributed detection multirelay sensor network system is satisfied then the system can achieve optimal detection performance.

The parallel distributed detection is studied for mobile wireless sensor networks (MWSNs) in the presence of Byzantine attacks in entirely unknown environment or complicated environment from the perspective of the information theory, where we pay most of our attention toward design, analysis, and evaluation of the attack strategy. In particular, the multihop relay network and the harsh wireless communication condition, e.g., the dynamic and entirely unknown channel, are taken into consideration in our configuration. Second, the conditions that the optimal attacking strategy should satisfy is analyzed and developed under different attacking scenarios. Third, the minimum attacking power is developed for the Byzantines to blind the fusion center (FC). Furthermore, the optimal attacking strategies are developed when no prior information of the system is known for the Byzantines. Finally, the traditional four typical attack strategies are evaluated, and we find that the fraction of Byzantines is the only factor that affects the reliable data fusion when the network size and the attacking strategy are fixed. The extensive simulation is conducted to verify our design, analysis, and evaluation of the attack strategy.

Real-time industrial video analytics is widely applied across diverse domains within cyber-physical systems (CPS). CPS devices equipped with networked cameras are wirelessly connected to servers for complex vision-based analytics and intelligent operations. Adaptive video streaming is a pivotal technique in these applications to effectively deliver video content to servers under varying network conditions, enabling complex analytics afterward. Our thorough data analysis reveals that conventional offline video streaming control policies cannot effectively adapt to the high dynamics in networks and industrial video scenes. This results in suboptimal analytic performance and necessitates online adaptation for streaming control policy models. Yet, updating control policy models requires ground-truth analytics results which are unavailable directly on end devices due to their limited capacity. Furthermore, naively streaming original videos to the server for online adaptation is greatly challenged by scarce and dynamic networks, leading to decreased accuracy and increased transmission costs. In this paper, we present RIVA, a novel Online Learning-enabled adaptive streaming framework for Real-time Industrial Video Analytics. To facilitate communication-efficient online retraining, we design a hierarchical reinforcement learning approach in which the upper-level module intelligently determines the timing for online retraining, balancing Quality of Service (QoS) improvement and communication cost. Meanwhile, the lower-level module dynamically allocates bitrate to maximize QoS. Extensive experiments based on real-world industrial video and network datasets demonstrate that our proposed framework achieves a 22.6% mean accuracy increase, a 64.9% decrease in the mean failure rate of video uploading, and a 60.2% mean latency decrease compared to the state-of-the-art solutions.

The rapid advancement of 6G, cloud-fog computing, and internet of things (IoT) has revolutionized the control paradigm of smart grid. With the closed coupling between communication and power domains, control performance heavily relies on timely and secure sensing, transmission, and computing of grid state information. Conventional approaches which treat the four sectors as separate subsystems suffer from slow convergence and even cascading control oscillations. In this paper, we address the key research problem of sensing-transmission-computing-control integrated optimization to minimize the overall voltage deviation. A timeliness-driven integrated optimization algorithm is proposed, where proactive optimization of communication resource adaptation and power-domain control decisions is conducted based on the evolution of information timeliness loss in sensing, transmission, and computing, as well as its impact on control accuracy. Particularly, a self-penalty based cost function is developed to quantify the mismatch between communication-domain resource allocation and voltage control deviation. Moreover, a novel timeliness indicator, named age of trustworthy information (AoTI), is introduced to capture timeliness-trustworthiness performance loss on proportional-integral (PI) consensus control stability margin. Consensus weights are optimized based on AoTI to further enhance convergence speed and improve control accuracy. Simulation results demonstrate that the proposed algorithm significantly improves power-domain control stability, validating the efficiency of AoTI as a critical indicator for control information importance.

Along with the rapid development of the fourth industrial revolution, industrial cyber-physical systems (ICPS) are anticipated to achieve precise mapping and management for the physical world by integrating digital sensing and automated control. However, the conflict between limited computing resources and extensive sampling data, combined with severe industrial interference, exacerbates the system’s processing burden and diminishes its accuracy, hindering its ability to meet the low-latency and high-reliability control requirements. To address this issue, this paper investigates an end-edge collaborative control framework to enhance control performance for a short-packet transmission ICPS by providing powerful computation capability. We utilize the age of information (AoI) to characterize the impact of information freshness on control accuracy and construct an AoI-aware control law to assist in data sensing, transmission, and computing strategy design. In addition, we consider the influence of sampling and short-packet decoding errors in AoI-aware control performance to enhance the reliability of sampling and transmission strategies design. A joint optimization scheme of sampling interval, sampling time, computation offloading, and bandwidth allocation based on the block coordinate descent method and game theory is proposed to achieve a tradeoff between the control cost and energy consumption. By considering a real-world trolley inverted pendulum manipulation model, numerical results verify the performance gain of the proposed end-edge collaborative framework and the effectiveness of the presented algorithm.

With the rapid advancement of technologies such as B5G/6G and edge computing, network scenarios are becoming increasingly complex and diverse, leading to the emergence of slicing networks. Virtualizing applications into distinct categories and establishing corresponding network slices ensures performance to a certain extent. However, the challenges posed by the complex slicing environment demand more fine-grained routing control and higher costs to locate requested content or services, areas where current state-of-the-art methods fall short. To address these challenges, this work introduces a system framework that integrates the principles of Segment Routing over IPv6 (SRv6). An SRv6 optimization layer is created between the control and infrastructure layers to manage slices effectively and enhance routing control. Additionally, we propose a novel policy routing method based on zero-trust and Graph Convolutional Network (GCN) technology. This method transforms actions into policies that can be flexibly deployed on SRv6 nodes, segment by segment. These actions encompass both routing and security measures, allowing for dynamic and flexible deployment of policies on each segment to achieve the desired goals. This integration of segment routing and zero-trust principles simplifies implementation and enhances security. Comprehensive experiments were conducted to evaluate the proposed method. The results demonstrate significant improvements over state-of-the-art methods, including a higher service acceptance rate, better resource utilization, and reduced average latency and packet loss rate.

Finding the triangles of large scale graphs is a fundamental graph mining task in many applications, such as motif detection, microscopic evolution, and link prediction. The recent works on triangle counting can be classified into merge-based or binary search-based paradigms. The merge-based triangle counting paradigm locates the triangles using the set intersection operation, which suffers from the random memory access problem. The binary search-based triangle counting paradigm sets the neighbors of the source vertex of an edge as the lookup array and searches the neighbors of the destination vertex. There are lots of expensive lock operations needed in the binary search-based paradigm, which leads to low thread efficiency. In this paper, we aim to improve the triangle counting efficiency on GPU by designing a lock-free policy named Skiff to implement a hash-based triangle counting algorithm. In Skiff, we first design a hash trie data layout to meet the coalesced memory access model and then propose a lock-free policy to reduce the conflicts of the hash trie. In addition, we use a level array to manage the index of the hash trie to make sure the nodes of the hash trie can be quickly located. Furthermore, we implement a CTA thread organization model to reduce the load imbalance of the real-world graphs. We conducted extensive experiments on NVIDIA GPUs to show the performance of Skiff. The results show that Skiff can achieve a good system performance improvement than the state-of-the-art (SOTA) works.

Unmanned Aerial Vehicles (UAVs) have gained great attention in Internet-of-Things (IoT) applications benefiting from the flexibility of deployment and line-of-sight (LoS) channel conditions. In this paper, we study a UAV-assisted Mobile Edge Computing (MEC) system for providing services to large-scale IoT nodes (INs). In the considered system, the UAV acts as an Aerial Base Station (ABS) that can selectively access large-scale INs to enable efficient data collection and computational offloading while ensuring data integrity. Specifically, we first derive the reconstruction error upper bound based on Graph Laplacian Regularization (GLR) as the data integrity metric. Considering that the UAV is usually limited in energy consumption, we propose an energy efficiency (EE) maximization problem that jointly optimizes the selection of INs, the scheduling of INs, the 3D flight and the computational resource allocation of the UAV, subject to constraints related to UAV motion, resources and data integrity. Due to the non-convex nature of the considered problem, a two-stage algorithm called GDA-3DNACRA is proposed, which adopts Gershgorin Disk Alignment (GDA), Convex Relaxation, and Successive Convex Approximation (SCA) for solving it efficiently. Simulation results have shown that the proposed approach can significantly improve the EE of the UAV while ensuring the data integrity.

Wi-Fi is a very common means for providing wireless access to the Internet, e.g., using the 2.4GHz Industrial, Scientific, and Medical (ISM) band and more recently also the 6 GHz band via Wi-Fi 6E. Thanks to a chip recently launched by Semtech, in the same 2.4GHz band now can also operate Long Range (LoRa), which is widely used in Internet of Things (IoT) applications due to its low power consumption and wide coverage range. To allow for data interchange among these technologies, multi-radio gateways are needed, which introduce additional costs, complexities, and potential points of failure. To address this challenge, we propose the concept of Wireless to LoRa (WiLo) to make directional communication from Wi-Fi to LoRa. WiLo uses physical-layer (PHY) communication and dedicated input chips in the 2.4 GHz band to transmit information. To overcome the modulation technique differences between Wi-Fi and LoRa, WiLo leverages narrow-band communication, a technique that generates ultra-narrowband signals using single-tone sinusoidal signals by manipulating the payload of Wi-Fi devices. These signals can be detected by LoRa Wide Area Network base stations due to their high receiver sensitivity for long-range communication. Our experiments, which make use of both Universal Software Radio Peripheral (USRP) and commodity devices, demonstrate that WiLo can achieve concurrent wireless communication over a distance of 500 m, from commercial Wi-Fi chips to a LoRaWAN, with more than 96% frame reception rate. These findings show the effectiveness of WiLo in enabling reliable and efficient wireless communication over long distances, making it particularly relevant for applications such as remote monitoring systems, sensor networks, and smart cities.

The increasing complexity of real-time healthcare necessitates intelligent systems for dynamic data management and personalized assistance. This paper proposes a novel dual-LLM framework that integrates large language models (LLMs) into wireless healthcare networks. The first LLM powers an interactive artificial intelligence module (IAIM) embedded within a mobile edge computing (MEC) environment, which dynamically optimizes user-specific data routing and reconfigurable intelligent surface (RIS) configurations via a modified proximal policy optimization (PPO) algorithm. A novel Greedy Look-Ahead Algorithm (GLAA) is introduced for real-time path selection based on signal strength, emergency factors, and user-specific parameters. The second LLM, utilizing a retrieval-augmented generation (RAG) approach, serves as a personalized healthcare chat assistant that delivers context-aware patient support using real-time and historical data. Simulation results demonstrate that the proposed IAIM achieves a 9.6% reduction in network overhead compared to manual modeling and reduces latency by up to 52.5% over baseline PPO approaches, thus enabling enhanced user experience and responsiveness in healthcare systems.

The application of multispectral image based partial discharge detection offers a dependable solution for high-voltage substations. Captured visible light and ultraviolet (UV) images are denoised, transmitted and fused to enhance detection performance. However, existing approaches separately design the sensing-layer image denoising, communication-layer image transmission, and computing-layer image fusion, and the lack of unified cooperation hinders the overall performance. To address this issue, it is crucial to integrate sensing, communication, and computing to improve detection accuracy and timeliness. In this paper, we formulate a timeliness and accuracy joint guarantee problem, which aims to minimize the weighted sum of peak age of information (AoI), false-positive detection ratio, and false-negative detection ratio by jointly optimizing sensing-layer filtering window size, communication-layer time division ratio, and computing layer wavelet decomposition level. We propose a multispectral integrated sensing, communication, and computing algorithm based on AoI and false-negative aware multi-experience replay cooperative learning to solve the problem. Simulation results demonstrate that the proposed algorithm outperforms existing methods in terms of peak AoI, false-positive detection ratio, false-negative detection ratio, and convergence speed.

In recent years, the Internet of Medical Things (IoMT) has significantly boosted the healthcare industry. Federated learning (FL) can enhance the utilization of patient data while protecting privacy. Despite the great potential of FL to enhance the architecture of IoMT, the need for effective interference management and the limited energy resources of IoMT devices make the integration of FL into IoMT environments particularly challenging. This study proposes an innovative framework to address these challenges by optimizing power allocation and client selection across participating IoMT devices in the FL process. By employing a Stackelberg game model, our approach orchestrates power allocation among IoMT devices to enhance communication efficiency while adhering to strict differential privacy (DP) standards. Regarding the availability of network state information, we propose non-uniform pricing and uniform pricing strategies, respectively. Then, we derive the optimal interference price and power for the IoMT devices using nonlinear programming and convex optimization. To tackle the issue of energy constraints in IoMT devices, we adopt Lyapunov optimization for adaptive client selection, ensuring sustainable device participation in the FL process over time. In addition, our approach integrates DP to protect patient data, carefully balancing between privacy and the accuracy of the learning model. Our extensive simulations demonstrate marked improvements in privacy preservation, communication efficiency, and energy management efficiency, highlighting the effectiveness of our proposed method over existing solutions.

Satellite networks have long been regarded as a vital component of space communication systems, which provide integrated satellite-terrestrial broadband access in seamless coverage and cost-effective manner. The inter-satellite routing design for low earth orbit (LEO) satellite constellations is critical for achieving low-latency and high-reliability communication in the space communication systems. However, the inherent dynamic nature of LEO satellites, coupled with the variability in inter-satellite connectivity, imposes significant challenges for routing efficiency and network dependability. Existing routing schemes cannot handle such topological fluctuations due to their insensitivity to real-time network changes, thus suffering from performance degradations in highly dynamic space environments. This paper presents Iris, an intelligent reliable routing scheme for inter-satellite communication, aiming at increasing efficiency and reliability of the packet transmission process. Specifically, we propose a comprehensive deep reinforcement learning (DRL) framework that learns a policy to select routing paths automatically under the emerging software-defined satellite networking (SDSN) architecture. To strengthen fault-tolerance in fluctuating environments, we train an agent in an incremental manner by gradually increasing scenario complexity. Simulation results indicate that our solution significantly outperforms baselines and exhibits advances in adaptability and reliability, especially under dynamic environments with frequent topology changes.

Mission-critical applications are of significant importance to sixth generation (6G)’s massive and ubiquitous Internet of things (IoT) communications. The mission-critical applications mostly fall within the scope of finite blocklength (FBL), and in order to assess the information freshness, age of information (AoI) has been introduced. However, packet error is inevitable in the FBL regime, which exerts impacts on the time for successful packet transmission, and thus increases the AoI. To optimize the performance of AoI, the management of queue packets is an effective way. Motivated by optimizing the AoI performance in the FBL regime, we consider a system equipped with a single buffer, and propose two schemes of packet management in this article. We subsequently derive the closed-form expressions for the average AoI and the average peak AoI and we discuss the relationship between AoI and the factors, i.e. the blocklength, data generation rate and signal-to-noise ratio. Afterwards, we give the optimal blocklength expression associated with the optimal AoI. In order to examine the information timeliness in the network under the proposed schemes, the closed-form expressions of the average delay are deduced. The simulation results validate the theoretical analysis and demonstrate the advantage of the proposed scheme in terms of the performance of AoI, delay, and their trade-off.

In advanced vehicular networks, Roadside Unit (RSU)-based amplify-and-forward (AF) relay and passive Reconfigurable Intelligent Surface (RIS) are two potential helpers to enhance the vehicle-to-vehicle (V2V) communications when the direct link experiences poor quality. This paper presents a comprehensive comparison of the two enhancement modes from the outage performance perspective. In the presence of both direct link and enhanced link, the analytical expressions of the outage probability (OP) for the V2V communication under the two enhancement modes are derived respectively. Moreover, considering the co-channel interference caused by relay/RIS, the OP of the neighbouring vehicle-to-infrastructure (V2I) communication is also derived. Additional analysis compares the diversity order and the strength of interference created by the V2V communication under the two enhancement modes. Further discussions are presented on the effect of the channel estimation error and phase quantization error under the RIS mode. Finally, the pros and cons of the two enhancement modes are demonstrated by both the analytical and numerical results.

Mobile-edge computing (MEC) and wireless power transfer (WPT) are pivotal for enhancing computational power and battery life in 5G/6G networks. However, their performance declines in remote or disaster-stricken areas due to the lack of access points and energy sources. This paper proposes a wirelessly powered unmanned aerial vehicle enabled MEC (UAV-MEC) system to address this issue, focusing on nodes with ignorable computing capabilities and randomly arriving, size-varying tasks. We aim to maximize the long-term average computation rate under constraints such as UAV coverage, time resources, energy, and task causality, formulating a non-convex problem with dynamic states and complex actions. To solve this problem, we introduce an exploration-enhanced deep reinforcement learning (EDRL) algorithm with a bi-layered structure: the main problem determines the UAV’s flying actions, while the sub-problem allocates time resources given these actions. EDRL employs a deep neural network to analyze real-time UAV positions and task demands, determining optimal flight paths. Upon path determination, an efficient algorithm utilizing bisection and golden section search methods allocates WPT and computational offloading durations. Simulations reveal that EDRL achieves an execution latency of just 11.5 ms in thirty-node networks, outperforming baseline DRL algorithms and predetermined trajectory schemes by 20% and 25% in long-term computation rates, respectively. These results highlight EDRL’s effectiveness and low computational complexity, making it a robust solution for challenging environments.

Social financial technology focuses on trust, sustainability, and social responsibility, which require advanced technologies to address complex financial tasks in the digital era. With the rapid growth in online transactions, automating credit card fraud detection and loan eligibility prediction has become increasingly challenging. Classical machine learning (ML) models have been used to solve these challenges; however, these approaches often encounter scalability, overfitting, and high computational costs due to complexity and high-dimensional financial data. Quantum computing (QC) and quantum machine learning (QML) provide a promising solution to efficiently processing high-dimensional datasets and enabling real-time identification of subtle fraud patterns. However, existing quantum algorithms lack robustness in noisy environments and fail to optimize performance with reduced feature sets. To address these limitations, we propose a quantum feature deep neural network (QFDNN), a novel, resource efficient, and noise-resilient quantum model that optimizes feature representation while requiring fewer qubits and simpler variational circuits. The model is evaluated using credit card fraud detection and loan eligibility prediction datasets, achieving competitive accuracies of 82.2% and 74.4%, respectively, with reduced computational overhead. Furthermore, we test QFDNN against six noise models, demonstrating its robustness across various error conditions. Our findings highlight QFDNN’s potential to enhance trust and security in social financial technology by accurately detecting fraudulent transactions while supporting sustainability through its resource-efficient design and minimal computational overhead.

In recent years, more and more scholars have begun to focus on sentiment analysis on social media. Current sentiment analysis collects all relevant data, including public thoughts, opinions, and feelings, from a variety of open sources. In addition, it automatically predicts different aspects of outcomes or trends based on information collected globally in real time. This research area explores how to extract sentiment information from different modalities (e.g., text, images, and audio). However, the currently existing techniques face several challenges. It is difficult to achieve effective interaction with completely heterogeneous data, and these techniques cannot adequately guarantee data security during data interaction, which is particularly important when dealing with sensitive information. Therefore, this article introduces existing methods for protecting data privacy. Based on this foundation, we propose a novel algorithm called collaborative dynamic hierarchical federated learning with hypernetwork aggregation (CDHFL-HA), which is suitable for sentimental analysis. CDHFL-HA ensures that the data remain local to each participant while leveraging the data similarity between participants on the server and processing interference data on the participant to enhance the accuracy of the current sentimental analysis. In addition, an essential aspect considered in the proposed algorithm is explainability. Understanding the decisions and predictions made by sentiment analysis models is crucial for gaining trust and acceptance in real-world applications. CDHFL-HA incorporates explainability features, providing insights into the decision-making process, thus enhancing the interpretability of sentiment analysis results. Numerous experimental results show that the algorithm outperforms existing algorithms in complex scenarios, with a minimum accuracy of 0.6007 and a maximum of 0.9962. In addition, it can be seen from the experimental results in this article, that the communication parameters in the experiments are similar to those of other federated learning, while the number of training rounds is improved by up to 50% (i.e., 20 rounds faster) relative to other algorithms.

This paper investigates fixed-time prescribed performance control issue of vehicular platoon subject to input nonlinearities induced by actuator dead-zone and saturation. Due to the occurrence of input nonlinearities, the spacing error increases, which may exceed the desired performance requirement. To deal with the dilemma, by merging an auxiliary variable, an improved prescribed performance control scheme is developed with a remarkable advantage that the performance boundary can be self-adjusted when actuator nonlinearities occur, while guaranteeing the tracking error tend to the predefined region in a fixed time. Then, with the help of an error transformation, an adaptive fixed-time sliding mode control approach is proposed, in which a new compensator with faster convergence is designed to eliminate the influence of actuator nonlinearities in a better way. The rigorous analysis shows that the given scheme is capable of guaranteeing fixed-time compounded individual vehicle stability, fixed-time string stability and reachability of fixed-time prescribed performance. Lastly, numerical simulations and experiments are carried out to verify the feasibility of the proposed platoon control protocol.

Traffic speed prediction is significant for intelligent navigation and congestion alleviation. However, making accurate predictions is challenging due to three factors: 1) traffic diffusion, i.e., the spatial and temporal causality existing between the traffic conditions of multiple neighboring roads, 2) the poor interpretability of traffic data with complicated spatio-temporal correlations, and 3) the latent pattern of traffic speed fluctuations over time, such as morning and evening rush. Jointly considering these factors, in this paper, we present a novel architecture for traffic speed prediction, calledInterpretable Causal Spatio-Temporal Diffusion Network(ICST-DNET). Specifically, ICST-DNET consists of three parts, namely the Spatio-Temporal Causality Learning (STCL), Causal Graph Generation (CGG), and Speed Fluctuation Pattern Recognition (SFPR) modules. First, to model the traffic diffusion within road networks, an STCL module is proposed to capture both the temporal causality on each individual road and the spatial causality in each road pair. The CGG module is then developed based on STCL to enhance the interpretability of the traffic diffusion procedure from the temporal and spatial perspectives. Specifically, a time causality matrix is generated to explain the temporal causality between each road’s historical and future traffic conditions. For spatial causality, we utilize causal graphs to visualize the diffusion process in road pairs. Finally, to adapt to traffic speed fluctuations in different scenarios, we design a personalized SFPR module to select the historical timesteps with strong influences for learning the pattern of traffic speed fluctuations. Extensive experimental results on two real-world traffic datasets prove that ICST-DNET can outperform all existing baselines, as evidenced by the higher prediction accuracy, ability to explain causality, and adaptability to different scenarios.

Bike-sharing demand prediction involves complex, dynamic spatio-temporal dependencies and various influencing factors, thus becomes one of technical challenges in intelligent transportation systems. Existing methods often rely on predefined adjacency matrices based on distance or road connectivity, and typically ignore multi-scale temporal features and external factors such as weather, holidays, social events, and so on. To address these limitations, we propose a model based on dynamic time warping (DTW) and spatio-temporal graph attention network (GAT) to improve the accuracy of bike-sharing demand prediction. In the proposed model, we use a data-driven approach to construct an adjacency matrix that effectively reflects the real dependencies between bike-sharing stations, and temporal attention mechanism is integrated with graph attention network to capture dynamic spatio-temporal correlations hidden in the data. Moreover, multi-scale temporal gated convolutions are applied to fuse short-term and long-term temporal features. The experimental results demonstrate that our proposed model significantly outperforms recent baseline methods in terms of MAE and RMSE evaluation metrics. Meanwhile, we find that the external factors of weather, public facilities and traffic accidents have different influence on results, and the weather has the greatest impact on bike-sharing demand.

Intelligent resource allocation maintains a better quality of service among devices in next-generation heterogeneous network infrastructures (NG-HetNetIs). NG-HetNetIs include industry 5.0 enabled infrastructures like Internet of Things (IoT), cognitive radio (CR) enabled B5G and 6G networks, unmanned aerial vehicles (UAVs), wireless sensor networks (WSNs) and autonomous vehicles (AVs). Digital twin (DT) joins hand with cognitive radio and resource aggregation technologies to provide the integrated framework for intelligent resource allocation in NG-HetNetIs. In NG-HetNetIs, the obtained statistics of measured radio activity as prior information play an instrumental role in enabling optimized resource allocation using context awareness. Unfortunately, the already available static approaches are inefficient to replicate (DT) the radio activity in a heterogeneous radio environment. To address the issue, static implementation framework is extended as dynamic radio activity characterization framework (DRAC) to have context awareness in NG-HetNetIs. The proposed DRAC replicates (DT) the wide sense stationarity of time and carrier aggregated radio activity due to its exploitation of more localized temporal and spectral information in NG-HetNets. The obtained localized statistics using DRAC can be exploited as appropriate prior knowledge and test statistics during the spectrum sensing phase of NG-HetNetIs for intelligent resource allocation instead of a single statistic obtained by the static approach.

Cloud-enabled edge computing scenarios can intelligently cache and update the content on a periodic basis, thereby enhancing users' overall perception of quality, which is called quality of experience (QoE). To enhance the QoE, we aim to the multi-objective optimization, which maximizes the cache hit ratio while simultaneously minimizing traffic load and time latency. To address this issue, we focus on employing an innovative algorithm named HT-PAD, which provides a complete solution for prediction and decision-making for proactive caching. First, to improve the prediction accuracy of the cached content, we use the encoding layer in hyperdimensional computing to extract the information features. Second, HD-Transformer, as the prediction part of HT-PAD, is proposed to make predictions based on user preferences, historical information, and popular information. HD-Transformer uses DNN to predict user preferences and process time series data by combining hyperdimensional computation with Transformer. Third, to avoid error in the prediction content, we employ PER-MADDPG as the decision-making part of HT-PAD, which consists of Multi-Agent Deep Deterministic Policy Gradient (MADDPG) and Prioritized Experience Replay (PER). We use MADDPG to enhance the content decision-making and utilized PER to select appropriate training samples for PER-MADDPG. Finally, our experiments have shown that our proposed approach achieves the strong performance in terms of the edge hit ratio, the latency, and the traffic load, thus improving the QoE

The Digital Twin Network (DTN) establishes a real-time virtual mirror of physical networks. Data collection plays an essential role in DTN, which collects the status data of physical network for building highly consistent digital twins. In this paper, we present a network-wide data collection scheme based on In-band Network Telemetry (INT). To build a lifelike mirror of the physical network, the probing path set is required to cover all links so that network topology, traffic load, and port-level device information is captured. We present a Latency-aware High-degree Replicated First (LHRF) vertex-cut graph partitioning algorithm to partition the network into several balanced subgraphs while trying to replicate the high-degree vertexes among partitions first. LHRF aims to balance the length and accumulated latency of the probing paths. With shorter and stabler probing latencies, the information received by digital twin can reflect the latest and consistent network-wide status. To prevent the packets from being fragmented due to overlong paths, a deep limited search (DLS) based path planning algorithm is employed to generate non-overlapped probing paths covering all edges in the separated subgraphs. Simulation results demonstrate that the proposed scheme generates more balanced INT paths with constrained path length and shorter, stabler probing delay.

In mobile edge computing (MEC), task offloading can significantly reduce task execution latency and energy consumption of end user (EU). However, edge server (ES) resources are limited, necessitating efficient allocation to ensure the sustainable and healthy development for MEC system. In this paper, we propose a dynamic pricing mechanism based near-optimal resource allocation for elastic edge offloading. First, we construct a resource pricing model and accordingly develop the utility functions for both EU and ES, the optimal pricing model parameters are derived by optimizing the utility functions. In the meantime, our theoretical analysis reveals that the EU’s utility function reaches a local maximum within the search range, but exhibits barely growth with increased resource allocation beyond this point. To this end, we further propose the Dynamic Inertia and Speed-Constrained particle swarm optimization (DISC-PSO) algorithm, which efficiently identifies the near-optimal resource allocation. Comprehensive simulation results validate the effectiveness of DISC-PSO algorithm, demonstrating that it significantly outperforms existing schemes by reducing the average number of iterations to reach a near-optimal solution by 86.88%, increasing the EU utility function value by 0.13%, and decreasing the variance of results by 96.78%.

Artificial Intelligence of Things (AIoT) utilizes a combination of computing, storage, and networking resources to provide highly reliable and low-latency information services to the industrial production processes. However, with the increasing integration of numerous smart terminals into real-time sensing, autonomous decision-making, and precision manufacturing execution systems, the current task scheduling pattern appears to be insufficient to meet the latency requirements of computationally intensive tasks. To address the above challenge, this paper presents a collaborative edge-terminal task offloading scheme. First, the Task Backlog and Multi-slot Scheduling (TBMS) problem is converted from a long-term offloading problem to a single timeslot scheduling problem by Lyapunov optimization. Then, to simplify the problem, the single timeslot problem is decomposed into three subproblems: the local resource allocation problem, the server resource allocation problem, and the indicator weight selection problem. The two resource allocation problems are proved to be convex, which have been solved by using the Bisection method and the Karush-Kuhn-Tucker (KKT) method, respectively. For the indicator weight selection problem, we proposed the enhanced jumping spider optimization algorithm that integrates the elite opposition-based learning strategy. Extensive experiments show that the proposed algorithm can alleviate the computing pressure of the terminal device. Compared with the traditional methods, the offload system cost is effectively reduced by at least 58.8% and the average execution success rate is increased by at least 6%.

The rapid development of satellite technology has significantly enhanced satellite computing service capabilities, particularly in terms of its application potential for complex tasks such as hyperspectral image (HSI) processing. Satellite edge computing (SEC) substantially improves processing efficiency by transferring task processing to the satellite. At the same time, intelligent reflective surfaces (IRS) reduce the pressure on ground service center communication resources by optimizing communication links between satellites on the ground. However, existing works mainly optimize general computing tasks, resulting in limited performance when processing HSI tasks. This paper proposes an IRS-assisted HSI processing SEC system to achieve the optimal balance between HSI processing accuracy and system energy consumption. We formulate an optimization problem as a joint task covering HSI offloading, band selection, and IRS phase shift optimization to achieve optimal overall performance. To address the problem, we propose the joint feature iterative optimization (JFIO) framework for HSI processing, which generates optimized task offloading solutions through graph attention networks, utilizes multi-feature attention capsule networks to achieve efficient band selection, and combines this with IRS modules to optimize communication link conditions. Extensive experiments on various datasets demonstrate that the proposed framework achieves an excellent balance between accuracy and energy consumption, with its performance significantly outperforming other baseline methods.

Wireless train communication network (WLTCN) is an emerging technology for enabling intelligent rail vehicles. It is responsible for providing train control services (TCS), passenger information services (PIS), and train sensing services (TSS). These services within WLTCN have notably different quality of service (QoS) requirements from traditional telecommunication services. In this paper, to incorporate multiple services in a single WLTCN, we propose a radio access network (RAN) slicing architecture empowered WLTCN to satisfy the demands of services and save bandwidth resource. In particular, the service and slicing models of TCS, PIS, and TSS are investigated. By analyzing the heterogeneous characteristics and QoS requirements of the above services within WLTCN, we exploit the orthogonal multiple access scheme for TCS and PIS and the non-orthogonal multiple access scheme for TSS, respectively. The system bandwidth minimization problem is formulated with slicing resource allocation for TCS, PIS, and TSS and non-orthogonal access grouping for TSS terminals as a mixed-integer nonlinear programming (MINLP). To solve the intractable MINLP, the original problem is transformed and decoupled into the two subproblems. Then, we propose a joint bandwidth optimization and terminal clustering (JBOTC) algorithm to tackle the bandwidth allocation problem with optimal terminal grouping strategy for TSS effectively. The closed-form expressions of the optimal bandwidth allocation strategy for three services are derived. The simulation results illustrate the performance superiority for saving bandwidth of the JBOTC algorithm to the benchmark schemes. Our proposed slicing strategy enables WLTCN to support heterogeneous services co-existence with minimal bandwidth consumption.

This paper proposes a secrecy enhancement scheme for the space shift keying (SSK) assisted multiple-input single-output (MISO) visible light communications (VLC) system in a complex indoor environment, where the line-of-sight (LoS) link of the transmitter and legitimate user can be blocked or exist. By leveraging a properly arranged mirror array as an optical intelligent reflecting surface (ORIS), a legitimate user can access confidential information, while an eavesdropping user cannot intercept the confidential message. To achieve this goal, an optical artificial noise (OAN) assisted secrecy enhancement strategy is introduced. In this strategy, the transmitter transmits both the desired signal and the OAN signal simultaneously while adhering to power and amplitude constraints. The average mutual information (AMI) and achievable secrecy rate (ASR) are employed to analyze the secrecy performance of the ORIS aided SSK VLC system. Furthermore, to adapt to different environments, four system configuration scenarios are presented, and the corresponding secrecy performance is analyzed. To clarify the theoretical results of the OAN assisted indoor MISO SSK VLC system with an ORIS, extensive simulation results are performed.

Incorporating an active reconfigurable intelligent surface on an autonomous aerial vehicles (AAVs), denoted as an aerial reconfigurable intelligent surface (ARIS), introduces a novel dimension for secure transmissions. Given the constraint of limited battery capacity in AAVs, energy management emerges as a key challenge within AAV networks. In response, we propose a secure energy efficiency (SEE) transmission scheme for ARIS networks, where active ARIS is strategically deployed to enhance information security. In addition, a SEE optimal problem is formulated by considering the imperfect wiretap channel state information to optimize the active beamforming vector and the ARIS position. For this non-convex problem, we first reformulate the fractional SEE objective into an equivalent form and subsequently decompose it into two distinct subproblems: optimizing the AAV’s position and designing the active beamforming. For the AAV’s position optimization, we propose a sophisticated deep deterministic policy gradient algorithm that enables the AAV to autonomously determine the optimal ARIS position through a self-learning strategy. Regarding beamforming design, we transform this aspect into a quadratic constrained quadratic programming problem and design an alternating direction multiplier method to optimize the reflection coefficient. Subsequently, an alternating optimization algorithm is proposed to synergistically solve these subproblems. Empirical simulations validate our proposed scheme, indicating an improvement in SEE of up to 47.2%. This significant improvement underscores the efficacy of the proposed ARIS-assisted secure transmission scheme in enhancing both security and energy efficiency in AAV networks.

This paper investigates the secure transmissions in the Unmanned Aerial Vehicle (UAV) communication network facilitated by a Reconfigurable Intelligent Surface (RIS). In this network, the RIS acts as a relay, forwarding sensitive information to the legitimate receiver while preventing eavesdropping. We optimize the positions of the UAV at different time slots, which gives another degree to protect the privacy information. For the proposed network, a secrecy rate maximization problem is formulated. The non-convex problem is solved by optimizing the RIS’s phase shifts and UAV trajectory. The RIS phase shift optimization problem is converted into a series of subproblems, and a non-linear fractional programming approach is conceived to solve it. Furthermore, the first-order taylor expansion is employed to transform the UAV trajectory optimization into convex function, and then we use the deep Q-network (DQN) method to obtain the UAV’s trajectory. Simulation results show that the proposed scheme enhances the secrecy rate by 18.7% compared with the existing approaches.

As we advance towards sixth-generation (6G) communications, the integration of dual-functional radar and communication (DFRC) systems with active reconfigurable intelligent surfaces (RIS) emerges as a promising solution to enhance spectrum efficiency. This work introduces an innovative DFRC system enhanced by an active RIS, capable of amplifying and phase-shifting signals to improve communication quality. We propose an optimization algorithm that maximizes combined data rates while considering constraints on radar probing power and power budgets for both DFRC and active RIS. Employing techniques such as weighted minimum mean square error (WMMSE) and fractional programming, our simulation results demonstrate that active RIS-enhanced DFRC systems significantly outperform passive RIS and non-RIS setups in terms of Weighted Sum Rate (WSR). Further analyses reveal that active RIS effectively mitigates multiplicative fading and enhances signal directivity, with performance gains influenced by the number of RIS elements, total system power, and radar detection power ratio. This work highlights the transformative potential of active RIS in optimizing next-generation wireless networks.

This paper investigates the deployment of a full-duplex integrated sensing and communication (ISAC) system for task offloading service to serve ultra-reliable low-latency communications (URLLC), significantly enhanced by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). Utilizing a non-orthogonal multiple access frame-work, this study extensively addresses the challenges associated with latency-sensitive task offloading from information receivers (IRs) to a mobile edge computing platform. We introduce a novel and sophisticated multi-agent deep reinforcement learning (MA-DRL) approach aimed at minimizing latency in task offloading under a variety of stringent ISAC-URLLC network constraints, including imperfect channel state information. The proposed MA-DRL operates on decentralized execution while maintaining centralized training, using multi-actor-critic networks to enhance learning and performance. The innovative reward decentralization framework in the present MA-DRL optimizes downlink and uplink communications through dynamic power allocation, precise beamforming, and intelligent phase shift management facilitated by the STAR-RIS. The proposed MA-DRL framework significantly outperforms existing multi-agent DRL algorithms, demonstrating substantial gain in reward maximization (i.e., linked to offloading latency minimization) by 27.46% and 52.73%.

Internet of Things (IoT) is crucial for the hierarchical medical system, and enables the real-time monitoring and collection of data, thereby improving patient treatment outcomes. However, achieving secure, efficient, timely, and controllable medical IoT data sharing between higher-lever hospital (HLH) and lower-level hospital (LLH) is a challenging task for the hierarchical medical system. Consortium blockchain, which is an effective way to achieve secure and trustworthy data sharing, has the potential to address these issues. In this article, we propose a novel cloud-chain sharing scheme for medical IoT data based on consortium blockchain. HLH and LLH establish a consortium blockchain, where medical IoT data is stored both on-chain and off-chain. On-chain data adopt a proxy re-encryption based on elliptic curve cryptography (ECC-PRE) strategy and attribute-based strategy to facilitate secure access and controlled sharing of data. Off-chain data sharing provides three different modes: private data collection (PDC), direct channel, and cloud storage (CS), according to the urgency of patient and the sensitivity of the data. Furthermore, a file security breakpoint resume scheme, rooted in the consortium blockchain, and a file weighting strategy are employed to enhance the efficiency and timeliness of data sharing. Finally, the performance of our proposed solution is verified by experimental results, and the results demonstrate our solution is feasible and efficient. In future work, we plan to use searchable encryption technology to make this scheme more versatile and gradually implement dynamic adjustment of permissions.

This paper explores a cutting-edge terahertz (THz) integrated sensing and communication system (ISAC) that utilizes active simultaneously transmitting and reflecting reconfigurable intelligent surfaces (A-STAR-RIS). The system incorporates a novel dynamic delay alignment (DDA) modulation technique, allowing signals from different paths to reach the receiver simultaneously, eliminating the need for complex channel equalization and mitigating inter-symbol interference, while considering the dynamic movement of the vehicles, and accounting for time-selective fading and uniform Doppler power spectra (DPS) model. Our system features a dual-function radar and communication multiple antenna base station (BS), serving both communication and target sensing functions concurrently through an A-STAR-RIS. The objective is to maximize the sum rate by jointly optimizing BS transmit beamforming, A-STAR-RIS reflection and transmission beamforming matrices, vehicular unit (VU) mobility correlation parameters, and radar receive filter. Given the intricate nature of this non-convex optimization problem, owing to dynamic changes in communication links and the interplay of multiple variables, traditional optimization methods prove challenging. To overcome this, we propose a machine learning (ML) based deep deterministic policy gradient (DDPG) algorithm. Our simulations validate the substantial benefits of A-STAR-RIS over conventional benchmark scenarios.

The Internet of Vehicles (10V) brings significant economic benefits to countries. However, large-scale smart vehicle production planning remains challenging in the 10V. Currently, heuristic algorithms and solvers commonly used for these problems often lack scalability and fall into local optima. Moreover, security concerns about wireless data transfer arising from multi-factory manufacturing processes are garnering attention. To address these issues, this paper introduces an algorithm, TRL, which is a Transformer-based Reinforcement Learning for vehicle production planning problems. Furthermore, we propose a Transformer-based Federated Reinforcement Learning algorithm, named TFRL, tailored for large-scale manufacturing and secure wireless communication. Experimental results showcase the high performance and security of TFRL. It schedules 1000 orders in about 14 seconds and avoids exchanging plaintext during the interaction. Compared to Non-dominated Sorting Genetic Algorithm II(NSGA-II), the TFRL enhances computational speed by 95.12% and reduces constraint violation scores by 93.18%.

With the advent of the multimodal immersive communication system, people can interact with each other using multiple devices for sensing, communication and/or application level control either onsite or remotely. As a breakthrough concept, a distributed sensing, computing, communications, and control (DS3C) fabric is introduced in this paper for provisioning 6G services in multi-tenant environments in a unified manner. The DS3C fabric can be further enhanced by natively incorporating intelligent algorithms for network automation and managing networking, computing, and sensing resources efficiently to serve vertical use cases with extreme and/or conflicting requirements. As such, the paper proposes a novel end-to-end 6G system architecture with enhanced intelligence spanning across different network, computing, and business domains, identifies vertical use cases and presents an overview of the relevant standardisation and pre-standardisation landscape.

Container technologies promise efficient deployment of distributed services, but their potential is hampered by suboptimal resource utilization and network congestion stemming from initial placement decisions made without knowledge of future demands. Existing container cluster management strategies lack robust adaptive capabilities to efficiently balance load as workloads evolve unpredictably over time. This article puts forth the Adaptive Load-aware Container Deployment (ALCoD), a novel container cluster management approach integrating worst fit decreasing heuristic placement with deep Reinforcement Learning (RL)-based migration optimization. ALCoD adapts to fluctuating resource availability and service demands by leveraging the complementary strengths of each technique. The worst fit decreasing approach allows rapid initial cluster deployment when resources are abundantly available, while the deep RL policy orchestrates intelligent container migrations to optimize load balancing during times of resource scarcity, maintaining service availability throughout. Comprehensive evaluations verified that compared to state-of-the-art strategies, ALCoD reduces system response times by 29.19%, improves load balancing by 51.31%, and decreases bandwidth usage by 27.4% under real-world conditions. Beyond these raw performance improvements, ALCoD demonstrates the potential of hybrid algorithms that blend complementary techniques to match the intrinsic dynamics of container clusters. This pioneering approach establishes a solid foundation for realizing the full promise of containerized services through reliable, responsive delivery even as operating conditions continuously evolve.

With recent advents of industrial Internet of Things (IIoT), the connectivity and data collection capabilities of industrial equipment have be significantly enhanced, yet bringing new challenges for the remaining useful life (RUL) prediction. To fulfill the RUL predicting demand in multivariate time series, this work proposes an encoder-decoder model termed as dual-scale transformer model (DSFormer), built upon the Transformer architecture. First, in the encoder part, a dual-attention module is designed for the weight feature extraction from both dimensions of the sensor and time series, aiming to compensate for the diverse impacts of different sensors on the prediction. Next, a temporal convolutional network (TCN) module is introduced to capture sequence features and alleviate the loss of positional information incurred by stacking blocks. Then, the feature decomposition module is integrated into the decoder for trend feature extraction from sequences, providing the model with additional sequence information. Finally, compared to existing models, the proposed method can obtain the superior performance in terms of the root mean square error (RMSE) and Score metrics on the FD001, FD002 and FD003 subsets of the C-MAPSS dataset, with an average improvement of 3.2% and 2.5% respectively. In particular, the ablation experiment further validates the effectiveness of proposed modules in handling multivariate time series and extracting features.

In this article, we address the key problem of sensing and communication integrated resource allocation for 6G-empowered distribution grid hierarchical coordinated control. First, we construct a novel information timeliness metric for electric semantic communication, namely, Peak Age of Semantics (PAoS), which covers the entire lifecycle of information sensing, semantic compression, semantic transmission, and semantic decoding. Second, we propose a sensing and semantic communication integrated resource allocation algorithm based on Top-$\text {N}^{2}$and hybrid knowledge–statistic-driven fuzzy reinforcement learning. A deep fuzzy neural network is utilized to build a knowledge model between the grid operating state and decision making. The knowledge is embedded into statistic-driven model of reinforcement learning to enhance accuracy of upper confidence bound (UCB) utility evaluation. Finally, simulations based on realistic application scenarios indicate that compared with two comparison algorithms, the proposed algorithm reduces average PAoS by 4.72% and 9.49%, and the maximum PAoS by 5.76% and 13.57%. Additionally, its end-to-end delay trend and semantic packet decoding success rate align more closely with semantic importance.

With the development of internet of things (IoT) technology and its wide application in urban traffic, the next-generation vehicle-to-everything (V2X) communication network should support high-capacity, ultra-reliable, and low-latency massive information exchange to provide unprecedentedly diverse user experiences. The development of the sixth-generation (6G) mobile communication technology will pave the way for realizing this vision. Reconfigurable intelligent surfaces (RISs), a critical 6G technology, is expected to make a big difference in V2X communications when used in conjunction with unmanned aerial vehicles (UAVs), allowing for extremely increased communication capacity and reduced latency. We propose a UAV-enhanced RIS-assisted V2X communication architecture (UR-V2X) suitable for urban three-dimensional (3D) IoT traffic and design an adapted MAC protocol UR-V2X-MAC to accomplish communication resource allocation and scheduling. The UAVs are used as access points and resource allocation centers, while the RISs are used as passive relays to assist V2X communication in proposed architecture. To improve the performance of UR-V2X-MAC, we use a distributed optimization algorithm in the message report phase of the protocol to maximize the system capacity by allocating the transmit power and alternately optimizing the RIS phase shift matrix. We analyze the delay and system capacity characteristics under different parameter settings through theoretical derivation and protocol performance simulation. Analysis and simulation results are presented to demonstrate that UR-V2X-MAC achieves a reduction in communication delay and a significant increase in system capacity through detailed design and alternate optimization compared to the existing V2X MAC protocol and no-RIS case.

By 2050, there will be a 50% rise in energy demand, and existing natural and renewable resources will be under extreme scrutiny. Optimizing current power generation and transmission to reduce energy consumption, cost, and other factors is equally vital to upgrading methods for effectively harvesting renewable energy. However, it gets more challenging for conventional computers to perform optimization as the number of factors affecting power generation and transmission rises. Extreme environmental cases will consequently lead to the imperfect functioning of Internet of Things (IoT) systems. By utilizing quantum-mechanical properties, such as superposition and entanglement, quantum computers can computationally outperform classical computers while consuming much less energy. In this article, we investigate various quantum machine learning algorithms on two data sets (TWTDUS and SDWTT18) related to IoT extreme environment and study the effect of a noisy quantum environment. We observe that for the TWTDUS data set, the variational$UU^{\dagger }$with analytical clustering methods achieves the highest accuracy of 98.10%. Similarly, for the SDWTT18 data set, the$UU^{\dagger }$method with$k$-means clustering achieves an accuracy of 94.43%. The results show that the accuracy of the proposed quantum algorithms outperforms the existing classical methods and can be utilized to forecast output power generation daily by measuring the metrics required in energy sector decision-making situations. This will be useful to save energy and costs in an IoT-extreme environment, where energy organizations must decide instantly whether to start or stop generating units.

To enable resource-constrained wireless devices (WDs) to process the computation-intensive and latency-sensitive computation tasks, the wireless powered mobile edge computing (WP-MEC) network has been proposed as a promising approach. Incorporating mobile cloud computing (CC) in the WP-MEC network, we investigate the wireless powered mobile edge-CC (WP-MECC) network, where the WDs first harvest energy from a hybrid access point (HAP), and then consume the harvested energy to compute the tasks locally, offload them to the HAP for computation, or offload them to the cloud server (CS) via the relaying of the HAP. To pursue fairness among the WDs, we minimize the maximum task completion time (TCT) of WDs by jointly optimizing the time resources, computing mode selection, and computation resources. We prove the minimization problem is NP-hard. To tackle the problem, we decompose it into the subproblem and top problem, and propose an alternate optimization-based resource allocation and the computing mode selection (ARACM) algorithm with low computational complexity, which achieves a comparable performance with the exhaustive search method in terms of the minimal maximum TCT of WDs. Moreover, we propose a deep reinforcement learning (DRL)-based resource allocation and the computing mode selection (DRACM) algorithm with less execution latency than the ARACM algorithm. Numerical results show that the two proposed algorithms achieve satisfactory performance in terms of the minimal maximum TCT of WDs and execution latency.

Precisely predicting the location of the user in a Global-Navigation-Satellite-System-degraded environment is a highly challenging task. Localization based on cellular signal fingerprints is one of the promising solutions to this problem and has attracted increasing attention. Long Term Evolution (LTE) signal is popularly utilized for localization due to its global usage, extensive urban coverage, and favorable signal properties. This paper proposes a new multiband multicell Reference Signal Received Power (MBMC-R) fingerprint, which properly fuses LTE signals’ carrier band information, the physical cell identifier information, and RSRP values. Next, a sequential block-matching weight K nearest neighbor algorithm with a cosine similarity criterion is specially designed for performing the pattern-matching localization with the MBMC-R fingerprint. The proposed method also includes the derivation of the Cramer-Rao lower bound, which reveals the impact of various factors on the lower bound of position error. Simulation and on-field experiments prove the performance superiority over other fingerprint localization algorithms reported in the literature.

In the rapidly evolving domain of satellite communications, integrating advanced machine learning techniques, particularly split learning, is crucial for enhancing data processing and model training efficiency across satellites, space stations, and ground stations. Traditional ML approaches often face significant challenges within satellite networks due to constraints such as limited bandwidth and computational resources. To address this gap, we propose a novel framework for more efficient SL in satellite communications. Our approach, Dynamic Topology-Informed Pruning, namely DTIP, combines differential privacy with graph and model pruning to optimize graph neural networks for distributed learning. DTIP strategically applies differential privacy to raw graph data and prunes GNNs, thereby optimizing both model size and communication load across network tiers. Extensive experiments across diverse datasets demonstrate DTIP’s efficacy in enhancing privacy, accuracy, and computational efficiency. Specifically, on Amazon2M dataset, DTIP maintains an accuracy of 0.82 while achieving a 50% reduction in floating-point operations per second. Similarly, on ArXiv dataset, DTIP achieves an accuracy of 0.85 under comparable conditions. Our framework not only significantly improves the operational efficiency of satellite communications but also establishes a new benchmark in privacy-aware distributed learning, potentially revolutionizing data handling in space-based networks.

Combining different heterogeneous components into a full HPC system results in combinatorial effects in their complexity.It is a huge challenge to design systems such that they can be used efficiently by the expected workloads, particularly, when the workload is very heterogeneous.Modular systems can help, deciding according to the user portfolio how much weight a particular module should get, and what connectivity is required within and between modules.To deal with these challenges, integrated projects that cover all levels of the HPC ecosystem are needed.Also, interoperability and exchangeability of components, both hardware and software, should be easier to give system designers and users, alike, more flexibility.This special issue calls for recent research which focused on the heterogeneous HPC for IDA, such as memory management, workload management for heterogeneous systems and so as the heterogeneity in storage technologies.

As the growing trends in technology continue to drive massive transformation throughout the automotive sector, connected and autonomous transportation has become the future vision. Many researchers and practitioners wonder how connected, and autonomous vehicles will affect future transportation. This Special Issue explores some issues in the transition towards autonomous vehicles and their future trends and developments with artificial intelligence (AI) and robotics.

Wireless powered mobile edge computing (WP-MEC), which combines mobile edge computing (MEC) and wireless power transfer (WPT), is a promising paradigm for coping with the computing power and energy constraints of wireless devices. However, how to realize the online optimal offloading decision and resource allocation in the WP-MEC system is very challenging. This paper studies the system computation completion time (SCCT) minimization problems for WP-MEC networks using non-orthogonal multiple access (NOMA) communication under binary and partial offloading modes. Due to the complexity of the optimization problems and the time-varying nature of the channel state information, we decouple the original problems into a top-problem of optimizing WPT duration and a sub-problem of optimizing resource allocation, and then propose a convolutional deep reinforcement learning online (CDRO) algorithm. For the top-problem, a deep reinforcement learning framework is used to obtain the near-optimal WPT duration, and an incremental exploration policy is designed to balance the exploration accuracy and exploration range to improve the convergence performance of the CDRO algorithm. For the sub-problems, we propose their corresponding low-complexity algorithms based on in-depth analysis and derivation of the optimal offloading decision’s properties. Finally, numerical results show that the proposed CDRO algorithm achieves near-optimal SCCT with low computational complexity, enabling online decision-making in time-varying channel environments.

For the future 6G-enabled massive Internet of Things (mIoT), how to effectively manage spectrum resources to support huge data traffic under the large-scale overlapping caused by the dense deployment of massive devices is the imperative challenge. In this paper, a novel hypergraph interference model is designed, and two reinforcement learning (RL)-based resource management algorithms in the 6G-enabled mIoT are proposed to enhance the network throughput and avoid overlapping interference. Then, based on the hypergraph interference model, the resource management problem of execution network throughput maximization is theoretically formulated under large-scale overlapping interference scenarios. To handle this problem, we convert it into a Markov decision process (MDP) model and then deal with this MDP model through the advantage actor-critic (A2C)-based resource management algorithm and asynchronous advantage actor-critic (A3C)-based resource management algorithm, which aim to maximize network throughput of the spectrum resource allocation among massive devices. The simulation results verify that the proposed algorithms can not only avoid large-scale overlapping interference but also improve the network throughput.

This paper investigates a digital twin (DT) and reconfigurable intelligent surface (RIS)-aided mobile edge computing (MEC) system under given constraints on ultra-reliable low latency communication (URLLC). In particular, we focus on the problem of total end-to-end (E2E) latency minimization for the considered system under the joint optimization of beamforming design at the RIS, power, bandwidth allocation, processing rates, and task offloading parameters using DT architecture. To tackle the formulated non-convex optimization problem, we first model it as a Markov decision process (MDP). Later, we adopt deep deterministic policy gradient (DDPG) based deep reinforcement learning (DRL) algorithm to solve it effectively. We have compared the DDPG results with proximal policy optimization (PPO), modified PPO (M-PPO), and conventional alternating optimization (AO) algorithms. Simulation results depict that the proposed DT-enabled resource allocation scheme for the RIS-empowered MEC network using DDPG algorithm achieves up to 60% lower transmission delay and 20% lower energy consumption compared to the scheme without an RIS. This confirms the practical advantages of leveraging RIS technology in MEC systems. Results demonstrate that DDPG outperforms M-PPO and PPO in terms of higher reward value and better learning efficiency, while M-PPO and PPO exhibit lower execution time than DDPG and AO due to their advanced policy optimization techniques. Thus, the results validate the effectiveness of the DRL solutions over AO for dynamic resource allocation w.r.t. reduced execution time.

In the ongoing 5G and upcoming 6G eras, the intelligent Internet of Things (IoT) network will take increasingly important responsibility for industrial production, daily life and so on. The IoT devices with limited battery size and computing ability cannot meet many applications brought out by the data-driven artificial intelligence technique. The combination of wireless power transfer (WPT) and edge computing is regarded as an effective solution to this dilemma. IoT devices can collect radio frequency energy provided by hybrid access points (HAPs) to process data locally or offload data to the edge servers of HAPs. However, how to efficiently make offloading decisions and allocate resource is challenging, especially for the networks with multiple HAPs. In this paper, we consider the sum computation rate maximization problem for a WPT empowered IoT network with multiple HAPs and IoT devices. The problem is formulated as a mixed-integer nonlinear programming problem. To solve this problem efficiently, we decompose it into a top-problem of optimizing offloading decisions and a sub-problem of optimizing time allocation under the given offloading decisions. We propose a deep reinforcement learning (DRL) based algorithm to output the near-optimal offloading decision and design an efficient algorithm based on Lagrangian duality method to obtain the consequent optimal time allocation. Simulations verified that the proposed DRL-based algorithm can achieve more than 95 percent of the maximal computation rate with low complexity. Compared with the common actor-critic algorithm, the proposed algorithm has the substantial advantage in convergence speed, achieved computation rate and running time.

This special issue comprises 12 articles, showcasing the latest advances in computational social systems research.

Mobile edge computing (MEC) offers promising solutions for various delay-sensitive vehicular applications by providing high-speed computing services for a large number of user vehicles simultaneously. In this paper, we investigate non-orthogonal multiple access (NOMA) assisted secure offloading for vehicular edge computing (VEC) networks in the presence of multiple malicious eavesdropper vehicles. To secure the wireless offloading from the user vehicles to the MEC server at the base station, the physical layer security (PLS) technology is leveraged, where a group of jammer vehicles is scheduled to form a NOMA cluster with each user vehicle for providing jamming signals to the eavesdropper vehicles while not interfering with the legitimate offloading of the user vehicle. We formulate a joint optimization of the transmit power, the computation resource allocation and the selection of jammer vehicles in each NOMA cluster, with the objective of minimizing the system energy consumption while subjecting to the computation delay constraint. Due to the dynamic characteristics of the wireless fading channel and the high mobility of the vehicles, the joint optimization is formulated as a Markov decision process (MDP). Therefore, we propose an asynchronous advantage actor-critic (A3C) learning algorithm-based energy-efficiency secure offloading (EESO) scheme to solve the MDP problem. Simulation results demonstrate that the agent adopting the A3C-based EESO scheme can rapidly adapt to the highly dynamic VEC networks and improve the system energy efficiency on the premise of ensuring offloading information security and low computation delay.

Millimeter wave (mmWave) with abundant spectrum resources can realize high-rate communications in vehicular networks. However, the mobility of vehicles and the blocking effect of mmWave propagation bring new challenges to communication security. Cooperative communication is envisioned as a promising physical layer security (PLS) approach to enhance the secrecy performance, but it will induce extra energy consumption of vehicles. This paper proposes a deep recurrent reinforcement learning (DRRL)-based energy-efficient cooperative secure transmission scheme in mmWave vehicular networks, where eavesdropping vehicles attempt to intercept the multi-user downlink communications. We jointly design the mmWave beam allocation, the cooperative nodes selection, and the transmit power of vehicles. Specifically, the mmWave base station selects idle vehicles as relays to overcome the severe blocking attenuation of legitimate transmissions and controls the transmit power to reduce energy consumption. Moreover, to ensure secure transmission, a cooperative vehicle is selected to transmit jamming signals to the eavesdropping vehicles while the legitimate users are not disturbed. We conduct comprehensive interference analysis for both direct transmission and relay-aided transmission, and derive the theoretical expressions for the secrecy capacity. We then design the Dueling Double Deep Recurrent Q-Network (D3RQN) learning algorithm to maximize the total secrecy capacity subject to the energy consumption constraint. We set the energy consumption punishment mechanism to avoid relay vehicles consuming too much power for forwarding signals. We demonstrate that the proposed scheme can rapidly adapt to the highly dynamic vehicular networks and effectively improve secrecy performance while reducing the energy consumption of vehicles.

We are delighted to introduce the fifth part of the Special Issue on intelligent autonomous transportation systems with 6G, which aims to provide the scientific community with a comprehensive overview of innovative technologies, advanced architectures, and potential challenges for the 6G-supported intelligent autonomous transport systems. Forty-two papers were selected for publication in this issue. All the papers were rigorously evaluated according to the standard reviewing process of IEEE Transactions on Intelligent Transportation Systems. The evaluation process considered originality, technical quality, presentational quality, and overall contribution. We will introduce these articles and highlight their main contributions in the following.

The upcoming sixth generation (6G) driven Internetof- Things (IoT) will face the great challenges of extremely low power demand, high transmission reliability and massive connectivities. To meet these requirements, we propose a novel hybrid ambient backscatter communication (ABCom) or relay communication (RCom) assisted non-orthogonal multiple access (NOMA) network, which simultaneously enables traditional relay networks and ABCom-assisted IoT networks. Specifically, we investigate the reliability and the finite signal-to-noise ratio (SNR) diversity-multiplexing trade-off (f-DMT) of the proposed system to characterize the outage performance of the proposed system in the non-asymptotic SNR region. We derive the outage probability (OP) and the finite SNR diversity gain when two sources aim to communicate through either ABCom or RCom. On the basis that the results of Monte Carlo simulation and analysis are in perfect agreement, we discover that in the high SNR regime, the OP for ABCom tends to be a constant, leading to a zero diversity gain and an error floor, while the OP for RCom is monotone decreasing with respect to the SNR. Also, compared with the imperfect successive interference cancellation (ipSIC) mode, the reliability of the system under the ideal condition is significantly improved; Moreover, in the lower multiplexing gain regime, for both ABCom and RCom, the higher finite SNR diversity gain results in better system reliability, which provides good opportunities for ABCom to adapt f-DMT and improve relevant performance metrics by adapting the reflection parameter.

In the last decade, MIMO spatial multiplexing and distributed beamforming play a significant role in improving data throughput through cooperative transmission. It has been widely used in wireless communication, especially in 6G. However, the distributed uplink beamforming is still an open problem in highly dynamic environments. However, the proposed 6G technology represents the further integration of deep learning and wireless communication. In this article, we propose Argute Distributed Uplink Beamforming (ArguteDUB), which uses a feedback algorithm with an offline-trained deep learning model to implement highly dynamic distributed uplink beamforming for the Internet of Vehicles (IoV) in 6G. Specifically, each vehicle enables the base station (BS)/access point (AP) to separate different channel state information (CSI) by inserting orthogonal sequences into the sending data. The BS adopts deep learning to filter the noise and predict the beamforming weight to achieve phase synchronization. Unlike traditional distributed uplink beamforming, ArguteDUB can be adapted to the highly dynamic time-varying channels. The simple network structure ensures the fast response of ArguteDUB. In addition, we make ArguteDUB Orthogonal Frequency Division Multiplexing (OFDM) compatible so that it can be easily deployed in 6G networks. Our evaluation shows that ArguteDUB has an signal-to-noise ratio (SNR) gain of about 5 dB to 5.3 dB over the single vehicle transmission mode.

Federated learning offers a partial safeguard for participants’ data privacy. Nevertheless, the current absence of an efficient privacy-preserving federated learning technology tailored for the Internet of Things (IoT) poses a challenge. Numerous privacy-preserving federated learning frameworks have been proposed, primarily relying on homomorphic cryptosystems, yet their suitability for IoT remains limited. Furthermore, the application of federated learning in IoT confronts two significant obstacles: mitigating the substantial communication costs and communication failure rates, and effectively discerning and utilizing high-quality data while discarding low-quality data for collaborative modeling purposes. In order to address these challenges, this paper introduces a privacy-preserving optimal aggregation federated learning framework that relies on the utilization of the multi-key EC-ElGamal cryptosystem (MEEC) and the federated sum optimization algorithm (FSOA), which are characterized by their lightweight nature and fair properties. The proposed MEEC approach aims to tackle the issue of multi-key collaborative computing within the context of federated learning, thereby resulting in reduced communication costs and enhanced communication efficiency. This is achieved through the leverage of the EC-ElGamal cryptosystem, which is known for its ability to generate short keys and ciphertexts. Furthermore, this paper presents a dynamic federated learning framework that incorporates user dynamic quit and join algorithms. The primary objective of this framework is to mitigate the adverse effects of communication failures and enhance power computation on IoT devices. Additionally, an FSOA is devised to ensure the acquisition of optimal training data, thereby preventing the inclusion of low-quality data in the training process. Subsequently, the proposed scheme undergoes rigorous security analysis and performance evaluation. The obtained results unequivocally demonstrate that our scheme outperforms existing solutions in terms of security, practicality, and efficiency with lower communication and computational costs.

Aiming to solve the problem of interruption of normal communication service caused by the damage of ground communication facilities after disaster, an Air-Ground Integrated Mobile Edge Network (AWMEN) offloading model was established under the constraints of communication security, energy consumption and coverage. The traditional method needs to be re-iterated every time the preset environmental state changes, which will waste a lot of communication resources and computing resources, greatly reduce the efficiency, and face the risk of data privacy disclosure. However, the deep reinforcement learning method under the federated learning framework will be more flexible and applicable to dynamic scenarios. A Markov decision process model is constructed based on the unmanned aerial vehicles (UAV) and the environment. The experience trajectory is designed by interacting with the external environment, and the optimal offloading strategy is obtained. The Twin Delayed Deep Deterministic Policy Gradient of behavior cloning (TD3-BC-R) is compared with baseline method (0-1 mode), Actor-Critic (AC-R), Deep Deterministic Policy Gradient (DDPG-R) and Twin Delayed Deep Deterministic Policy Gradient (TD3-R), the experiment shows that, The total time cost of TD3-BC-R is reduced by more than 1/3, and low latency transmission is also achieved.

In this paper, a realization technique is designed with a unique analytical model for transmitting multimedia data to appropriate end users. Transmission of multimedia data to all end users through a variety of visualization methods is the foundation of future computer systems. Yet, highly limited system resources prevent the updating of the methods used to manage multimedia data. Hence, a high-end visualization technique where uncertainties are eliminated is required for the visualization process with a multimedia system. As a result, the suggested system incorporates a clustering technique utilizing an analytical framework to ensure a high degree of transmission for all multimedia data. The technical contribution of the proposed method depends on a multimedia visualization process that takes place with high security features by including necessary parametric relationships such as occurrence of jitter, data density points, time period, multimedia storage, data smoothness and distance. For the established parametric relationship the validation methodology is integrated with a hierarchical clustering algorithm, thereby transmitting every clustered data with high security feature, thereby the examined outcomes under five scenarios proves that data security which is represented by simulation outcomes is improved to 88% as compared to the existing approach.

Flow monitoring is widely applied in software-defined networks (SDNs) for monitoring network performance. Especially, detecting heavy hitters can prevent the Distributed Denial of Service (DDoS) attack. However, many existing approaches fall into one of two undesirable extremes: (i) inefficient collection where only accuracy is concerned in the method; (ii) sacrifice of accuracy due to fast detection. One practical problem with this is that it does not have the flexibility to adjust the monitoring strategy to the monitoring needs, making it difficult to meet different applications. To alleviate this problem, we propose our design of a novel flow monitoring framework that keeps the balance between accuracy and efficiency. It provides customized monitoring services for applications, where network resources can be saved, and the error rate can also be confined. In this paper, we present cReFeR, a three-step “compression Report-Feedback-Report” framework to monitor SDNs. The IP and the value compressor are specially designed to reduce the volume of flow statistics collection. This framework thus can achieve accuracy-ensured and resource-saving flow monitoring in SDNs. Theoretical analysis and simulated evaluation have proved the effectiveness of our solution. cReFeR keeps the error rate under 3% and reduces the amount of monitoring data more than 40%, which guarantees high efficiency compared with existing methods.

The Maritime Internet of Things (MIoTs) provides intelligent information services for marine scientific research, emergency response and environmental monitoring by leveraging its wide coverage and ubiquitous connectivity. However, challenging maritime communication conditions and limited sea-based network resources hinder MIoT from meeting the evolving network quality of service requirements of growing maritime activities. This poses a significant challenge to ensuring real-time and reliable transmission of mixed traffic flows. To address issues such as link contention and transmission delays in software-defined MIoT systems, a deterministic scheduling and highly reliable routing mechanism based on cross-layer design is proposed. First, a deterministic scheduling mechanism for mixed traffic flows is introduced, which effectively reduces transmission delays and improves the schedulability of data flows. Second, a high-reliability, low-latency routing mechanism based on Double Deep Q Network (DDQN) is proposed, which is capable of dynamically screening neighbouring nodes based on real-time link and node states, thus facilitating fast and high-quality path selection. Extensive simulation results show that DSMTF improves flow schedulability by 28% compared to traditional algorithms, while HRLDQ increases the network packet delivery rate by 25.8% and reduces the average end-to-end delay by 23.6%.

In this paper, an effective simultaneous channel estimation and sensing algorithm is proposed for millimeter wave (mmWave) multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) systems. The proposed algorithm consists of a two-stage channel estimation scheme and a reliable sensing scheme, which enables high-quality channel estimation and precise sensing in three-dimensional (3D) space. Specifically, the proposed algorithm first puts forward an improved simultaneous orthogonal matching pursuit algorithm that utilizes structural relation between the sparse basis and indexes to implement coarse estimation of multiple parameters. Subsequently, taking the obtained coarse parameters as initial values, optimization of channel parameters is achieved using the idea of maximum likelihood and gradient descent algorithm. Finally, we develop a reliable sensing scheme to realize user localization and mapping of scattering environment in various scenarios. Cramér-Rao bounds (CRBs) of the parameters and positions are also derived and used as a benchmark in simulations. Simulation results demonstrate that compared with the existing algorithms, the proposed algorithm has better channel estimation and sensing performance and is closer to CRBs. Moreover, even in challenging environment with unknown user orientation and clock bias, the proposed algorithm can achieve precise user localization and mapping of scattering environment.

Ambient backscatter communication (AmBC) has emerged as a paradigm distinguished by its energy-efficient attributes and low-power dynamics, ideally suited to address the vast expanse of the Internet of Things (IoT). Unmanned aerial vehicles (UAVs) deployed with flexibility can effectively establish wireless connections for isolated IoT devices through AmBC. This paper delves into the exploration of secure transmission within a UAV-assisted AmBC network, particularly addressing the challenges posed by the presence of a passive eavesdropper. Specifically, a UAV is utilized as an aerial base station to offer services to an isolated ground user, an AmBC tag transmits its information to its associated receivers by leveraging the UAV’s radio frequency (RF) signals. Furthermore, a multi-antenna cooperative jammer is integrated within the system to intentionally interfere with the eavesdropper without affecting legitimate receivers. To characterize the secrecy performance, the expressions of secrecy outage probability of the air-ground link and backscatter link are both deduced leveraging a two-layer Gaussian-Chebyshev quadrature. Moreover, the asymptotic behaviors under the high signal-to-noise ratio (SNR) regime are also analyzed. Monte Carlo simulations are performed to validate the correctness and effectiveness of the analytical results.

The substantial power consumption attributed to the active components within fully-connected reconfigurable intelligent surface (RIS) architecture significantly hinders the efficiency and sustainability of DT-enabled MEC networks. To tackle this challenge, we present an innovative sub-connected architecture for active RIS within the digital twin (DT) integrated mobile edge computing (MEC) framework of an Internet-of-Things (IoT) networks, capitalizing on edge intelligence to enhance ultra-reliable and low-latency communication (URLLC) services. The primary aim of our research is to improve uplink data transmission from IoT URLLC user nodes (UNs) to a base station (BS) with the aid of an active RIS, even under an imperfect channel state information (CSI). We have formulated the total end-to-end (e2e) latency minimization problem, which is solved by using an efficient alternating optimization (AO) algorithm. The algorithm breaks down the proposed non-convex problem into five subproblems, namely, beamforming design, caching and offloading policy optimization, joint communication and computation optimization, and joint active RIS phase shift and amplification factor vector optimization. We conducted a thorough analysis of the convergence properties of the proposed AO algorithm, benchmarking its performance against the established Heuristic algorithm. Our simulation results consistently demonstrate the superiority of our proposed DT-assisted optimal phase sub-connected active RIS scheme over various benchmark schemes, taking into account various factors such as the number of RIS elements, power budget constraints, imperfect CSI, edge computing server (ECS) cache capacity, number of IoT UNs, and the number of power amplifiers.

This paper explores the state-of-the-art terahertz (THz) integrated sensing and communication system (ISAC) that uses active reconfigurable intelligent surfaces (ASRIS) that can transmit and reflect signals at the same time. The system incorporates a novel dynamic delay alignment (DDA) modulation technique, allowing signals from different paths to reach the receiver simultaneously, eliminating the need for complex channel equalization and mitigating inter-symbol interference, while considering the dynamic movement of the vehicular units (VUs), and accounting for time-selective fading and uniform Doppler power spectra (DPS) model. Our system is equipped with a dual-function radar and communication multiple-antenna base station (BS), which simultaneously serves both communication and target sensing functions through an ASRIS. The objective is to maximize the sum rate by jointly optimizing BS transmit beamforming, ASRIS reflection and transmission beamforming matrices, VU mobility correlation parameters, and radar receive filter. Traditional optimization methods prove challenging given the intricate nature of this non-convex optimization problem, owing to dynamic changes in communication links and the interplay of multiple variables. To overcome this, we propose a machine learning (ML)-based multi-agent deep deterministic policy gradient (MADDPG) algorithm. MADDPG enables collaborative learning, adapts to the dynamic communication environment, and excels in optimizing interdependent parameters in the proposed THz system. Deep deterministic policy gradient (DDPG), proximal policy optimization (PPO), and modified-PPO (MPPO) algorithms serve as benchmarks, showcasing the distinctive advantages of the ML-based MADDPG solution for the proposed system’s complexities. Our simulations validate the substantial benefits of ASRIS over conventional RIS benchmark scenarios.

In this paper, a novel reconfigurable intelligent surface (RIS)-assisted HAP-UAV secure multi-user mixed radio frequency (RF)/free space optical (FSO) system is proposed. Specifically, the Gamma-Gamma distribution is utilized to characterize the atmospheric turbulence effect for the FSO link from UAV to HAP, while the Rayleigh and Nakagami-$m$distribution fading are applied to simulate the legitimate and wiretap RF links, respectively. We present the closed-form expressions for the probability density functions, the cumulative distribution functions, and the secrecy outage probability (SOP) of the end-to-end signal-to-noise ratio (SNR) in terms of Meijer’s G-function. To gain more insight into secrecy performance, we further obtain the closed-form expressions for the asymptotic SOP, the asymptotic probability of positive secrecy capacity (PPSC), the diversity gain, and the coding gain at high SNR regions. We can observe that the secrecy performance depends on the weaker channel between the RF and FSO, and is closely related to the number of RIS elements, the number of terrestrial users, the atmospheric turbulence factor, pointing error parameters, and the fading parameter of Nakagami-$m$distributed wiretap link. Finally, numerical results validate the derived results and demonstrate that the proposed design achieves superior secrecy performance over the benchmarks.

Secret Image Sharing (SIS) is the technology that shares any given secret image by generating and distributing$n$shadow images in the way that any subset of$k$shadow images can restore the secret image. However, in the existing SIS schemes, the shadow images will be easily tampered and corrupted during the communication, which will pose serious security issues. Recently, blockchain has emerged as a promising paradigm in the field of data communication and information security. To securely communicate and effectively protect the secret image data in wireless networks, we propose a Blockchain-based Secure and Efficient Secret Image Sharing (BC-SESIS) scheme with outsourcing computation in wireless networks. In the proposed BC-SESIS scheme, the shadow images are encrypted and stored in the blockchain to prevent them from being tampered and corrupted. The identity authentication-enabled smart contract is deployed to achieve the$(k,n)$threshold for secret image restoring. Furthermore, to reduce the computational burden of smart contract and users, an efficient outsourcing computation method is designed to outsource the restoring task, which is securely implemented by agent miners in the encryption domain. Theoretical analysis and extensive experiments demonstrate that the BC-SESIS scheme can achieve desirable communication security and high computational efficiency in the wireless networks.

This study proposes an interesting approach for vehicle-to-vehicle (V2V) communication, which integrates blockchain technology, federated learning (FL), and allocation optimization of latency and resources. The research evaluates the proposed system using various performance metrics such as packet delivery ratio (PDR), model accuracy, and latency and demonstrates its superiority over existing techniques. Further-more, the system provides enhanced security through consensus optimization and k-anonymity for data privacy. Overall, the proposed system is a promising solution for efficient and secure V2V communication in the era of connected and autonomous vehicles. Moreover, the proposed approach achieves higher reli-ability, lower latency, and better resource utilization compared to traditional 5G.

The terahertz (THz) band (0.1-10 THz) is widely considered to be a candidate band for the sixth-generation mobile communication technology (6G). However, due to its short wavelength (less than 1 mm), scattering becomes a particularly significant propagation mechanism. In previous studies, we proposed a scattering model to characterize the scattering in THz bands, which can only reconstruct the scattering in the incidence plane. In this paper, a three-dimensional (3D) stochastic model is proposed to characterize the THz scattering on rough surfaces. Then, we reconstruct the scattering on rough surfaces with different shapes and under different incidence angles utilizing the proposed model. Good agreements can be achieved between the proposed model and full-wave simulation results. This stochastic 3D scattering model can be integrated into the standard channel modeling framework to realize more realistic THz channel data for the evaluation of 6G.

The trend of integrated satellite-HAP-ground networks (IS-HAP-GNs) as an critical directions for the future development of next generation network technology is widely recognized by academia and industry. Besides, utilizing reconfigurable intelligent surfaces (RIS) as a green paradigm, unmanned aerial vehicles (UAVs) can be equipped to reflect uplink signals from vehicle transmitters (VTs) to high altitude platforms (HAPs). Acting as relays, HAPs then forward these signals to satellites via hybrid free-space optical (FSO) links to enable rapid link deployment. In this paper, we firstly investigate a novel uplink signal transmission mode to maximize the system ergodic sum rate. Then, to tackle the high-dimensional non-convex optimization problems, we propose an asymmetric long short-term memory (LSTM)-deep deterministic policy gradient (DDPG) (AL-DDPG) algorithm builds on the deep reinforcement learning (DRL) framework. The numerical results demonstrate the superiority of the AL-DDPG algorithm over traditional optimization algorithms and reveal the effect of different system parameter settings on the performance.

This work proposes a novel design for an active reconfigurable intelligent surface (RIS)-assisted digital twin (DT) based mobile edge computing (MEC) model that leverages edge intelligence to enhance ultra-reliable and low-latency communications (URLLC) services in Internet-of- Things (loT) networks. The system model considers uplink data transmission from the single antenna IoT-URLLC nodes (UNs) to a multi-antenna base station (BS) with the aid of an active RIS under imperfect channel state information (CSI). We formulate a total end-to-end (E2E) latency minimization problem for the proposed system model. An efficient alternating optimization (AO) algorithm is proposed to tackle the non-convexity of the problem by reformulating it into five subproblems: beamforming design, caching and offloading policies optimization, joint communication and computation optimization, and active RIS phase shift optimization. Simulation results demonstrate that the proposed DT-assisted optimal-phase active RIS scheme consistently outperforms benchmark schemes, such as optimal-phase passive RIS, random-phase active RIS, and no- RIS systems, considering factors such as imperfect CSI, power budget, number of RIS elements, the caching capacity of edge computing server (ECS) and the number of loT UNs.

Federated learning (FL) has been deployed on low earth orbit (LEO) satellites Internet of Things (IoT), where learning models can be trained collaboratively, thus preserving IoT data privacy without centralizing. However, the efficiency of FL is significantly hindered by the straggler that cause maximum latency. The data allocation strategy that parallelizes learning could potentially increase efficiency of FL for LEO satellite IoT networks since multiple LEO satellites can access a terrestrial IoT gateway concurrently. However, modeling and optimizing data allocation poses a significant challenge. To address this challenge, this paper proposes a collaborative learning method with latency-aware data allocation for LEO satellite IoT networks. Particularly, we formulate the data allocation strategy as an optimization problem of minimizing the maximum latency which is the sum of training time of the learning model and signal propagation delay, while considering the constraint of limited energy at each satellite. Next, we use a line search sequential quadratic programming (SQP) method to decompose the problem into a sequence of quadratic programming (QP) subproblems, which are further solved by the active-set algorithm. Simulation results show that nearly the half of the maximum latency per round can be decreased and the procedure of convergence is accelerated about 25 % in a large LEO satellite constellation with 1000 satellites and 10 IoT gateways.

Wireless train communication network (WLTCN) is a promising technology for intelligent rail vehicles. It is responsible for bearer of train control services (TCS) and passenger information services (PIS), with the latter mainly referring to multimedia services. The two services have notably different quality of service (QoS) requirements from traditional telecommunication services. To realize multiple services co-existence bearer with different quality of service (QoS) requirements in a single network, we propose a radio access network (RAN) slicing framework to fully utilize the bandwidth resource within a WLTCN in this paper. Based on the characteristics of TCS and PIS, the communications models for the two services are proposed. Next, the slicing strategy problem is formulated as the system bandwidth minimization problem, and then it is transformed into an equivalent problem as the non-convexity. We proposed a dual-decomposition based bandwidth allocation (DBA) algorithm to derive the closed-form expressions for the optimal resource allocation. Simulation results show that the proposed slicing strategy enables WLTCN to meet the QoS requirements for TCS and PIS with minimal bandwidth consumption.

Digital twins have been extensively explored in vehicular social networks, while wireless communication quality is limited to support digital twins and their applications due to the high mobility of the vehicular environment. To address this issue, we propose a semantic communication empowered two-level digital twin architecture, called Semantic Twin, which supports reliable communication of efficient cloud-edge collaborative vehicular digital twin, specifically comprising low-level semantic twin (L-SemTwin) and high-level semantic twin (H-SemTwin). First, we design a social behavior semantic extraction scheme based on semantic encoder on the vehicle side to capture essential content features. Second, a semantic transmission scheme in vehicle-to-everything communication is performed to reduce the overall transmission burden and error rate, and build L-SemTwin. Moreover, we propose a deep reinforcement learning-based semantic caching strategy with the assistance of city-wise semantic information in cloud-side H-SemTwin. The experiment results demonstrate the promotion under the proposed architecture compared to conventional methods in terms of the quality of communication and user experience in vehicular social networks.

In this work, we consider a full-duplex (FD) communication system that uses a simultaneous transmission and reflection (STAR) enabled reconfigurable intelligent surfaces (RIS) to assist the communication and sensing between a base station (BS) to a single set of UL and DL user, and target over the same-time frequency dimension. In order to explore the performance of the proposed framework, we offer an analytical framework and accordingly, we propose an optimization problem to jointly optimize the phase-shift matrices at the STAR RIS (S-RIS) that maximizes the possible sum-rate. Due to the non-convexity of the optimization problem, we then propose a low-complexity meta-reinforcement learning (MRL) algorithm that reduces the overall training overhead. We also demonstrate the effectiveness of the proposed algorithm in providing near-optimal design in the case of imperfect channel state information (ICSI). Additionally, in order to verify how well the proposed framework work and to show the superiority of the proposed algorithm, we provide a fair comparison with two baseline schemes a) twin delayed deep deterministic policy gradient (TD3) and b) deep deterministic policy gradient (DDPG). Simulation results verify that the proposed approach results in superior performance.

Reconfigurable intelligent surfaces (RIS) assisted unmanned aerial vehicle (UAV) communications are an effective way to enhance communication and effectively improve rescue efficiency in disaster scenarios. This provides a good communication guarantee for the collection and transmission of big data. Beam training is the key method to solve the problem of beam alignment between the receiving and transmitting ends. However, existing schemes rely on feedback from uniform or finite precision codebooks, which are not suitable for complex electromagnetic environments, resulting in large beam training overhead. We consider a non-uniform codebook-based beam training scheme under Karush-Kuhn-Tucker (KKT) conditions to optimize the energy consumption of rescued user. Specifically, we consider the RIS assisted UAV communication system with emergency rescue. Then, considering the constraints of RIS phase-shift, the system communication rate and energy efficiency, we propose an optimization problem to minimize the system transmission power. In addition, we propose an optimization algorithm of successive approximation codebook iteration with Karush-Kuhn-Tucker (KKT) to solve this problem with low precision non-uniform codebook. Finally, the simulation results show that the proposed optimization algorithm can effectively reduce the transmission power of the system.

Data dissemination is critical for 6G autonomous driving (AD) service because of the extensive demand for real-time traffic information. However, the heavier data transmission burden and more stringent requirements of AD service bring challenges for current data dissemination methods. In this paper, we first propose a novel digital twin (DT)-based semantic dissemination architecture to better support 6G AD service. Under this architecture, an energy-efficient semantic communication mechanism is developed to reduce the data dissemination burden while keeping low semantic model update costs. Meanwhile, the DT network is leveraged to disseminate semantic data in parallel with the physical vehicular networks, which alleviates the physical transmission contention and improves the dissemination efficiency. Second, we design a deep-reinforcement-learning (DRL)-driven semantic data dissemination scheme for the proposed architecture, named Proximal-policy-optimization for Digital-twin-aided Data Dissemination (PD3), which seeks the optimal DT transfer and semantic transmission scheduling strategy. Finally, experimental results show that our approach surpasses the state-of-the-art methods by 18.36% lower dissemination delay and 4.51% higher dissemination ratio on average.

Terahertz communication with abundant spectrum resources is envisioned as the key technology of 6G. Despite its narrow beam, terahertz transmission is still vulnerable to eaves-dropping attacks in indoor scenarios. In this paper, we propose a blockage feature-based artificial noise scheme to safeguard the indoor network in the presence of multiple access points (APs), users, and eavesdroppers. Those APs with blocked links to the typical user are selected to emit artificial noise to deteriorate the reception of eavesdroppers. Thus, communication security is ensured without escalating the instability of legitimate connections caused by the small coverage nature of terahertz beams. By comprehensively considering the propagation characteristics of terahertz, such as the three dimensions narrow beam and the human blocking effect, we derive the theoretical expressions of the connection outage probability, the secrecy outage probability, and the average number of perfect links per unit area. Numerical results demonstrate that the proposed scheme outperforms the traditional schemes in terms of connection stability and secrecy performance.

As one of the key technologies in the Internet of Things (IoT), Zigbee is widely used in industrial, agricultural, or medical scenarios due to its short distance, low delay, and high reliability of communications. However, the intensive deployment and concurrent transmissions of devices in ZigBee networks lead to severe collision and interference, reducing the throughput of the entire wireless network. Effective collision resolutions have been explored in the literature, but most of them use chip sequence as unit to decompose collision through waveform comparison and subtraction, which will cause issues such as error propagation, bit errors due to scarce samples, and high computational complexity. To make up for the above deficiencies, we propose a new physical layer design called OrthZig to achieve high-precision collision resolution in ZigBee networks without central controller. The key idea of OrthZig is to utilize the Direct Sequence Spread Spectrum adopted by ZigBee physical layer to achieve concurrent transmissions of multiple users based on the orthogonality of signal waveforms and distributed coordination. Additionally, we reduce the requirement for synchronization accuracy by partitioning the sync pair in the spread sequence, ensuring orthogonality for correct decoding. Extensive simulations are conducted from multiple dimensions to simulate the overall performance. Evaluation results demonstrate that OrthZig has lower average bit error rate and higher network throughput than the state-of-the-art.

Physical-layer Network Coding (PNC) has great potential to improve the throughput and latency in wireless networks. However, there are two main challenges in PNC systems that do not exist in the conventional Point-to-Point$(\mathrm{P}2\mathrm{P})$communication systems: 1) time and frequency asynchrony of the paired PNC users; and 2) ambiguity of the PNC mapping at the relay node. To address these challenges, in this paper, we apply precoding for power control and phase synchronization of the paired PNC users, while we use modulo$-\sqrt{M}$addition for the PNC mapping ambiguity removal in higher-order M-ary Quadrature Amplitude Modulations (M-QAM). We evaluate the performance of the system in the framework of the Orthogonal Frequency Division Multiplexing (OFDM)-PNC systems with cyclic prefix extension under Rayleigh faded Tapped Delay Line (TDL)-C and Rician faded TDL-D channel models, proposed by the Third Generation Partnership Project$(3\text{GPP})$, as well as the Additive White Gaussian Noise (AWGN) channel model. The results reveal that our proposed technique can achieve a significant Signal-to-Noise Ratio (SNR) improvement of 12$\text{dB}$over its asynchronous OFDM-PNC counterpart (without precoding) for Binary Phase Shift Modulation (BPSK) under a TDL-C faded channel model. Moreover, without channel coding, our proposed PNC technique requires an SNR of around$13\text{dB}$to deliver a two-way 16-QAM communication at a Bit Error Rate of 10−3under a Rayleigh faded TDL-C channel.

Nowadays, vehicle edge computation supports a novel computing resource provisioning roadmap for green smart cities, which benefits the distributed intelligent applications, such as unmanned vehicle. Despite the fact that vehicle edge computation can better offload computing resource, there are still certain problems in implementing vehicle edge computation in green smart cities. To begin, the geographical imbalance in computing resource results in a time latency when computation offloading. Second, the security of computing resource is an issue that cannot be disregarded. This is because the loss of some sensitive data in computing resource may result in repercussions that cannot be undone. To address aforementioned challenges, we present a collaborative-filtering privacy-preserving vehicular edge computation offloading approach (CVECO). By utilizing collaborative filtering, the CVECO algorithm is able to reduce the latency of the computation offloading. Meanwhile, the CVECO algorithm is able to efficiently provide high security and protect computing resource privacy by applying multiple privacy mechanisms. Finally, the results of the simulation indicate that the CVECO algorithm is capable of lowering the latency associated with the computation offloading while simultaneously preserving a high degree of safety regarding the computing resource. To the best of our knowledge, our proposed approach is capable of performing vehicle edge computation offloading well, which permits a rational use of electricity in green smart cities, further lowering greenhouse gas emissions.

Nowadays, empowered by mobile networks, electronic health (E-health) can bring more advanced medical services. Electronic health records (EHRs) with rich medical knowledge have gained increasing popularity for supporting E-health. Blockchain-based secure storage and access are novel trends for EHRs, but there are still following unresolved problems in this area. On one hand, specific medical tasks (e.g., COVID-19 diagnosis) require corresponding EHRs, but it lacks the medicine knowledge graph to help doctors or researchers find the EHRs on-demand in the massive distributed and encrypted medical data. On the other hand, as the documents include knowledge, EHRs with digital copyrights shared by medical institutions or patients need to be protected during sharing. To address these challenges, this paper proposes a blockchain and Non-Fungible Token (NFT) empowered on-demand medicine knowledge sharing architecture for EHRs. First, we propose a medicine knowledge graph construction scheme based on smart contracts and medical task knowledge relationships. Second, to provide on-demand EHRs sharing, we design the EHRs matching and clustering algorithms, regarding the dynamic importance and similarity of graph nodes. Third, we establish the interplanetary file address driven NFT minting mechanism for EHRs to protect digital copyrights. Finally, we conduct experiments in Ethereum using real medical datasets, which demonstrates the feasibility and efficiency of the proposed architecture. To our best knowledge, this work is the first to realize the medicine knowledge graph with copyright protection for EHRs.

High-speed computation resources are provided by mobile edge computing (MEC) to boost various delay-sensitive vehicular applications. However, compared to computing tasks locally, the MEC approach consumes extra energy in the offloading process. In this paper, an asynchronous deep reinforcement learning-based energy-efficient secure offloading (EESO) is proposed to enhance the energy efficiency and security of the vehicular edge computing (VEC) network in the presence of multiple malicious eavesdropper vehicles. To secure the wireless offloading process of the information, a group of jammer vehicles is scheduled to form a NOMA cluster with each user vehicle for providing jamming signals to the eavesdropper vehicles while not interfering with the legitimate user vehicle. We minimize the system energy consumption with the computation delay constraint by jointly optimizing the transmit power, the computation resource allocation, and the selection of jammer vehicles in each NOMA cluster. Then we adopt an asynchronous advantage actor-critic (A3C) learning algorithm to solve the optimization problem. With proper training, the A3C-based EESO scheme can reduce the system energy consumption and improve offloading security.

With the advent of edge computing, more and more tasks are offloaded to edge servers, but the computing and storage capabilities of edge servers are limited. Although some works propose efficient schemes for task allocation and pricing, they may ignore users' preferences for continuous tasks. However, the combinatorial preference causes high computational complexity. In this paper, we propose a dominant-strategy incentive compatibility (DSIC) and computationally efficient mechanism for green continuous task allocation based on the combinatorial auction. Besides, the activity on edge (AOE) network is introduced to describe the continuity of tasks. The proposed mechanism gives an approximate solution to the winner determination problem (WDP) in polynomial time and a pricing strategy that can guarantee the truthfulness and individual rationality of auction participants. We demonstrate the approximate ratio of the proposed algorithm through theoretical analysis. Experimental results show that the proposed mechanism achieves truthfulness, individual rationality, and high computational efficiency while considering green continuous task allocation.

Federated learning (FL)-assisted model training plays an important role in distributed energy scheduling of smart park. However, the time synchronization error between edge and end sides and the adversarial routing competition cause poor accuracy and high delay of model training. In this paper, we address this challenge and propose a time synchronization-aware edge-end collaborative deep Q network-based routing management algorithm named TSA-RM. TSA-RM minimizes the weighted sum of model training loss function and delay via routing optimization. TSA-RM achieves time synchronization awareness and avoids adversarial competition by incorporating time synchronization related information in state space construction and relay selection related information in penalty function design. Simulation results verify the superior performance of TSA-RM in terms of global loss function, model training delay, and time synchronization error compared with two state-of-the-art algorithms.

Device scheduling plays a key role in federated learning model training for energy management in low-carbon smart park. It is intuitive to achieve high-accuracy and low-latency model training by scheduling devices with smaller local training loss function and better channel condition. However, the adverse impact of model poisoning attack on model training performance and device scheduling adjustment cannot be neglected. The error model parameters uploaded by malicious attackers-controlled devices significantly reduce model training accuracy and convergence speed. To address this challenge, we propose an Endogenous Security-Aware Deep Q Network (ESA-DQN) based device scheduling algorithm. ESA-DQN integrates model poisoning attach detection with DQN networks to actively adjust device scheduling in accordance with estimated attack probability, thereby achieving endogenous security awareness. Numerical results show that ESA-DQN has excellent performances in terms of model training accuracy and delay.

The development of Industrial Internet of Things (IIoT) and Industry 4.0 has completely changed the traditional manufacturing industry. Intelligent IIoT technology usually involves a large number of intensive computing tasks. Resource-constrained IIoT devices often cannot meet the real-time requirements of these tasks. As a promising paradigm, the mobile-edge computing (MEC) system migrates the computation intensive tasks from resource-constrained IIoT devices to nearby MEC servers, thereby obtaining lower delay and energy consumption. However, considering the varying channel conditions as well as the distinct delay requirements for various computing tasks, it is challenging to coordinate the computing task offloading among multiple users. In this article, we propose an autonomous partial offloading system for delay-sensitive computation tasks in multiuser IIoT MEC systems. Our goal is to provide offloading services with minimum delay for better Quality of Service (QoS). Enlighten by the recent advancement of reinforcement learning (RL), we propose two RL-based offloading strategies to automatically optimize the delay performance. Specifically, we first implement the$Q$-learning algorithm to provide a discrete partial offloading decision. Then, to further optimize the system performance with more flexible task offloading, the offloading decisions are given as continuous based on deep deterministic policy gradient (DDPG). The simulation results show that the$Q$-learning scheme reduces the delay by 23%, and the DDPG scheme reduces the delay by 30%.

Unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) systems have been implemented for over a decade, from transportation to military surveillance, and is proven worthy of integration in the next generation of wireless protocols. Though UAVs have immense potential, they have major drawbacks when it comes to real-world implementation, such as energy capacity, loss of signal quality, and spectrum limitations. To overcome these challenges, integration of UAVs with spectrum-efficient techniques, including cognitive radio (CR) and nonorthogonal multiple access (NOMA) has been proposed. In this article, we incorporate transmit-antenna selection (TAS) into an underlay cognitive radio NOMA network, which provides additional benefits through employing multiple-antenna-selection approach at the UAV with the goal of better serving the ground NOMA devices. The links associated with the multiantenna UAV are theoretically assumed to experience Nakagami-$m$fading distribution. We also emphasize the degraded performance caused by imperfect successive interference cancelation (SIC) when decoding signals at the ground NOMA devices. The closed-form expressions for the proposed model are derived to evaluate two main performance metrics, namely, the outage probability and the ergodic capacity. Monte Carlo simulations are performed to analyze the performance of the system in different scenarios. We observe that the power allocation factors for the devices in a group and the altitude of UAV have a noticeable impact on the performance of the system. Furthermore, the increase in the number of antennas at the UAV can complement these effects and further improve the system performance.

With the rapid development of edge intelligence in wireless communication networks, mobile-edge networks (MENs) have been broadly discussed in academia. Supported by considerable geographical data acquisition ability of mobile Internet of Things (IoT), the MENs can also provide spatial locations-based social service to users. Therefore, suggesting reasonable points-of-interest (POIs) to users is essential to improve user experience of MENs. As the simple user-location data is usually sparse and not informative, existing literature attempted to extend feature space from two perspectives: 1) contextual patterns and 2) semantic patterns. However, previous approaches mainly focused on internal features of users, yet ignoring latent external features among them. To address this challenge, in this article, a deep distributed-learning-based POI recommendation (Deep-PR) method is proposed for situations of MENs. In particular, hidden feature components from both local and global subspaces are deeply abstracted via representative learning schemes. Besides, propagation operations are embedded to iteratively reoptimize expressions of the feature space. The successive effect of the above two aspects contributes a lot to more fine-grained feature spaces, so that a recommendation accuracy can be ensured. Two types of experiments are also carried out on three real-world data sets to assess both efficiency and stability of the proposed Deep-PR. Compared with seven typical baselines with respect to four evaluation metrics, obtained results of the overall performance of the Deep-PR are excellent.

Due to the massive applications of Internet of Things (IoT) and the prevalence of wearable devices, e-healthcare systems are widely deployed in medical institutions. As a significant carrier of medical data, electronic medical record (EMR) is convenient to be stored and retrieved, which greatly simplifies the experience of medical treatment and cuts down the trivial work of paramedics. However, EMRs usually include much sensitive information, such as patients’ identification numbers or home addresses that may be easily captured by unauthorized doctors and cloud servers. Based on this concern, e-healthcare systems can make use of attribute-based encryption (ABE) to protect private information while achieving fine-grained access control of encrypted EMRs. Whereas, most ABE schemes do not support both policy hiding and keyword search. To address the above issues, we propose an inner product searchable encryption scheme with multikeyword search (MK-IPSE) based on blockchain to provide full privacy preservation and efficient ciphertext retrieval for EMRs. Inner product encryption (IPE) can not only specify access permissions such that only users with matched attributes can get the target files but also support access policy hiding. Besides, the proposed scheme combines searchable encryption (SE) and federated blockchain (FB) to implement efficient and stable multikeyword search. Compared with the existing schemes, MK-IPSE shows better performance on computation and storage. Additionally, security analysis demonstrates that our scheme can resist IND-CKA and collusion attacks.

Digital twins (DTs), defined as the virtual representation of a real-world entity or system, act as a mirror to provide a way to simulate, predict physical behaviors, and possibly control the real-world entity where applicable. Originating in the industry, advances in computing capacity and recent progress in artificial intelligence (AI)-based analytics make DTs attractive to a broader set of use cases including mobile networks.

6G communication networks are expected to become an integral part of the infrastructure needed for developing a smart society in the future. Addressing the challenges on the road towards realizing 6G network requirements in terms of quality of service, user experience, and security, is therefore of utmost importance. The digital twin (DT) technology can potentially improve the efficiency, reliability, and security of 6G networks. Digital twins for mobile networks (DTMNs) are seen as a key factor in harnessing the full benefits of 6G. Using digital twins can help address several problems, including network optimization, fault diagnosis, and fault management. Furthermore, DTMNs can characterize the physical entities in a 6G network and their relationships to each other, build their virtual models, and use simulation, learning, and reasoning capabilities to make predictions and support informed decision-making,

In this paper, we explore the use of reconfigurable intelligent surface (RIS) in unmanned aerial vehicle (UAV) based multiuser downlink communications, where a flying UAV serves multiple single antenna users through multiple RISs mounted on various buildings. More specifically, we consider the selection of RISs based on the outdated and imperfect channel state information (CSI) of the composite UAV-RIS-User channels at the UAV. After selection process, the UAV communicates to the user via the selected RISs and also with the direct link. Particularly, we derive an infinite series based expression for selection probability of RISs under both the outdated and imperfect CSI of composite channels based selection scheme. We also derive the statistical distribution of instantaneously received signal-to-noise ratio (SNR) under outdated and imperfect CSI conditions of both the direct and composite links at the user. Next, using the derived statistics, we analyze the network’s performance in terms of the average coverage probability (ACP) and average bit error rate (ABER) over the complete UAV flight time. Moreover, we discuss the behavior of ACP and ABER for very small and very large values of UAV transmit power, respectively. It is depicted through numerical results that selecting more RISs from a group of small-sized RISs may not be as advantageous as selecting fewer RISs from a group of large-sized RISs. Moreover, we also demonstrate the effect of several system parameters such as number of RIS reflecting elements, number of selected RISs, the severity of UAV-RIS and RIS-User links, and the severity of imperfect and outdated CSI on the network’s performance. The analytical results are corroborated with Monte-Carlo simulations.

With the advancement of wireless communication technologies, reconfigurable intelligent surfaces (RISs) have recently paved the way to augmenting the performance of wireless networks with the aid of multiple reflecting surfaces by efficiently attuning the signal reflection through a large number of low-cost passive elements. In this paper, we consider an RIS-aided full-duplex (FD) communication network consisting of a FD access point (AP) that communicates with an uplink and a downlink user simultaneously with the aid of an RIS as well as through the direct link between the AP and users. To evaluate the system performance under infinite blocklength (IBL) and finite blocklength (FBL) codes, we derive the analytical expressions for the outage probability and throughput in case of IBL, and for block-error rate (BLER) and goodput in the case of FBL, for both uplink and downlink transmission. Furthermore, the expressions for the maximum achievable rate under FBL and IBL transmission are derived. Next, we also extend the analysis of the single-user framework to a more practical scenario with multiple users utilizing non-orthogonal multiple access (NOMA) and derive analytical expressions for the outage probability and BLER at each downlink user and at the AP. The accuracy of the derived expressions is validated via simulation results, and insights are provided regarding the impact of the number of reflecting elements and imperfect channel state information (CSI) on the performance of the considered system. Finally, from the comparative analysis, it is shown that the RIS-aided system outperforms the system without RIS in both IBL and FBL scenarios, providing remarkable improvement in the outage probability and BLER.

Sentiment analysis is an important task in social computing and behavior analysis, and is a typical indicator of social health. It is a challenging mission to predict the sentiment of people in socially implemented Internet of Medical Things (IoMT) systems. The existing methods have several defects, and a typical defect is that most methods ignore the fact that there is much noise in IoMT systems and it is far not enough only to develop classification models for one type of data. In socially implemented IoMT systems, many methods treat the review text as plain text but ignore the potential knowledge structure. To solve those problems, in this article, we propose a novel solution, which is composed of adversarial learning and a hierarchical attention mechanism. We construct a hierarchical attention mechanism to learn the knowledge structure of a text. We propose to apply the attention mechanism both at the word level and sentence level, enabling us to learn the knowledge from each word and each sentence. We propose to use adversarial learning to learn new knowledge as non-random perturbations, which promotes the model’s robustness. We evaluate our method on several large-scale real-world datasets, covering a wide range of cases of sentiment analysis. Experimental results demonstrate that our method achieves superior performance compared to state-of-the-art methods.

The efficient and effective framework for next-generation (5G and beyond 5G) wireless networks should include mission-critical aspects such as ultralow latency ($\leq \!\!1$ms), ultrahigh reliability (99.999%), and enhanced data rate. Billions of ubiquitously connected devices are expected to serve various industrial applications in upcoming industry standards such as Industry 5.0. These industrial applications include mission-critical tasks such as smart grids, remote surgery, and intelligent transportation systems. This article considers an industrial Internet of Things (IIoT) environment in mission-critical ultrareliable low latency communication (URLLC) application where the main industrial unit or industrial control node (CN) sends messages to the target device (TD) with the aid of a cooperative device (CD). We investigate a novel transmission protocol and analyze the network’s performance. Considering the nonlinear energy harvesting (EH) mechanism at power-constrained nodes and direct and cooperative phase transmissions, the outage probability (OP) and block error rate (BLER) performances are evaluated for Rayleigh distributed fading channels. The analytical results are validated through Monte–Carlo simulations.

The real-time electrical equipment management, such as renewable energy, controllable loads, and storage units, plays a key role in low-carbon operation of smart industrial park. Digital twin (DT), which explores cloud-edge-device collaboration and artificial intelligence to establish accurate digital representation of physical equipment, is a cutting-edge technology to realize intelligent optimization of electrical equipment management. However, the practical implementation still faces reliability and communication efficiency problems, such as adverse impact of electromagnetic interference on DT reliability, high communication cost of DT model training, and uncoordinated resource allocation among cloud, edge, and device layers. We propose a Cloud-edge-device Collaborative reliable and Communication-efficient DT for lOW-carbon electrical equipment management named$\text{C}^{3}$-FLOW. It minimizes the long-term global loss function and time-average communication cost by jointly optimizing device scheduling, channel allocation, and computational resource allocation. Simulation results verify that$\text{C}^{3}$-FLOW performs superior in loss function, communication efficiency, and carbon emission reduction.

The papers presented in this special issue focus on technological innovations in wearable, implantable, mobile, and remote healthcare that include Internet of Things (IoT), sensor informatics, and patient monitoring applications. These new technologies are used to track the key signs of people’s health to improve their lifestyle and health disorders. Innovations in IoT devices play a vital role in assisting patients in managing their health conditions. Thanks to the advent of modern communication technologies and Internet of Things (IoT) paradigms that have made the implementation of biomedical devices nearly universal. Now with the evolving industrial revolution, patients and healthcare providers are expecting something more. Practically speaking, healthcare wearables have experienced tremendous growth in the past few years, and it is expected to grow even more shortly, making it an ideal space for the biomedical informatics research community to solve complex healthcare problems and more informed decision making to improve human health.

We are delighted to introduce the third part of the Special Section on Intelligent Autonomous Transportation Systems with 6G, which aims to provide the scientific community with a comprehensive overview of innovative technologies, advanced architectures, and potential challenges for the 6G-supported Intelligent Autonomous Transport Systems. Twenty articles were selected for publication in this issue. All the articles were rigorously evaluated according to the standard reviewing process of the IEEE Transactions on Intelligent Transportation Systems. The evaluation process considered originality, technical quality, presentational quality, and overall contribution. We will introduce these articles and highlight their main contributions in the following.

We are delighted to introduce the fourth part of the Special Issue on Intelligent Autonomous Transportation Systems ith 6G, hich aims to provide the scientific community ith a comprehensive overvie of innovative technologies, advanced architectures, and potential challenges for the 6G-supported Intelligent Autonomous Transport Systems. Forty-three papers ere selected for publication in this issue. All the papers ere rigorously evaluated according to the standard revieing process of the IEEE Transactions on Intelligent Transportation Systems. The evaluation process considered originality, technical quality, presentational quality, and overall contribution. e ill introduce these articles and highlight their main contributions in the folloing.

The satellite-terrestrial networks (STN) utilize the spacious coverage and low transmission latency of the Low Earth Orbit (LEO) constellation to transfer requested content for subscribers especially in remote areas. With the development of storage and computing capacity of satellite onboard equipment, it is considered promising to leverage in-network caching technology on STN to improve content distribution efficiency. However, traditional caching and distribution schemes are not suitable in STN, considering dynamic satellite propagation links and time-varying topology. More specifically, the unevenness of user distribution heightens difficulties for assurance of user quality of experience. To address these problems, we first propose a density-based network division algorithm. The STN is divided into a series of blocks with different sizes to amortize the data delivery costs. To deploy the caching satellites, we analyze the link connectivity and propose an approximate minimum coverage vertex set algorithm. Then, a novel cache node selection algorithm is designed for optimal subscriber matching. On the basis of time-varying network model, the STN cache content updating mechanism is derived to enable a stable and sustainable quality of user experience. The simulation results demonstrate that the proposed user-oriented STN content distribution scheme can obviously reduce the average propagation delay and network load under different network conditions and has better stability and self-adaptability under continuous time variation.

introduction Share on Introduction to the Special Issue on Cognitive Computing for Internet of Medical Things in Smart Healthcare Authors: Syed Hassan A. Shah California State University, Fullerton, USA California State University, Fullerton, USA 0000-0002-1381-5095View Profile , Shahid Mumtaz Instituto de Telecomunicações, Portugal Instituto de Telecomunicações, Portugal 0000-0001-6364-6149Search about this author , Wei Wei Xi'an University of Technology, China Xi'an University of Technology, China 0000-0002-8751-9205Search about this author Authors Info & Claims ACM Transactions on Sensor NetworksVolume 19Issue 3Article No.: 48epp 1–3https://doi.org/10.1145/3584742Published:25 April 2023Publication History 0citation69DownloadsMetricsTotal Citations0Total Downloads69Last 12 Months69Last 6 weeks9 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

A new differential chaos shift keying scheme with multidimensional index modulation, referred to as MIM-DCSK scheme, is proposed in this paper. This design objective of the proposed MIM-DCSK scheme is to enhance the data rate, energy efficiency, and spectral efficiency of traditional DCSK scheme. In the proposed MIM-DCSK scheme, in addition to the information bits allocated for physical transmission, multidimensional transmission entities, namely the time slot, carrier, and Walsh code are simultaneously considered as indices to convey additional information bits, thus achieving high data rate, spectral efficiency, and energy efficiency. The theoretical bit-error-rate (BER) expressions of the MIM-DCSK scheme are derived over additive white Gaussian noise (AWGN) and multipath Rayleigh fading channels. Furthermore, the data rate, complexity, spectral efficiency, and energy efficiency of the MIM-DCSK scheme are analyzed. Simulation results verify the accuracy of the theoretical analysis and illustrate the superiority of the proposed scheme. The proposed MIM-DCSK scheme is a promising solution for low-power and low-cost short-wireless communications.

Dispatching and control information freshness conducts an important impact on the training accuracy of distributed energy dispatching and control model. Poor information freshness will increase the loss function of the training model, and reduce the reliability and economy of dispatching and control. Simplified power internet of things can provide plug-and-play and multi- mode fusion communication support, but it still faces challenges of the coupling of model training and data transmission as well as the difficulty in guaranteeing dispatching and control information freshness. In this paper, a semi-distributed federated learning- based framework for dispatching and control model training decision-making is proposed, and a dispatChing and control informAtion fReshness-aware batch size Optimization aLgorithm (CAROL) is presented. CAROL leverages deep Q network and dispatching and control information freshness awareness to learn the batch size optimization strategy. CAROL can minimize model loss function while guaranteeing long-term dispatching and control information freshness constraints. Compared with existing feder- ated learning algorithms, CAROL achieves superior performance in global loss function and information freshness.

By leveraging massive low-cost reconfigurable reflect array elements, intelligent reflecting surface (IRS) is recently proposed to exhibit the favorable wireless propagation environment of Internet of Things (IoT) systems. In this article, we provide a spectrum-efficiency approach by exploiting non-orthogonal multiple access (NOMA) as well as cognitive radio (CR) to form NOMA IRS-assisted CR system. In this IRS-based IoT, the active access point in secondary network transmits beamforming and the passive IRS elements are jointly operated to achieve different performance relying on demands of IoT devices, while maintaining the normal operation of primary network. Then, the exact closed-form formulas are introduced to evaluate outage probability at each IoT device. Moreover, to provide more insights of the system, a diversity order is considered aiming to look at limitation of outage performance when the system tries to increase average signal to noise ratio (SNR) at the secondary transmitter. Finally, we conduct numerical simulations to verify the superior performance of IoT systems with higher meta-surface elements at IRS over the necessary comparisons in practical scenarios.

Multi-mode power internet of things (PIoT) provides spatio-temporal coverage for low-carbon operation in smart park through combining various communication media. Heterogeneous resources are dynamically and intelligently managed to improve resource utilization and achieve anti-eavesdropping. However, resource management in multi-mode power IoT confronts challenges such as the mutual contradiction in joint communication and security quality of service (QoS) guarantee and the inadaptability to low-carbon services. In this paper, we propose an Adaptive learNing-based secure and enerGy-awarE resource management aLgorithm (ANGEL) to optimize multi-mode channel selection and power splitting for artificial noise (AN)-based anti-eavesdropping. Based on deep actor-critic (DAC) and “win or learn fast (WoLF)” mechanism, ANGEL can realize multi-attribute QoS guarantee, adaptive resource management, and security enhancement. Simulation results demonstrate its superior performance in energy consumption, secrecy capacity, and adaptability to differentiated low-carbon services.

Recently, reconfigurable intelligent surface (RIS) has arisen as an excellent technology for assisting wireless communications. In order to handle the intractable non-convex problem for jointly optimizing beamforming and PSs in multiple-input multiple-output (MIMO) transmission, we propose a novel alternating direction (AD) method by maximizing the achievable rate (AR) at the receiver. Specifically, the initial problem is divided into the following two processes: i) optimizing the beamforming vector with fixed PSs, ii) determining a specific PS based on a closed-form solution when the other PSs and beamforming are fixed. Simulation results corroborate that the proposed AD method provides robust attainable performance with reduced computational complexity compared to its traditional counterparts.

Channel modeling has always been the core part in communication system design and development, especially in 5G and 6G era. Traditional approaches like stochastic channel modeling and ray-tracing (RT) based channel modeling depend heavily on measurement data or simulation, which are usually expensive and time consuming. In this paper, we propose a novel super resolution (SR) model for generating channel character-istics data. The model is based on multi-task learning (MTL) convolutional neural networks (CNN) with residual connection. Experiments demonstrate that the proposed SR model could achieve excellent performances in mean absolute error and standard deviation of error. Advantages of the proposed model are demonstrated in comparisons with other state-of-the-art deep learning models. Ablation study also proved the necessity of multi-task learning and techniques in model design. The contribution in this paper could be helpful in channel modeling, network optimization, positioning and other wireless channel characteristics related work by largely reducing workload of simulation or measurement.

In this paper, we investigate the collaborative computation offloading method in satellite edge computing by allowing computation tasks to be executed by multiple satellites with computing capacity. The main purpose is to optimize the resource allocation to minimize the energy consumption of the network, which is formulated as a non-convex optimization problem. To solve it efficiently, we first provide the optimal task allocation scheme and then divide the original optimization problem into two subproblems based on an alternative optimization method. Although two subproblems are still non-convex, we can apply successive convex approximation method to deal with them and design an iterative algorithm to solve them. Finally, simulation results demonstrate the superiority and effectiveness of our proposed algorithm.

In the last decade, MIMO spatial multiplexing and distributed beamforming play a significant role in improving data throughput through cooperative transmission. It has been widely used in wireless communication, especially in 6G. However, the distributed uplink beamforming is still an open problem in highly dynamic environments. However, the proposed 6G technology represents the further integration of deep learning and wireless communication. In this paper, we propose Argute Distributed Uplink Beamforming (ArguteDUB), which uses a feedback algorithm with an offline-trained deep learning model to implement highly dynamic distributed uplink beamforming for the Internet of Vehicles (IoV) in 6G. Specifically, each vehicle enables the base station (BS)/access point (AP) to separate different channel state information (CSI) by inserting orthogonal sequences into the sending data. The BS adopts deep learning to filter the noise and predict the beamforming weight to achieve phase synchronization. Unlike traditional distributed uplink beamforming, ArguteDUB can be adapted to the highly dynamic time-varying channels. The simple network structure ensures the fast response of ArguteDUB. In addition, we make ArguteDUB Orthogonal Frequency Division Multiplexing (OFDM) compatible so that it can be easily deployed in 6G networks. Our evaluation shows that ArguteDUB has an SNR gain of about 5dB to 5.3dB over the single vehicle transmission mode.

Non-linear Multiple-Input Multiple-Output (MIMO) has attracted considerable attention because of its high power-efficiency characteristic, particularly in the fifth generation (5G) and beyond. This paper focuses on the non-linear MIMO baseband algorithms in power-efficiency networks. In previous works, Generalized Approximate Message Passing (GAMP) and importance sampling technique were used to solve the non-linear distortion in Halved Phase-Only (HPO-) MIMO system. However, its convergence rate becomes unstable, and it’s converge is not guaranteed in some cases. In this paper, to improve the efficiency of convergence rate, we propose Gaussian Mixture Model (GMM) based ExpectationMaximization (EM) signal processing algorithm in HPO MIMO system. We first transforme channel estimation and multiuser detection problems into generalized linear mixed problems under π-phase observations. Then, the GMM algorithm is used to estimate the distribution of π-phase observation. Meanwhile, the EM algorithm is used to estimate the recovered signal. Simulation results show that the proposed method achieves high convergence and has better performance than the reference GAMP algorithm.

The low-carbon operation of smart park requires to deploy massive internet of things (IoT) devices to provide real-time monitoring and control services. Digital twin (DT) provides accurate guidance for communication network resource management in low-carbon smart park by establishing a digital representation of physical entities. Facing the strict requirements of DT on delay and accuracy, as well as the constraints of access priority and energy consumption, we propose a federated learning-based DT framework and a Latency-awarE diGital twIn assisted resOurce maNagement algorithm (LEGION). LEGION can achieve a well tradeoff between delay and accuracy performances under the long-term constraints of access priority and energy consumption. Compared with existing algorithms, LEGION has superior performance in average iteration delay, DT loss function, energy consumption, and access priority deficit.

In this paper, we investigate the performance of space-time block-coded multiple-input-multiple-output (STBCMIMO) LoRa system over Nakagami-m and Ricean fading channels with perfect and imperfect channel state information (CSI). Specifically, we derive the closed-form bit-error-rate expressions and analyze the diversity order of the STBC-MIMO LoRa system with perfect and imperfect CSI, where two common channel estimation error models are considered. Moreover, we perform simulations to evaluate the coverage performance of the system and to verify the accuracy of the theoretical analyses.

Artificial noise elimination (ANE) has arisen as a possible countermeasure for mitigating the influence of artificial noise (AN) at the eavesdropper (Eve). However, conventional ANE schemes require the attainable channel state information (CSI) between the transmitter (Alice) and legitimate receiver (Bob), which reduces the feasibility of this proposal. In this paper, we investigate the issue of ANE without the CSI of Alice-Bob link by minimizing the artificial-noise-to-signal ratio (ANSR). Moreover, the detailed minor component analysis (MCA) algorithm is presented, and the computational complexity is quantified. Simulation results demonstrate that MCA can effectively degrade the influence of AN without the knowledge of CSI.

Abstract Multi‐mode green internet of things (IoT) network that integrates multiple communication media can well meet data transmission and processing demands of low‐carbon smart building. However, network resource management optimisation including joint optimisation of gateway and channel selection still faces technical challenges such as differentiated quality of service (QoS) demand guarantee, coupling between optimisation problems with different timescales, and adversary caused by multi‐device competition. To address these challenges, an adversarial learning‐based multi‐timescale network resource management algorithm for multi‐mode green IoT is proposed. Specifically, the minimisation problem of weighted difference between energy consumption and throughput under the long‐term queuing delay constraints is formulated to achieve differentiated QoS guarantee. Large‐timescale gateway selection is decoupled from small‐timescale channel selection by establishing matching preferences based on empirical performance, and optimised by using bilateral matching with quota. Finally, an exponential‐weight algorithm for exploration and exploitation (EXP3)‐based small‐timescale channel selection algorithm is proposed to achieve adversary awareness. Simulation results demonstrate that compared with asynchronous greedy matching algorithm and auction‐based many‐to‐many matching algorithm, the proposed algorithm performs superior in terms of energy consumption and throughput.

Abstract The green internet of things with heterogeneous communication technologies can provide data transmission and computing services for low‐carbon operation of smart buildings. However, latency‐sensitive task offloading in smart buildings for multi‐mode green internet of things still faces several challenges such as coupling between multi‐mode channel and multiple gateway selection, diversified quality of service requirement guarantee, and contradiction of long‐term performance guarantee and short‐term optimisation objectives. To address these challenges, a three‐dimensional quota matching‐based latency‐sensitive task offloading algorithm is proposed to minimise the weighted difference between energy consumption and throughput under the long‐term queuing delay constraints. Specifically, the minimisation problem is decoupled by Lyapunov optimisation. The three‐dimensional quota matching among devices, gateways, and channels is employed to solve the conflicts between gateway selection and channel selection. Finally, the three‐dimensional quota matching is converted to a two‐side quota matching to further reduce complexity and solved iteratively. Numerical results demonstrate that compared with H3CG and MMCS, the proposed algorithm improves the weighted difference between energy consumption and throughput by 21.85% and 27.91%, respectively, and reduces the sensor‐side average queuing delay by 30.82% and 16.83%, and gateway‐side average queuing delay by 16.57% and 26.71%, respectively.

The modern industrial sector generates enormous amounts of high-dimensional heterogeneous data daily. However, mostly the vectored data (rank-one tensor) have been considered for anomaly detection, whereas the data in real-life is high dimensional. The expressive power of methods based on vector data is restrictive as they may destroy the structural information embedded in data and lead to the curse-of-dimensionality and overfitting. In this paper, we present a novel anomaly detection approach for large-scale tensor data. We first present novel one-class support tensor machines (OCSTM) with bounded loss function. We further extend it by leveraging the randomness to design a scalable approach that can also be used for large-scale anomaly detection. To solve the corresponding optimization of the objective function, we utilize half-quadratic optimization followed by solving it like a traditional OCSTM optimization at each iteration. We demonstrate the proposed randomized OCSTM with bounded hinge loss through experiments on 14 benchmark data sets. Experimental results demonstrate the effectiveness of the proposed approach against anomalies and a significant reduction in the computational complexity.

Alzheimer's disease (AD) is an irreversible and progressive disorder where a large number of brain cells and their connections degenerate and die, eventually destroy the memory and other important mental functions that affect memory, thinking, language, judgment, and behavior. Not a single test can effectively determine AD; however, CT and magnetic resonance imaging (MRI) can be used to observe the decrease in size of different areas (mainly temporal and parietal lobes). This paper proposes an integrative deep ensemble learning framework to obtain better predictive performance for AD diagnosis. Unlike DenseNet, we present a multiresolutional ensemble PartialNet tailored to Alzheimer detection using brain MRIs. PartialNet incorporates the properties of identity mappings, diversified depth as well as deep supervision, thus, considers feature reuse that in turn results in better learning. Additionally, the proposed ensemble PartialNet demonstrates better characteristics in terms of vanishing gradient, diminishing forward flow with better training time, and a low number of parameters compared with DenseNet. Experiments performed on benchmark AD neuroimaging initiative data set that showed considerable performance gain (2 + % ↑ $\uparrow $ ) and (1.2 + % ↑ $\uparrow $ ) for multiclass and binary class in AD detection in comparison to state-of-the-art methods.

As one of the most successful industrial realizations of Internet of Things, a smart grid is a smart IoT system that deploys widespread smart meters to capture fine-grained data on residential power usage. Unfortunately, it always suffers diverse privacy attacks, which seriously increases the risk of violating the privacy of customers. Although some solutions have been proposed to address this privacy issue, most of them mainly rely on a trusted party and focus on the sanitization of metering masurements. Moreover, these solutions are vulnerable to advanced attacks. In this paper, we propose a decentralized mechanism for privacy-preserving computation in smart grid called DDP, which leaverages the differential privacy and extends the data sanitization from the value domain to the time domain. Specifically, we inject Laplace noise to the measurements at the end of each customer in a distributed manner, and then use a random permutation algorithm to shuffle the power measurement sequence, thereby enforcing differential privacy after aggregation and preventing the sensitive power usage mode informaton of the customers from being inferred by other parties. Extensive experiments demonstrate that DDP shows an outstanding performance in terms of privacy from the non-intrusive load monitoring (NILM) attacks and utility by using two different error analysis.

Reconfigurable intelligent surface (RIS) has emerged as an important transmission technology for numerous applications in Internet of Things (IoT) systems. Thus, in this paper, we investigate the application of RIS in energy harvesting (EH) based unmanned aerial vehicle (UAV) communication network with finite block length (BL) codes, where a rotary wing type flying UAV communicates with the multiple single antenna IoT users with the aid of multiple RISs mounted on several skyscraper buildings. To transmit the signal to a particular IoT user, the UAV selects an RIS on the basis of either UAV-RIS (i.e., partial) or UAV-RIS-IoT (i.e., full) channel state information (CSI) and then transmits the signal through the selected RIS along with the direct link transmission. In particular, we derive (i) the expression for probability of RIS selection, (ii) the statistical distribution of instantaneously received information signal-to-noise ratio (SNR) at the IoT user. Based on the derived statistics, we analyze the performance of the considered system under finite BL codes in terms of the average outage probability, average block error rate (ABLER) and goodput averaged over entire flying duration. Moreover, the BLER performance with finite BL codes is also compared with the infinite BL codes scenario. Additionally, we also investigate the impact of various channel and system parameters like imperfect CSI, number of RISs and the number of reflecting elements at each RIS, location of IoT users, variable altitude of the UAV, and the severity of channel fading of UAV-RIS link on the system performance. Furthermore, we have obtained the optimum UAV location in each time slot which minimizes the ABLER per time slot over all the users in the network. The analytical results are corroborated with Monte Carlo simulations.

To realize high energy-efficient, high spectrum-efficient, and high-throughput data transmission, a multi-carrier differential chaos shift keying communication system with hybrid index modulation, referred to asHIM-MC-DCSK system, is proposed in this paper. In the HIM-MC-DCSK system, we intelligently integrate the carrier-number-index technique and carrier-index technique into the MC-DCSK modulation in order to significantly boost the transmission efficiency in wireless communication systems. Especially, we use a pair of orthogonal signals to represent the active and inactive subcarriers in the HIM-MC-DCSK system so as to exploit all the subcarriers to transmit information bits. In addition, the theoretical bit-error-rate (BER) expressions of the HIM-MC-DCSK system are derived over additive white Gaussian noise (AWGN) and multipath Rayleigh fading channels. Also, the data rate, spectrum efficiency, energy efficiency, and complexity of the proposed system are carefully analyzed. Monte-Carlo simulations not only verify the accuracy of the theoretical analysis but also illustrate the superiority of the proposed system.

In conventional star-topology LoRa networks, the gateways are expected to collect the data from all the nodes nearby. However, a major challenge for the conventional LoRa system is the performance degradation due to the long-range communication over fading channels. To resolve the challenging issue, this paper investigates a two-hop amplify-and-forward relaying LoRa network in a two-dimension plane, where random nodal distances are considered. Moreover, a relay-selection mechanism is developed for the proposed system. Based on the best relay-selection protocol, the analytical bit-error-rate (BER) and asymptotic BER expressions, achievable diversity order, coverage probability, and throughput of the proposed system are derived over the Nakagami-$m$fading channel. Furthermore, to maximize the throughput of the proposed system, a two-dimensional resource allocation optimization problem (i.e., the spread factor selection and power allocation optimization) is formulated and investigated. The proposed optimal spread factor selection and power allocation scheme is verified to outperform the two baseline schemes. Simulation and numerical results show that although the proposed system reduces the throughput compared to the conventional LoRa system, it significantly improves the BER and coverage probability. Hence, the proposed system can be considered as a promising technique for low-power, long-range and highly reliable Internet-of-Things applications.

Multi-access edge computing (MEC) has been one promising solution to reduce the computation delay of wireless devices. Due to the high spectrum efficiency of non-orthogonal multiple access (NOMA), this paper studies the single-user multi-edge-server MEC system based on downlink NOMA, aiming to minimize task computation delay by jointly optimizing the NOMA-based transmission duration (TD) and workload offloading allocation (WOA) among edge computing servers. This task computation delay minimization (CDM) problem is formulated as a nonconvex optimization problem. To solve the CDM problem efficiently, we decompose it into the sub-problem of determining the optimal WOA with a given TD and the top-problem of optimizing the TD. For the sub-problem, we first derive its some important properties and then design an efficient channel quality ranking based algorithm to obtain the optimal WOA. We solve the top-problem for the static-channel and dynamic-channel scenarios, respectively. For the static-channel scenario, we design an optimal algorithm which only apply once the golden section search method to obtain the optimal TD of first task and directly obtain the optimal offloading solution for any consequently arrived task with different workloads. For the dynamic-channel scenario where the channel qualities from the wireless device to the edge-computing servers are varying, it is critical to quickly determine the current task’s offloading solution under the current channel state and task workload, which is very challenging for the traditional optimization methods. In order to conquer this challenge, we propose the deep reinforcement learning (DRL) based algorithm, which can obtain the near-optimal offloading solution instantly after enough learning. Finally, we validate through simulations the advantages of NOMA over frequency division multiple access (FDMA).

In this paper, we conceive the design of dynamic wireless networks assisted by multiple intelligent reflecting surfaces (IRSs), where the connection states between users and IRSs are capable of being updated timely. Taking into account the time-varying states of the system, we further construct a long-term dynamic process. Our goal is to maximize the time average sum-rate of the dynamic system under the time average rate and power constraints of users, via jointly optimizing the power allocation at users and the reflecting coefficients at IRSs. With the aid of Lyapunov concept-based drift-plus-penalty (DPP) algorithm, the long-term optimization problem is formulated as an infinite-horizon time-average one. Subsequently, the fractional programming method based on Lagrangian dual transform is applied to optimize power allocation and reflecting coefficients in an iterative manner, and the closed-form solutions of power and reflecting coefficients can be obtained at each iteration. Finally, simulation results demonstrate the convergence and effectiveness of the proposed algorithm. Further performance comparisons indicate that the proposed algorithm can maintain a balance between supply and demand for resource allocation and improve the fairness of users.

The combination of air–ground integrated power Internet of Things (AGI-PIoT) and cloud-edge-end collaboration enables flexible coverage and real-time data processing. However, how to achieve intelligent cloud-edge-end collaboration in AGI-PIoT faces several challenges such as dynamics of aerial networks, coupling of resource allocation in multiple layers, timescales, and dimensions, incomplete information, and dimensionality curse. In this article, we propose a FEderated Deep rEinforcement leaRning-based multi-lAyer multi-Timescale multi-dImensional resOurce allocatioN algorithm (FEDERATION). The multilayer multitimescale multidimensional resource allocation problem is decomposed into three subproblems based on Lyapunov optimization. For the subproblem of joint task offloading and power control, a federated deep actor-critic-based semidistributed algorithm is developed. The subproblem of admission control is solved by quadratic programming. The third subproblem is addressed through smooth approximation and Lagrange dual decomposition. Simulation results indicate that FEDERATION outperforms existing algorithms in queuing delay, energy consumption, and convergence.

Digital twin (DT) provides accurate guidance for multidimensional resource scheduling in 5G edge computing-empowered distribution grids by establishing a digital representation of the physical entities. In this article, we address the critical challenges of DT construction and DT-assisted resource scheduling such as low accuracy, large iteration delay, and security threats. We propose a federated learning-based DT framework and present a Secure and lAtency-aware dIgital twin assisted resource scheduliNg algoriThm (SAINT). SAINT achieves low-latency, accurate, and secure DT by jointly optimizing its total iteration delay and loss function, and leveraging abnormal model recognition (AMR). SAINT enables intelligent resource scheduling by using DT to improve the learning performance of deep Q-learning. SAINT supports access priority and energy consumption awareness due to the consideration of long-term constraints. Compared with state-of-the-art algorithms, SAINT has superior performance in cumulative iteration delay, DT loss function, energy consumption, and access priority deficit.

Network virtualization allows the coexistence of multiple network slices over a shared physical infrastructure, each delivering a service with own requirements in terms of quality of service, coverage, and time span. Spectrum is an expensive commodity, so it must be managed among these slices in the most efficient way. However, current spectrum sharing techniques are too rigid to address all combinations of service requirements and properly exploit the new air interface flexibility and network deployment options introduced by the Fifth Generation (5G) mobile networks. In this paper, we extend the state of the art first by proposing a novel spectrum sharing scheme that supports an unlimited number of radio networks. Second, we propose a 5G compliant novel service-based network management architecture to integrate Machine Learning (ML) algorithms for Radio Resource Management (RRM), including spectrum sharing. Finally, we propose a new three-stage ML framework that exploits forecasting, clustering and reinforcement learning algorithms to implement the proposed spectrum sharing scheme. Our proposed solution can allow an arbitrary number of 5G network slices to share spectrum more effectively and more dynamically either with each other or with other radio networks.

Recently, we have experienced an incredible surge of interest in connected and autonomous vehicles and related enabling technologies, which are expected to revolutionize future Intelligent Transportation Systems (ITS). This surging demand and popularity of ITS with the Internet of Vehicles technology has led to a tremendous rise in the number of connected vehicles. Driven by this massive number of connected vehicles, and the stringent requirements of autonomous vehicles and data-intensive applications such as ultralow latency, high reliability, and high security, intelligent transportation systems are rapidly moving to the 6G networks. The 6G-supported ITS is expected to be a transformative factor for both society and the economy by delivering unprecedented, seamless, reliable, efficient massive connectivity to millions of users and connected vehicles.

In the near future, the 6G-supported Intelligent Transportation System (ITS) is expected to be a transformative factor for both society and the economy by delivering unprecedented, seamless, reliable, efficient massive connectivity to millions of users and connected vehicles. This Special Issue aims to provide the scientific community with a comprehensive overview of innovative technologies, advanced architectures, and potential challenges for the 6G-supported Intelligent Autonomous Transport System. Twenty articles were selected for publication in the second part of the issue. All the articles were rigorously evaluated according to the standard reviewing process of the IEEE Transactions on Intelligent Transportation Systems. The evaluation process considered factors pertaining to originality, technical quality, presentational quality, and overall contribution. We will introduce these articles and highlight their main contributions in the following.

Currently, the highly dynamic fog computing resource requirements introduced by the diverse services of the Internet of Things (IoT) result in an imbalance between computing resource providers and consumers. However, current computing resource scheduling schemes cannot cognize the dynamic resources available and do not possess decision-making or management capabilities, which leads to inefficient use of computing resources and a decreased quality of service (QoS). Balancing computing resources cognitively at the IoT edge remains unresolved. In this paper, a cognition-centric fog computing resource balancing (CFCRB) scheme is proposed for edge intelligence-enabled IoT. First, we propose a cognitive balance architecture with a cognition plane, which includes service demand monitoring, policy processing and knowledge storage of cognitive fog resources. Second, we propose the fog functions structure with sensing, interaction and learning functionalities, realizing the knowledge-based proactive discovery and dynamic orchestration of resource sharing nodes. Finally, a distributed edge learning algorithm is proposed to construct knowledge of the balance between computing resource helpers and requesters in cognitive fogs, which is further proved with mathematics. The simulation results indicate the efficiency of the proposed scheme.

Driving safety is one of the most important points to concern on the road. Vehicles constantly generate messages under vehicle-to-everything (V2X) assisted driving. Especially, in dense urban environments, the massive messages carrying precise data can help us to improve road safety. However, vehicles do not always provide accurate data due to a variety of reasons, such as defective vehicle sensors, or selfish. It is critical to check and analyze the data supplied by vehicles in real time and correct the possible errors to eliminate the unsafe issues. In this article, we introduce a cOllaborative vehiClE dAta correctioN method (OCEAN) based on rationality and$Q$-learning techniques to correct the error V2X data for ensuring the driving safety of vehicles on the road, which can be deployed on both vehicles and road side unit. Extensive experimental results show that OCEAN can detect error V2X data up to 80$\%$and cut down 60$\%$average error distance for most attributes in vehicle data.

Convergecast is a fundamental operation in wireless sensor networks (WSNs). To support long-term deployment of WSNs, sensor nodes normally operate at low-duty-cycles. However, the low-duty-cycle operation significantly reduces the communication chance between nodes. Consequently, the risk of data collisions significantly increases when multiple senders transmit packets to a receiver during its very short active period. This problem further causes not only wasted packet retransmissions, but also a large delivery latency. Under such conditions, collision-free medium access is more appealing than recovering after collision for low-duty-cycle WSNs. In this work, we propose anincast-collision-free convergecast protocol, named iCore, to address the many-to-one collision problem in low-duty-cycle WSNs. iCore employs the dynamic forwarding technique, establishes a non-conflicting schedule for efficient convergecast, and improves the channel utilization by allowing senders to opportunistically transmit packets once detecting unused slots. Specifically, we design efficient forwarder assignment and forwarding optimization algorithms that ensure low end-to-end latency under diverse data traffic types. Through comprehensive performance evaluations, we demonstrate that, compared with the baseline protocol, iCore effectively minimizes the end-to-end delay by 25% ~ 57% and maintains high delivery ratio and energy efficiency for different many-to-one convergecast scenarios.

The rapid technological advances of cellular technologies will revolutionize network automation in industrial internet of things (IIoT). In this paper, we investigate the two-timescale resource allocation problem in IIoT networks with hybrid energy supply, where temporal variations of energy harvesting (EH), electricity price, channel state, and data arrival exhibit different granularity. The formulated problem consists of energy management at a large timescale, as well as rate control, channel selection, and power allocation at a small timescale. To address this challenge, we develop an online solution to guarantee bounded performance deviation with only causal information. Specifically, Lyapunov optimization is leveraged to transform the long-term stochastic optimization problem into a series of short-term deterministic optimization problems. Then, a low-complexity rate control algorithm is developed based on alternating direction method of multipliers (ADMM), which accelerates the convergence speed via the decomposition-coordination approach. Next, the joint channel selection and power allocation problem is transformed into a one-to-many matching problem, and solved by the proposed price-based matching with quota restriction. Finally, the proposed algorithm is verified through simulations under various system configurations.

Non-orthogonal multiple access (NOMA) promises to fulfill the fast-growing connectivities in future Internet of Things (IoT) using abundant multiple-access signatures. While explicitly notifying the utilized NOMA signatures causes large signaling cost, blind signature classification naturally becomes a low-cost option. To accomplish signature classification for NOMA, we study both likelihood- and feature-based methods. A likelihood-based method is firstly proposed and showed to be optimal in the asymptotic limit of the observations, despite high computational complexity. While feature-based classification methods promise low complexity, efficient features are non-trivial to be manually designed. To this end, we resort to artificial intelligence (AI) for deep learning-based automatic feature extraction. Specifically, our proposed deep neural network for signature classification, namely DeepClassifier, establishes on the insights gained from the likelihood-based method, which contains two stages to respectively deal with a single observation and aggregate the classification results of an observation sequence. The first stage utilizes an iterative structure where each layer employs a memory-extended network to explicitly exploit the knowledge of signature pool. The second stage incorporates the straight-through channels within a deep recurrent structure to avoid information loss of previous observations. Experiments show that DeepClassifier approaches the optimal likelihood-based method with a reduction of 90% complexity.

The advanced Internet of Things (IoT) enables more and more interactions between people and machines in the emerging applications, which rely on real-time communication and computing. However, the limited battery capacity and low computing capacity of IoT nodes can hardly support high-performance computing applications. The integration of wireless power transmission (WPT) and mobile edge computing (MEC) is a feasible and promising solution to address the energy shortage and computing capacity limitation of IoT nodes by harvesting radio frequency signal’s energy and offloading the nodes’ computation tasks to edge computing servers (ECSs). In this work, we focus on the wireless powered MEC network with an ECS and multiple edge devices (EDs), and study the joint optimization of WPT duration, transmission time allocation of each ED and partial offloading decision to maximize the sum computation rate. First, we formulate this as a non-convex problem which is hard to solve. Second, to conquer this problem, we decompose the original offloading problem into the sub-problem of optimizing the offloading time allocation among EDs and the proportion of harvested energy allocated for offloading at each ED under a given WPT duration and the top-problem of optimizing the WPT duration. Finally, we design an online DRL-based framework where one DNN together with its exploration strategy and training strategy is adopted to learn the near-optimal WPT duration and an efficient optimal algorithm is designed to solve the sub-problem. Numerical results show that the DRL-based offloading algorithm achieves the near-maximal sum computation rate while greatly reducing the processing time by at least three orders of magnitude compared with using the solver CVX for the sub-problem and the DNN for the top-problem.

Since Internet of Things (IoT) is suggested as the fundamental platform to adapt massive connections and secure transmission, we study physical-layer authentication in the point-to-point wireless systems relying on reconfigurable intelligent surfaces (RIS) technique. Due to lack of direct link from IoT devices (both legal and illegal devices) to the access point, we benefit from RIS by considering two main secure performance metrics. As main goal, we examine the secrecy performance of a RIS-aided wireless communication systems which show secure performance in the presence of an eavesdropping IoT devices. In this circumstance, RIS is placed between the access point and the legitimate devices and is designed to enhance the link security. To specify secure system performance metrics, we firstly present analytical results for the secrecy outage probability. Then, secrecy rate is further examined. Interestingly, we are to control both the average signal-to-noise ratio at the source and the number of metasurface elements of the RIS to achieve improved system performance. We verify derived expressions by matching Monte-Carlo simulation and analytical results.

The integration of air-ground electricity internet of things (AGE-IoT) and machine learning, enables flexible network coverage and intelligent task offloading. However, dynamics of AGE-IoT networks, incomplete information, and resource allocation coupling are still major challenges in achieving intelligent AGE-IoT. In this paper, we investigate a joint multi-timescale task offloading and power control optimization problem to minimize the queuing delay of all the EIoT devices under the long-term constraint of energy consumption. We firstly decompose the joint optimization problem and transform it to large-timescale task offloading optimization and small-timescale power control optimization. Then, we propose a fed-erated deep actor-critic-based task offloading algorithm (FDAC) with two actor-critic networks for multi-timescale optimization. Numerical results show that FDAC has excellent performances in queuing delay and energy consumption compared with existing algorithms.

Collaborative vehicular network is a key enabler to meet the stringent communication and computing requirements of user vehicles (UVs). A UV dynamically optimizes task offloading by exploiting its collaborations with edge servers and vehicular fog servers (VFSs). However, the optimization of task offloading in highly dynamic collaborative vehicular networks faces several challenges such as queuing delay guaranteeing, incomplete information, and dimensionality curse. In this paper, a Deep Reinforcement lEarning-based queue-Aware task offloading algorithM named DREAM is proposed to maximize the throughput of the UVs while satisfying the long-term queuing delay constraints in a best-effort way. Compared with existing task offloading algorithms, DREAM achieves superior performance in throughput, convergence, and queuing delay.

Coverage, capacity and throughput can be enhanced significantly by offering a hybrid solution consisting of microwave high power base station (HPB) underlaid millimeter wave low power base station (LPB) augmented by the downlink uplink decoupled (DU-De) user association strategy in N-tier heterogeneous networks (HetNets). However, secure user association, power and microwave and millimeter wave sub-carriers allocation employing DU-De strategy has not been investigated in the past. This work formulates mathematical models for DU-De strategy and downlink uplink coupled (DU-Co) strategy to investigate secure user association, power and sub-carrier in microwave and millimeter wave band allocation for secrecy rate maximization in N-tier HetNets. Nature of the formulated problems is complex, challenging and NP-hard. Outer approximation algorithm, with less complexity, is used to solve the formulated problems for optimal solution. Extensive simulation results in terms of secure user association and average secrecy rate show the effectiveness of DU-De strategy over DU-Co strategy in HetNets.

In this paper, we propose a hybrid OFDM-SNM scheme, named joint-mapping OFDM-SNM (JM-OFDM-SNM), to avoid transmitting variable lengths of information bits. In JM-OFDM-SNM, the signal vectors are generated by jointly considering subcarrier activation patterns and constellation symbols. To relieve the high computational complexity of the optimal maximum-likelihood (ML) detection, we design a low-complexity detection method via resorting to the log-likelihood ratio criterion. We also analyze the upper bound on the bit error rate of JM-OFDM-SNM. To further enhance the utilization of frequency resource, we propose a more general scheme, named adaptive JM-OFDM-SNM (AJM-OFDM-SNM), to accommodate the constellation orders for different numbers of activated subcarriers. Simulation results show that AJM-OFDM-SNM achieves better performance than both JM-OFDM-SNM and OFDM-SNM at the same spectral efficiency. The low-complexity detection method of JM-OFDM-SNM achieves very close performance to the optimal ML detection, and the theoretical curves well match the simulation curves in the high signal-to-noise ratio region.

Air-Ground Integrated Power Internet of Things (AGI-PIoT) is a key enabler to meet the stringent communication and computing requirements of PIoT devices. In AGI-PIoT, the computation-intensive and delay-sensitive tasks can be either offloaded to edge servers through unmanned aerial vehicles (UAVs) or offloaded to cloud servers through ground base stations (GBSs), while the computational resources of edge servers and cloud servers should be jointly allocated. However, the joint optimization of task offloading and resource allocation faces several challenges such as incomplete information, dimensionality curse, and coupling between long-term constraints of queuing delay and short-term decision making. In this paper, we propose a learning-based QUeue-AwaRe Task offloading and rEsouRce allocation algorithm (QUARTER). Specifically, by exploiting Lyapunov optimization, the joint optimization problem is decomposed into task offloading and server-side resource allocation. For the first subproblem, we propose a Queue-aware Actor-Critic-based task offloading algorithm named QAC to cope with dimensionality curse. A low-complexity heuristic algorithm is developed to solve the second subproblem. Compared with existing task offloading and resource allocation algorithms, simulation results demonstrate that QUARTER has superior performances in throughput, queuing delay, and convergence.

The evolution of deep learning has promoted the popularization of smart devices. However, due to the insufficient development of computing hardware, the ability to conduct local training on smart devices is greatly restricted, and it is usually necessary to deploy ready-made models. This opacity makes smart devices vulnerable to deep learning backdoor attacks. Some existing countermeasures against backdoor attacks are based on the attacker’s ignorance of defense. Once the attacker knows the defense mechanism, he can easily overturn it. In this paper, we propose a Trojaning attack defense framework based on moving target defense(MTD) strategy. According to the analysis of attack-defense game types and confrontation process, the moving target defense model based on signaling game was constructed. The simulation results show that in most cases, our technology can greatly increase the attack cost of the attacker, thereby ensuring the availability of Deep Neural Networks(DNN) and protecting it from Trojaning attacks.

Visible Light Communication (VLC) is a novel optical wireless communication technology which uses Light Emitting Diodes (LEDs) and Photodiodes for coherent detection and very-high-data rate data communication system. The stringent Line of Sight (LoS) requirement in VLC makes it very suitable for Indoor Positioning System (IPS), to be used for autonomous and smart city infrastructure. The current work aims to implement a real time IPS system using VLC link in Network Simulator (NS-3). The VLC module is implemented by modelling real-time attributes of LEDs, optical channel, and the photodiodes. The localization is carried out using trilateration scheme which measures received signal strength interference (RSSI) for position estimation of the target. Furthermore, a comparison is carried out between VLC link and other existing technology, Wi-Fi, as far as positioning accuracy and other important performance metrics are concerned. The simulation results show significant improvement for the VLC link over the Wi-Fi link.

Task scheduling on edge computing servers is an important issue that affects user experience. Existing scheduling methods require centralized control to achieve the best overall performance. However, it is impractical to force all users to act according to centralized control. We propose a distributed edge computing server task scheduling model based on game theory. Our method comprehensively considers the link quality from the mobile device to the server and the server's computing resource allocation when selecting edge computing servers, and achieves a balance between link quality and computing resources. Once the Nash equilibrium is reached, our model can provide different QoS for users of different priorities. Acceleration methods are proposed to achieve the Nash equilibrium faster. The simulation results show that the proposed model can provide differentiated services while optimizing the scheduling of computing resources, and ensure that the algorithm achieves an approximate Nash equilibrium in polynomial time.

Most existing research about task allocation in mobile crowdsensing mainly focus on requester-centric mobile crowdsensing (RCMCS), where the requester assigns tasks to workers to maximize his/her benefits. A worker in RCMCS might suffer benefit damage because the tasks assigned to him/her may not maximize his/her benefit. Contrarily, worker-centric mobile crowdsensing (WCMCS), where workers autonomously select tasks to accomplish to maximize their benefits, does not receive enough attention. The workers in WCMCS can maximize their benefits, but the requester in WCMCS will suffer benefit damage (cannot maximize the number of expected completed tasks). It is hard to maximize the number of expected completed tasks in WCMCS, because some tasks may be selected by no workers, while others may be selected by many workers. In this paper, we apply task bundling to address this issue, and we formulate a novel task bundling problem in WCMCS with the objective of maximizing the number of expected completed tasks. To solve this problem, we design an algorithm named LocTrajBundling which bundles tasks based on the location of tasks and the trajectories of workers. Experimental results show that, compared with other algorithms, our algorithm can achieve a better performance in maximizing the number of expected completed tasks.

The analysis for social networks, such as the sensor-networks in socially networked industries, has shown a deep influence of intelligent information processing technology on industrial systems. The large amounts of data on these networks raise the urgent demands of analyzing the topological content effectively and efficiently in Industrial Internet of Things. One of the ways to locate important information amongst such large troves of data is to recognize influential nodes. In this article, we examine an intelligent way to recognize the influence of such nodes automatically. Motivated by the concepts of system controllability and observability from control theory, we introduce a novel method to evaluate nodes from two different aspects, namely, the ability of “observe” information on the network (i.e., observability), and the ability to propagate information to other nodes (i.e., controllability). We propose a unified data mining framework that incorporates content analysis with nodes behavioral tendencies, and show that it is able to outperform competitive baselines in recognizing influential nodes in networks. We also show that it is important to detect the presence of spammer nodes within networks, which might otherwise be wrongly recognized as influential nodes. The experimental results demonstrate the superiority of the proposed approach in comparison with baseline methods.

Driven by the Internet-of-Things (IoT)-enabled massively data-intensive applications, such as virtual-augmented-reality-based gaming, ultramassive machine-type communications, holographic rendering and high-precision communications, multiway teleconferencing, etc., there is a need for technological advancements and evolutions for wireless communications beyond the fifth-generation (5G) networks. The wireless data traffic is estimated to reach 4394 EB by 2030 (Source: ITU), and 5G will be unable to provide support to most of these advanced applications. Here, 6G is expected to extend the 5G capabilities to very high levels where millions of connected devices and applications could operate seamlessly with high data rates and low latency.

In the Internet of Health Things (IoHT)-based e-Health paradigm, a large number of computational-intensive tasks have to be offloaded from resource-limited IoHT devices to proximal powerful edge servers to reduce latency and improve energy efficiency. However, the lack of global state information (GSI), the adversarial competition among multiple IoHT devices, and the ultra reliable and low latency communication (URLLC) constraints have imposed new challenges for task offloading optimization. In this article, we formulate the task offloading problem as an adversarial multi-armed bandit (MAB) problem. In addition to the average-based performance metrics, bound violation probability, occurrence probability of extreme events, and statistical properties of excess values are employed to characterize URLLC constraints. Then, we propose a URLLC-aware Task Offloading scheme based on the exponential-weight algorithm for exploration and exploitation (EXP3) named UTO-EXP3. URLLC awareness is achieved by dynamically balancing the URLLC constraint deficits and energy consumption through online learning. We provide a rigorous theoretical analysis to show that guaranteed performance with a bounded deviation can be achieved by UTO-EXP3 based on only local information. Finally, the effectiveness and reliability of UTO-EXP3 are validated through simulation results.

Non-orthogonal multiple access (NOMA) and ambient backscatter communication have been envisioned as two promising technologies for the Internet-of-things due to their high spectral efficiency and energy efficiency. Motivated by this fact, we consider an ambient backscatter NOMA system in the presence of a malicious eavesdropper. Under the realistic assumptions of residual hardware impairments (RHIs), channel estimation errors (CEEs) and imperfect successive interference cancellation (ipSIC), we investigate the physical layer security (PLS) of the ambient backscatter NOMA systems with emphasis on reliability and security. In order to further improve the security of the considered system, an artificial noise scheme is proposed where the radio frequency (RF) source acts as a jammer that transmits interference signals to the legitimate receivers and eavesdropper. On this basis, the analytical expressions for the outage probability (OP) and the intercept probability (IP) are derived. To gain more insights, the asymptotic analysis and corresponding diversity orders for the OP in the high signal-to-noise ratio (SNR) regime are carried out, and the asymptotic behaviors of the IP in the high main-to-eavesdropper ratio (MER) region are explored as well. Finally, the correctness of the theoretical analysis is verified by the Monte Carlo simulation results. These results show that compared with the non-ideal conditions, the reliability of the considered system is high under ideal conditions, but the security is low.

As a new energy saving approach for green communications, energy harvesting (EH) could be suitable technique to facilitate massive connections for large number of devices in such networks. The spectrum shortage occurs in huge number of devices which access with small-cell and macro-cell networks. To tackle these challenges, we develop a tractable framework relying on prominent techniques such as non-orthogonal multiple access (NOMA), antenna selection and energy harvesting. In this paper, we aim at practical scenarios of small cell networks by jointly evaluating capable of interference management and EH. We benefit from transmission approaches including full duplex (FD) and bi-directional transmission to improve the main performance system metrics such as outage probability and throughput. Three useful schemes are explored by considering EH and inter-cell interference. We derive the closed-form and asymptotic expressions for system metrics. We then perform extensive simulations with different system configurations to confirm the effectiveness of the proposed small-cell NOMA systems.

Orthogonal frequency division multiplexing with subcarrier number modulation (OFDM-SNM) has been recently proposed to improve the spectral efficiency (SE) of the traditional OFDM system. In this paper, we propose a joint-mapping OFDM-SNM (JM-OFDM-SNM) scheme to transmit the signal vector with a constant length of information bits by jointly considering the subcarrier activation patterns and constellation symbols. A low-complexity detection scheme based on log-likelihood ratio criterion is proposed to relieve the high computational complexity of the maximum-likelihood detection at the cost of a negligible performance loss. Upper-bounded bit error rate (BER) and lower-bounded achievable rate are both derived in closed-form to evaluate the performance of JM-OFDM-SNM. To suit different application scenarios, we further propose two enhanced schemes, named adaptive JM-OFDM-SNM (AJM-OFDM-SNM) and JM-OFDM with in-phase/quadrature SNM (JM-OFDM-IQ-SNM), where the former adjusts the constellation orders for different numbers of active subcarriers, and the latter extends the indexing to in-phase and quadrature domains. Simulation results corroborate the tightness of the derived BER expression in the high signal-to-noise ratio region and show that (A)JM-OFDM-SNM improves the performance of OFDM-SNM, while both AJM-OFDM-SNM and JM-OFDM-IQ-SNM schemes perform better than JM-OFDM-SNM at the same SE.

There are several kinds of smart devices, such as smartphones, sensors, and smart wearable devices, included in the Human-in-the-Loop (HITL) system, but different devices have their own data processing and programming paradigm. Programmers usually need to design the same data processing logic for different devices by using a different programming model. How to mapping the same code to different devices without any change is an emerging topic in the HITL system. Furthermore, the intelligent data processing for the smart CPS sector is experiencing significant growth in data volume, driven by a large number of smart devices that are anticipated in the near further. All these smart devices are expected to improve the overall HITL system performance marvelously. A large number of devices can also outstandingly increase the data volume, which needs to be processed in real time. How to process large-scale data on a smart device in real time is another challenge. Focused on these challenges, this article proposed a computing device-aware HITL CPS data processing framework, named Barge, aiming to map the regular code to the different hardware without any change. In Barge, a semantic model, an architecture-driven programming model, and a graph partition scheme are included. The semantic model is used to express the user-defined graph algorithms by using the domain-specific language. The architecture-driven programming model will execute the graph algorithms on a different device in parallel. Furthermore, the graph partition scheme will partition the large-scale graphs into suitable partitions by aware of the topology to make the partitioned data suitable for kinds of smart devices. We believe that our work would open a wide range of opportunities to improve the performance of large-scale graph processing for HITL systems.

The papers in this special section focus on cognitive analytics of social media for industrial manufacturing. Business innovation and industrial intelligence pave the way to a future in which smart factories, intelligent machines, networked processes, and big data are brought together to foster industrial growth and shift the modalities. Industry 4.0 or the Industrial Internet of Things (IIoT) is the latest catchphrase of technological innovation in manufacturing with the goal of increasing productivity in a flexible and efficient manner. Concurrently, the new collaborative Web (called Web 2.0) resiliently defines the notion of the techno-social system of computer-mediated, web/internet-based technologies and channels that have the primary objective of creating and enabling a collaborative and interactive virtual community of participants who can share or communicate information. These social technologies are essentially transforming the way we communicate, collaborate, consume, and create data and characterize one of the insurgent impacts of information technology on any industry, both within and outside industrial boundaries. Social media augments as a nontrivial element to this industrial value chain with the intent of making it more efficient. Collaborative sensing or crowd sensing can be used to help producers, suppliers, and customers understand and use insights learned from large amounts of sensing data in order to obtain competitive advantages

The ongoing deployment of 5G network involves the Internet of Things (IoT) as a new technology for the development of mobile communication, where the Internet of Everything (IoE) as the expansion of IoT has catalyzed the explosion of data and can trigger new eras. However, the fundamental and key component of the IoE depends on the computational intelligence (CI), which may be utilized in the sixth generation mobile communication system (6G). The motivation of this article presents the 6G enabled network in box (NIB) architecture as a powerful integrated solution that can support comprehensive network management and operations. The 6G enabled NIB can be used as an alternative method to meet the needs of next-generation mobile networks by dynamically reconfiguring the deployment of network functions, providing a high degree of flexibility for connection services in various situations. Especially the CI technology such as evolutionary computing, neural computing and fuzzy systems utilized as a part of NIB have inherent capabilities to handle various uncertainties, which have unique advantages in processing the variability and diversity of large amounts of data. Finally, CI technology for NIB, which is widely used is also introduced such as distributed computing, fog computing, and mobile edge computing in order to achieve different levels of sustainable computing infrastructure. This article discusses the key technologies, advantages, industrial scenario applications of CI technology as NIB, typical use cases and development trends based on IoE, which provides directional guidance for the development of CI technology as NIB for 6G.

Real-time recognition of driver emotions can greatly improve traffic safety. With the rapid development of communication technology, it becomes possible to process large amounts of video data and identify the driver's emotions in real time. To effectively recognize driver's emotions, this paper proposes a new deep learning framework called Convolution Bidirectional Long Short-term Memory Neural Network (CBLNN). This method predicts the driver's emotion based on the geometric features extracted from facial skin information and the heart rate extracted from changes in RGB components. The facial geometry features obtained by using Convolutional Neural Network (CNN) are intermediate variables for the heart rate analysis of Bidirectional Long Short Term Memory (Bi-LSTM). Subsequently, the output of Bi-LSTM is used as input to the CNN module to extract the hear rate features. CBLNN uses Multi-modal factorized bilinear pooling (MFB) to fuse the extracted information and classifies it into five common emotions: happiness, anger, sadness, fear and neutrality. Our emotion recognition method was tested, proving that it can be used to quickly and steadily recognize emotions in real time.

This article studies the confidential transmission performance of an electric vehicle (EV) in heterogeneous network when it communicates with vehicle to grid (V2G). Based on the relay selection strategy that maximizes the signal-to-noise ratio(SNR), the electric vehicle as a legitimate user in this article uses a multi-antenna maximum ratio combining method for signal reception. Among them, a single antenna is configured for the power grid sender, relay nodes and illegal eavesdropping users. The wireless channel adopts Nakagami-m fading channel and the relay adopts decode and forward (DF) method. First, based on the stochastic geometric analysis method, statistical characteristics such as probability density function(PDF) and cumulative distribution function(CDF) of the received SNR are obtained at legitimate users and illegal eavesdropping users, respectively. Then, a functional analysis method is used to derive closed expressions for the secrecy outage probability (SOP) and non-zero security capacity probability in multieavesdropping user systems. Finally, the effects of the system's related parameters on SOP and non-zero security capacity probability are verified through simulations. The simulation results prove the correctness of the theoretical analysis, which can guarantee the privacy and security of electric vehicle users in heterogeneous network.

The new developments in mobile edge computing (MEC) and vehicle-to-everything (V2X) communications has positioned 5G and beyond in a strong position to answer the market need towards future emerging intelligent transportation systems and smart city applications. The major attractive features of V2X communication is the inherent ability to adapt to any type of network, device, or data, and to ensure robustness, resilience and reliability of the network, which is challenging to realize. In this work, we propose to drive further these features by proposing a novel robust, resilient and reliable architecture for V2X communication based on harnessing MEC and blockchain technology. A three stage computing service is proposed. Firstly, a hierarchcial computing architecture is deployed spanning over the vehicular network that constitutes cloud computing (CC), edge computing (EC), fog computing (FC) nodes. The resources and data bases can migrate from the high capacity cloud services (furthest away from the individual node of the network) to the edge (medium) and low level fog node, according to computing service requirements. Secondly, the resource allocation filters the data according to its significance, and rank the nodes according to their usability, and selects the network technology according to their physical channel characteristics. Thirdly, we propose a blockchain-based transaction service that ensures reliability. We discussed two use cases for experimental analysis, plug-in electric vehicles in smart grid scenarios, and massive IoT data services for autonomous cars. The results show that car connectivity prediction is accurate 98% of the times, where 92% more data blocks are added using micro-blockchain solution compared to the public blockchain, where it is able to reduce the time to sign and compute the proof-of-work (PoW), and deliver a low-overhead Proof-of-Stake (PoS) consensus mechanism. This approach can be considered a strong candidate architecture for future V2X, and with more general application for everything-to-everything (X2X) communications.

In the last decade, the number of vehicles worldwide has increased every year, and this growth is projected to continue unabated. Thus, the congestions, incidents, and environmental pollution which are caused by the increasing number of road vehicles and traffics have resulted in hundreds of millions of losses and become a major challenge to the sustainable development of recent human society. Both academia and industry have already reached a consensus that vehicular communication is a vital element to extend the sensing ability of vehicles for ensuring safety driving. Unfortunately, current vehicular communication cannot meet the security, reliability, and effectiveness and other needs of ITS. The industry needs a more intelligent vehicular communication to support secure and reliable transmission of data. Therefore, the research community has to focus more on enhanced and completely new communication techniques.

A large scale fast-growing data generated in intelligent transportation systems (ITS) has become a ponderous burden on the coordination of heterogeneous transportation networks, which makes the traditional cloud-centric storage architecture no longer satisfy new data analytics requirements. Meanwhile, the lack of storage trust between ITS devices and edge servers could lead to security risks in the data storage process. However, a unified data distributed storage architecture for ITS with intelligent management and trustworthiness is absent in the previous works. To address these challenges, this paper proposes a distributed trustworthy storage architecture with reinforcement learning in ITS, which also promotes edge services. We adopt an intelligent storage scheme to store data dynamically with reinforcement learning based on trustworthiness and popularity, which improves resource scheduling and storage space allocation. Besides, trapdoor hashing based identity authentication protocol is proposed to secure transportation network access. Due to the interaction between cooperative devices, our proposed trust evaluation mechanism is provided with extensibility in the various ITS. Simulation results demonstrate that our proposed distributed trustworthy storage architecture outperforms the compared ones in terms of trustworthiness and efficiency.

The vertical security problem of maglev train is challenging for nonlinearity, external disturbances, unmeasurable airgap velocity and constrained output. To solve this problem, a semi-supervised controller based on deep belief network (DBN) algorithm is proposed in the presence of unknown external disturbances. Firstly, the extended state observer (ESO) is designed to ensure fast convergence of observation errors with high enough estimation precision. An output-constrained controller is designed by backstepping method, and the estimated value of ESO is introduced to ensure that the output airgap is constrained within a bounded range. Then, the stability of this method is proved based on the symmetric Barrier Lyapunov function. Subsequently, a semi-supervised controller is presented based on DBN algorithm and the output-constrained controller. The numerical simulation results show that this method can effectively deal with unmeasurable airgap velocity and generalized external disturbances, and guarantee the vertical security with output airgap within a bounded range. Finally, experiments are implemented on a full-scale maglev vehicle and the experimental results demonstrate that the developed deep learning controller can ensure the vertical security.

In this paper, we aim to give a unified performance limit analysis of the visible light-based positioning (VLP) for a vehicular user equipment (UE), which will help to understand the essence of time-domain localization cooperation and gain insights into how to improve the performance limit of the vehicular VLP system. This is challenging due to the complex system models and the complex dependency between UE location performance and orientation performance. To achieve the above goal, we will first characterize the closed-form error bounds of the UE location and orientation at each time slot, respectively, in terms of Fisher information. Generally, the VLP error will propagate over time as the vehicular UE moves, and hence the VLP error at the current time slot is affected by the VLP performance at the previous time slot, the UE mobility and the channel quality. Based on the obtained VLP error bounds, we then reveal the impact of prior UE location knowledge, UE mobility and signal-to-noise-ratio on the VLP performance. Furthermore, the time-domain evolution of the VLP error is studied, where the convergence of the time-domain VLP error evolution is established and its closed-form stable state is quantified, which will shed light on the long-term performance of the vehicular VLP system.

In this paper, we focus on wireless sensor network-based localization for user devices (UDs). Prior to UD localization, signal propagation model (SPM) is required to calibrate using training samples from a number of location grids. However, SPM usually suffers from measurement noise and inevitable error in calibration grid (CG) locations. This will significantly degrade UD localization performance. Nevertheless, the impact of CG location error, CG layout and the number of CGs on SPM calibration performance has not been characterized. Furthermore, the effect of SPM calibration error on UD localization performance has not been developed. In this paper, we aim to provide a unified framework for performance analysis of SPM calibration and UD localization. Firstly, we establish a closed-form Cramér-Rao lower bound on SPM calibration error and UD localization error, respectively. Secondly, the impact of measurement noise, CG location error and the number of CGs on SPM calibration performance is revealed. Thirdly, the influence of SPM calibration error, CG location error and measurement noise on UD localization performance is studied. The effect of modeling mismatch is also studied. The obtained analysis framework builds a theoretical basis for the design of efficient system optimization strategies, including resource allocation and CG deployment optimization, for UD localization performance enhancement.

This paper investigates power allocation problem for secure multiple-input single-output transmission with artificial noise (AN). With an unknown eavesdropper, we propose an optimal adaptive power allocation scheme, which adaptively adjust the power allocation factor (PAF) according to the instantaneous channel state information of the legitimate channel. On this basis, we derive a closed-form expression for the optimal PAF aiming to minimize the secrecy outage probability (SOP). A suboptimal fixed power allocation scheme is also proposed to reduce system complexity. Moreover, exact closed-form expressions of SOP for both schemes are also obtained.

Multiple-mode orthogonal frequency division multiplexing with index modulation (MM-OFDM-IM) is a promising IM technique for OFDM systems to improve the spectral efficiency. In this paper, a secure index and data symbol modulation (DSM) scheme based on MM-OFDM-IM systems is proposed by employing the notion of the channel reciprocity assuming time division duplexing mode. The channel state information of the legitimate link is adopted as a secret key for protecting data transmission. We investigate randomized mapping rules for both IM and DSM, based on which even though an eavesdropper has estimated the permutations of modes and their carrying symbols, he cannot decode the message correctly. Particularly, the randomized mapping rules for IM are designed by exploiting the redundancy of mode permutations. Both numerical simulations and theoretical analysis for achievable secure rate and bit error rate are processed, whose results demonstrate that the proposed scheme can enhance the physical-layer security of the considered communication system.

In the Internet of Health Things based e-Health paradigm, a large number of computational-intensive tasks have to be offloaded from resource-limited IoHT devices to proximal powerful edge servers to reduce latency and improve energy efficiency. However, the lack of global state information (GSI), the ultra-reliable and low-latency communication (URLLC) constraints, and the adversarial competition among IoHT devices have imposed new challenges for task offloading optimization. In this paper, we formulate the task offloading problem as an adversarial multi-armed bandit (MAB) problem. In addition to the average-based performance metrics, bound violation probability of queuing delays and statistical properties of excess values are employed to characterize URLLC constraints. Then, we propose an energy-aware and URLLC-aware Task Offloading scheme based on the exponential-weight algorithm for exploration and exploitation (EXP3) named UTO-EXP3. Guaranteed performance with a bounded deviation can be achieved by UTO-EXP3 based on only local information. The effectiveness and reliability of UTO-EXP3 are validated through simulation results.

Motivated by multiple-mode orthogonal frequency division multiplexing with index modulation (MM-OFDM-IM) and multiple-input multiple-output (MIMO) technologies, a new modulation technique named multiple-mode MIMO with IM (MM-MIMO-IM) is proposed, which is implemented over the virtual parallel channels resulting from singular value decomposition (SVD) of the MIMO channel. Moreover, coordinate interleaved technique is employed to improve its diversity gain. Compared with traditional MIMO systems, the spectral efficiency (SE) of MM-MIMO-IM systems is significantly improved due to the introduction of the mode permutation. We further extend MM-MIMO-IM to in-phase/quadrature (I/Q) dimension, yielding MM-MIMO-IM-IQ, which achieves a double SE with respect to MM-MIMO-IM. Asymptotically tight upper bounds on bit error rates (BERs) of the proposed schemes are derived to evaluate their performance. Monte Carlo simulation results demonstrate the advantages of the proposed schemes.

Reliable and flexible emergency communication is a crucial challenge for search and rescue in the circumstance of disasters, specifically for the situation when base stations (BS) are no longer functioning. Unmanned aerial vehicle (UAV)aided networking is becoming a prominent solution to establish emergency networks with the underlay device-to-device (D2D), which also should be energy-efficient. In this article, we study energy-efficiency (EE) maximization for interference-aware underlay D2D-enabled UAV-aided 5G systems. All the interference scenarios are taken into account while modeling the system architecture. Afterward, we formulate an objective function to optimize EE maximization, which shows the characteristic of an NP-hard nonconvex research problem. Therefore, we transform the nonconvex problem into a convex one by reformulating the constraint functions with the cubic inequality method. Several criteria are developed to satisfy the non-negativity of the reformulating constraint. This leads the problem to be solved as a convex optimization method and results in an efficient iterative resource allocation algorithm. In each iteration, the transformed problem is solved by using Lagrangian dual decomposition with a projected gradient method. In the end, we analyze the convergence behavior of the studied algorithm and also compared it with another existing algorithm through numerical simulations.

As a new network architecture, Information-Centric Networks (ICN) has great advantages in content distribution and can better meet our needs. But it faced with many threats unavoidably. There are four types of attack in ICN: naming related attacks, routing related attacks, caching related attacks and miscellaneous attacks. These attacks will undermine the availability of ICN, the confidentiality and privacy of data. In addition, routers store a large amount of content for the users' request, and it is necessary to protect these intermediate nodes. Since the styles of content stored in nodes are not the same, using a unified set of intrusion detection rules simply will cause a large number of false positives and false negatives. Therefore, every node should perform intrusion detection according to its own characteristics. In this paper, we propose an intrusion detection mechanism to alert for abnormal packets. We introduce a extensive solution using machine learning for attacks in ICN. Moreover, the nodes in this scheme can adapt to the external environment and intelligently detect packets. Simulation on the machine learning algorithm involved prove that the algorithm is effective and suitable for network packets.

To increase network capacity and coverage, new deployment strategies are being explored for beyond-5G networks. Building on the unique features and potentials of lamp post-based access points (APs) for hotspot deployment scenarios, this work investigates the performance of three hybrid precoding techniques at the APs, where the analog RF beamformer is based on orthogonal matching pursuit (OMP) and modified generalized low rank approximation of matrices (MGLRAM) while the digital baseband precoder is based on maximum ratio transmission (MRT) and zero forcing (ZF) precoding schemes. The performance of these hybrid precoding techniques is compared to the SVD-based upper bound. Using a stadium deployment as sample use case, this work considers a multi-cell, multi-user THz Massive MIMO operation for harnessing beamforming and multiplexing gains, where the APs use hybrid precoding while users employ analog-only beamforming. The spectral efficiency results show that this deployment strategy can deliver ultra-broadband connectivity for next-generation short-range communication networks.

Traditional downlink (DL)-uplink (UL) coupled cell association scheme is suboptimal solution for user association as most of the users are associated to a high powered macro base station (MBS) compared to low powered small base station (SBS) in heterogeneous network. This brings challenges like multiple interference issues, imbalanced user traffic load which leads to a degraded throughput in HetNet. In this paper, we investigate DL-UL decoupled cell association scheme to address these challenges and formulate a sum-rate maximization problem in terms of admission control, cell association and power allocation for MBS only, coupled and decoupled HetNet. The formulated optimization problem falls into a class of mixed integer non linear programming (MINLP) problem which is NP-hard and requires an exhaustive search to find the optimal solution. However, computational complexity of the exhaustive search increases exponentially with the increase in number of users. Therefore, an outer approximation algorithm (OAA), with less complexity, is proposed as a solution to find near optimal solution. Extensive simulations work have been done to evaluate proposed algorithm. Results show effectiveness of proposed novel decoupled cell association scheme over traditional coupled cell association scheme in terms of users associated/attached, mitigating interference, traffic offloading to address traffic imbalances and sum-rate maximization.

The Internet of Vehicles has attracted a lot of attention in the automotive industry and academia recently. We are witnessing rapid advances in vehicular technologies that comprise many components, such as onboard units (OBUs) and sensors. These sensors generate a large amount of data, which can be used to inform and facilitate decision making (e.g., navigating through traffic and obstacles). One particular focus is for automotive manufacturers to enhance the communication capability of vehicles to extend their sensing range. However, the existing short-range wireless access, such as dedicated short-range communication (DSRC), and cellular communication, such as 4G, is not capable of supporting the high volume data generated by different fully connected vehicular settings. Millimeter-wave (mmWave) technology can potentially provide terabit data transfer rates among vehicles. Therefore, we present an in-depth survey of the existing research, published in the last decade, and we describe the applications of mmWave communications in vehicular communications. In particular, we focus on MAC and physical layers and discuss related issues, such as sensing-aware MAC protocol, handover algorithms, link blockage, and beamwidth size adaptation. Finally, we highlight various aspects related to smart transportation applications, and we discuss future research directions and limitations.

Edge computing provides a promising paradigm to support the implementation of Industrial Internet of Things (IIoT) by offloading computational-intensive tasks from resource-limited machine-type devices (MTDs) to powerful edge servers. However, the performance gain of edge computing may be severely compromised due to limited spectrum resources, capacity-constrained batteries, and context unawareness. In this article, we consider the optimization of channel selection that is critical for efficient and reliable task delivery. We aim at maximizing the long-term throughput subject to long-term constraints of energy budget and service reliability. We propose a learning-based channel selection framework with service reliability awareness, energy awareness, backlog awareness, and conflict awareness, by leveraging the combined power of machine learning, Lyapunov optimization, and matching theory. We provide rigorous theoretical analysis, and prove that the proposed framework can achieve guaranteed performance with a bounded deviation from the optimal performance with global state information (GSI) based on only local and causal information. Finally, simulations are conducted under both single-MTD and multi-MTD scenarios to verify the effectiveness and reliability of the proposed framework.

Internet of Things and the related computing paradigms, such as cloud computing and fog computing, provide solutions for various applications and services with massive and high-dimensional data, while producing threats to the personal privacy. Differential privacy is a promising privacy-preserving definition for various applications and is enforced by injecting random noise into each query result such that the adversary with arbitrary background knowledge cannot infer sensitive input from the noisy results. Nevertheless, existing differentially private mechanisms have poor utility and high-computation complexity on high-dimensional data because the necessary noise in queries is proportional to the size of the data domain, which is exponential to the dimensionality. To address these issues, we develop a compressed sensing mechanism (CSM) that enforces differential privacy on the basis of the compressed sensing (CS) framework while providing accurate results to linear queries. We derive the utility guarantee of CSM theoretically. An extensive experimental evaluation on real-world data sets over multiple fields demonstrates that our proposed mechanism consistently outperforms several state-of-the-art mechanisms under differential privacy.

Reconfigurable intelligent surfaces (RISs) have recently emerged as an innovative technology for improving the coverage, throughput, and energy/spectrum efficiency of future wireless communications. In this paper, we propose a new transmission protocol for wideband RIS-assisted single-input multiple-output (SIMO) orthogonal frequency division multiplexing (OFDM) communication systems, where each transmission frame is divided into multiple sub-frames to execute channel estimation simultaneously with passive beamforming. As the training symbols are discretely distributed over multiple sub-frames, the channel state information (CSI) associated with RIS cannot be estimated at once. As such, we propose a new channel estimation method to progressively estimate the associated CSI over consecutive sub-frames, based on which the passive beamforming at the RIS is fine-tuned to improve the achievable rate for data transmission. In particular, during the channel training, the RIS plays two roles of embedding training reflection states for progressive channel estimation and performing passive beamforming for data transmission on the data tones. Based on the estimated CSI in each sub-frame, we formulate an optimization problem to maximize the average achievable rate by designing the passive beamforming at the RIS, which needs to balance the received signal power over different sub-carriers and different receive antennas. As the formulated problem is non-convex and thus difficult to solve optimally, we propose two efficient algorithms to find high-quality solutions. Simulation results validate the effectiveness of the proposed channel estimation and beamforming optimization methods. It is shown that the proposed joint channel estimation and passive beamforming scheme is able to drastically improve the average achievable rate and reduce the delay for data transmission as compared to existing schemes.

Rail transit systems are important parts of modern cities such as the urban subways and intercity trains. During the travelling time, many passengers access the networks via cellular communications for working or entertainment. However, intermittent connection of LTE signals usually decreases the user experiences, especially due to the poor LTE link quality of underground subways. On the other hand, we observe that there are usually multiple cellular operators covering a city. For example, AT&T and T-Mobile in American cities, China Mobile and China Unicom in Chinese cities. Based on the different eNodeB distributions of different operators, we propose to study a new problem cross-operator switch. Unlike the handover between eNodeBs from one operator, cross-operator switch can switch to an eNodeB belonging to other operators. Although current mobile phones support dual SIM cards and Android supports the quick swap between two cards, it is still challenging to determine which eNodeB to switch because of no information exchange between different operators. To address this challenge, we propose a robust Crowdsensing based Switch (CrowdSwitch) solution dedicated for rail transit systems. CrowdSwitch utilizes mass mobile devices of passengers to sense and collect LTE signals along subway tracks, builds the signal heat map (S-Map) in the cloud servers, and finally recommends the optimal switch taking advantages of the known tracks. We implement CrowdSwitch in off-the-shelf mobile phones and conduct extensive experiments on Shanghai Metro. The results show that CrowdSwitch can depict the accurate LTE distribution and always recommend the optimal operator for users to connect.

In this article, we propose a downlink non-orthogonal multiple access (NOMA) based coordinated direct and relay system with one cell-center user and multiple cell-edge users, where a decode-and-forward (DF) relay bridges the connection between the base station and the cell-edge users. Both full-duplex (FD) and half-duplex (HD) protocols are considered for the relay. We assume that the performance of the cell-edge users is subjected to the relay, and the cancellation of the mutual interference between the relay and cell-center user is imperfect. Both the exact analytical expression of outage probability and an approximate expression of the ergodic sum rate at high signal-to-noise ratio (SNR) are derived. Numerical results demonstrate that: 1) the FD relaying NOMA system outperforms the HD relaying NOMA system at low SNR, but the situation is exactly the opposite at high SNR; 2) the mutual interference can cause a larger performance gap than the self-interference at the relay; 3) the power allocation coefficients for the cell-center user and relay can affect the performance more significantly than those for cell-edge users.11This article was presented in part at the IEEE International Workshop on Signal Processing Advances in Wireless Communications 2019 [1].

This paper proposes a Bayesian approach for angle-based hybrid beamforming and tracking that is robust to uncertain or erroneous direction-of-arrival (DOA) estimation in millimeter wave (mmWave) multiple input multiple output (MIMO) systems. Because the resolution of the phase shifters is finite and typically adjustable through a digital control, the DOA can be modeled as a discrete random variable with a prior distribution defined over a discrete set of candidate DOAs, and the variance of this distribution can be introduced to describe the level of uncertainty. The estimation problem of DOA is thereby formulated as a weighted sum of previously observed DOA values, where the weights are chosen according to a posteriori probability density function (pdf) of the DOA. To alleviate the computational complexity and cost, we present a motion trajectory-constrained a priori probability approximation method. It suggests that within a specific spatial region, a directional estimate can be close to true DOA with a high probability and sufficient to ensure trustworthiness. We show that the proposed approach has the advantage of robustness to uncertain DOA, and the beam tracking problem can be solved by incorporating the Bayesian approach with an expectation-maximization (EM) algorithm. Simulation results validate the theoretical analysis and demonstrate that the proposed solution outperforms a number of state-of-the-art benchmarks.

Due to the bursty nature of Internet traffic, network service providers (NSPs) are forced to expand their network capacity in order to meet the ever-increasing peak-time traffic demand, which is however costly and inefficient. How to shift the traffic demand from peak time to off-peak time is a challenging task for NSPs. In this paper, we study the implementation of time-dependent pricing (TDP) for bandwidth slicing in software-defined cellular networks under information asymmetry and price discrimination. Congestion prices indicating real-time congestion levels of different links are used as a signal to motivate delay-tolerant users to defer their traffic demands. We formulate the joint pricing and bandwidth demand optimization problem as a two-stage Stackelberg leader-follower game. Then, we investigate how to derive the optimal solutions under the scenarios of both complete and incomplete information. We also extend the results from the simplified case of a single congested link to the more complicated case of multiple congested links, where price discrimination is employed to dynamically adjust the price of each congested link in accordance with its real-time congestion level. Simulation results demonstrate that the proposed pricing scheme achieves superior performance in increasing the NSP's revenue and reducing the peak-to-average traffic ratio (PATR).

Degree verification is the process of verifying the academic credentials of successfully graduated students. It is a time-consuming and costly process as universities annually spend millions of dollars on handling the degree verification requests. Hence, there is a dire need to improve the degree verification process, and the Massachusetts Institute of Technology, Cambridge, MA, USA, has introduced the blockcerts, a blockchain-based solution for freely handling the degree verification requests. Although blockcerts eliminates the cost of the degree verification process, it also alters the existing workflow of degree issuance. This is because blockcerts are primarily focused on facilitating the students, and there is room for improvement from the perspective of educational institutes. In this article, we have introduced the docschain to tackle the three mentioned limitations of the blockcerts. Docschain seamlessly incorporates within the existing workflow of degree issuance by operating over the hard copies of the degree documents. This is achieved through optical character recognition (OCR), and the record of each degree document is stored along with the details of the corresponding OCR template to understand the semantics of the data stored at different sections of the degree document. In contrast to blockcerts, docschain also supports the bulk submission of degree details for both the previously and newly graduated students.

For industrial Internet of Things (IIoT), data trustworthiness should be maintained both at the time of sensing and at the time of transmission. This article is concerned with trustworthiness during transmission, which is determined by transmission reliability. We present a low-complexity and flexible method via partial and recursive superposition to improve the transmission reliability of IIoT, resulting in an IIoT with high trustworthiness. In our method, a portion of the previously transmitted data are superimposed onto the current transmitted data to introduce memory among different transmissions, which are then exploited by the windowed decoder to obtain performance gain. The proposed method is referred to as partially recursive block Markov superposition transmission of low-density parity-check (PrBMST-LDPC) codes. This article is focused on the construction of low-complexity PrBMST-LDPC codes since IIoT is resource-limited in nature. The first construction is the memory-one PrBMST-LDPC code. We present a simplified density evolution algorithm to optimize the superposition ratio for memory-one PrBMST-LDPC code. Both the analytical and numerical results show that PrBMST with memory one can be used to reduce the packet loss ratio (PLR) of IIoT using LDPC codes. Particularly, around 1.0 dB performance gain is obtained by PrBMST. We then present a low-complexity construction for PrBMST-LDPC codes with encoding memory larger than one. Simulation results show that compared with memory-one PrBMST, a further PLR reduction of around one order of magnitude can be obtained.

Despite many existing research on data forwarding in low-duty-cycle wireless sensor networks (WSNs), relatively little work has been done on energy-efficient data forwarding with probabilistic delay bounds. Probabilistic delay guarantees (i.e., delay bounded data delivery with reliability constraints) are of increasing importance for many delay-constrained applications, since deterministic delay bounds are prohibitively expensive to guarantee in WSNs. However, radio duty-cycling and unreliable wireless links pose challenges for achieving the probabilistic delay guarantee in WSNs. In this paper, we propose EEAF, a novel energy-efficient adaptive forwarding technique tailored for low-duty-cycle WSNs with unreliable wireless links. We show the existence of path diversity in low-duty-cycle WSNs, where delay-optimal routing and energy-optimal routing are likely following different paths. The key idea of EEAF is to exploit the intrinsic path diversity to provide probabilistic delay guarantees while minimizing transmission cost. In EEAF, an early arriving packet will be adaptively switched to the energy-optimal path for energy conservation. Delay quantiles are derived at each node in a distributed manner and are used as the guidelines in the adaptive forwarding decision making. Extensive testbed experiment and large-scale simulation show that EEAF effectively reduces the transmission cost by 12%~25% with probabilistic delay guarantees under various network settings. In addition, we extend the EEAF technique with data aggregation for event-based traffic scenarios. Evaluation using publicly available WSN event traffic traces yields very encouraging results with up to 40% energy saving in probabilistic delay bounded data delivery.

In this paper, a novel dual-hop spatial modulation (SM) relay network is proposed, in which the relay is enabled to transmit its own information while forwarding the SM signal from the source over the same frequency band. Both decode-and-forward (DF) and amplify-and-forward (AF) relaying protocols are studied. For DF relaying, the relay embeds its own information into one out of two spatial dimensions of quadrature SM. For AF relaying, the relay activates only one transmit antenna to forward the received signal, and encodes its own information by the index of the active antenna. Under both relaying protocols, the throughput of the network is increased without consuming extra power. The bit error rate (BER) performance is analyzed, and tight upper bounds on the BERs are derived in closed form for the source and relay over Rayleigh fading channels for both DF and AF relaying. The performance analysis is verified by Monte Carlo simulations, showing that the proposed scheme provides a simple yet effective solution to the implementation of SM relay networks in which the relay has its own information to be transmitted.

Standard blind interference alignment (sBIA) suffers from noise accumulation which severely deteriorates received signal-to-noise ratio (SNR) and significantly reduces transmission rate. A noise accumulation factor is proposed to describe the loss between the user's received SNR, and the final post processing SNR which determines the performance of the decoding of the encoded data streams (EDSs). A heterogeneous semi-BIA (H-SBIA) framework where users with different noise accumulation factors can be flexibly allocated effective EDSs (E-EDSs) is constructed. Relying on the H-SBIA framework, a heuristic H-SBIA algorithm is designed for maximizing the overall E-EDSs considering both fairness and coherence time constraints. Extensive simulations demonstrate that H-SBIA produces great fairness performance improvement at a limited cost in the achievable sum rate. The Jain's fairness index is about 2.2 times greater than that for SNR-SBIA proposed in previous work, at the cost of sacrificing 10% of the achievable sum rate.

Large-scale multi-antenna systems can effectively improve data transmission reliability and throughput for smart grid. However, the massive number of antennas and radio frequency (RF) chains also result in high complexity and energy cost. In this paper, we develop a new performance benchmark named energy economic efficiency for measuring the time-average throughput per energy cost. Then, we investigate how to maximize long-term energy economic efficiency via the joint optimization of communication and energy resource allocation. The formulated joint optimization problem is NP-hard because it not only involves long-term nonlinear optimization objective and constraints, but also involves both integer and continuous optimization variables. Next, we propose an online joint antenna selection and power control algorithm by combining nonlinear fractional programming, Lyapunov optimization, and bisection method. The proposed algorithm can achieve bounded performance deviation from the optimum performance without requiring the prior knowledge of future channel state information (CSI), energy arrival, and electricity price. Finally, a comprehensive theoretical analysis is provided, and the proposed algorithm is verified through simulations under various system configurations.

With the emergence of IoT era and the increased pressure on networks placement of small cells in cellular architecture plays a significant role in boosting the system throughput while truncating the power and energy consumption of the network. To meet the growing demand of data traffic, wireless content caching can be cost effective solution in terms of backhaul energy and capacity while providing effective solution to meet the user demand and reduce the traffic in small cell network. Energy efficiency is a major concern for sustainable development of wireless networking-based IoT. Factors affecting the energy efficiency (EE) of the network are backhaul capacity, power and energy limitation, at the macro base stations. In this paper, we have investigated a file placement strategy in a cooperate small cell network with an objective of maximizing EE. We propose an energy efficient cooperative caching scheme (EECCS) in which files are placed in cooperative small cell base station (SBS) in such a manner that small cell base station can access files from the caches of other small cell base station to maximize the energy efficiency. Numerical results show that energy efficient cooperative caching scheme provides results that validate and characterize the implementation of the proposed algorithm (EECCA).

A Bayesian approach for joint beamforming and tracking is presented, which is robust to uncertain direction-of-arrival (DOA) estimation in millimeter wave (mmWave) multiple input multiple output (MIMO) systems. The uncertain or completely unknown DOA is modeled as a discrete random variable with a priori distribution defined over a set of candidate DOAs, which describes the level of uncertainty. The estimation problem of DOA is formulated as a weighted sum of previously observed DOA values, where the weights are chosen according to a posteriori probability density function (pdf) of the DOA. In particular, we present a motion trajectory-based a priori probability approximation method, which implies a high probability to perform a directional estimate within a specific spatial region. We demonstrate that the proposed approach is robust to DOA uncertainty, and the beam tracking problem can be addressed by incorporating the Bayesian approach with an expectation-maximization (EM) algorithm. Simulation results validate the theoretical analysis and demonstrate the effectiveness of the proposed solution.

In this paper, we investigate a metamaterial- inspired small rectenna (rectifying antenna) system for microwave energy harvesting and wireless power transfer at 5.8 GHz. We also study the received-power maximization technique using an array of antennas connected to a single load by an optimal RF power combiner. This investigation is done both in simulation and experiment. The power combiner is embedded between the metamaterial- inspired antennas and the load (including rectifier) for maximizing the power, harvested by the input antennas and to deliver it to the load in the most optimal way. Moreover, we design and test proof-of-concept prototypes of the main components of the investigated energy harvesting systems. The similarity between the simulation and the experimental results also confirms our method of investigation.

In this paper, the 5G wireless communication system with hybrid energy supply is considered, where the energy arrival variations and the channel fading are of different time-scales. In such a system, there exists multi-dimension randomness caused by the variations of energy harvesting, channel fading and electricity price. Faced with this challenge, we address the two time-scale cross-layer resource allocation problem to maximize the user experience while minimizing the energy cost from a long-term perspective. The formulated problem can be decoupled into three subproblems based on Lyapunov optimization, including the energy management subproblem over the large time-scale, and the rate control subproblem as well as the joint channel and power allocation subproblem over the small time- scale. Next, these subproblems are solved by combining linear programming, convex optimization, and matching theory, without the requirement of noncausal information. Finally, simulation results demonstrate that the proposed algorithm can achieve superior performance while guarantee reliable data transmission and efficient energy utilization.

Datacenter is an irreplaceable and crucial infrastructure to power the ever-growing Internet services and applications. In response to today's application constraints (e.g., throughput, end-to-end delay, bandwidth), most datacenter networks are designed to maintain multiple parallel paths between any given pair of hosts. Consequently, multi-path routing has emerged as a technology of choice which can fully utilize the dormant path diversity. However, due to the growing heterogeneity of datacenter topologies and resource requirements, it demands intensive efforts to configure the right multi-path routing algorithms in real deployment according to the specific service targets. Such user burdens are caused by the lack of empirical knowledge about characteristics of various routing algorithms. To fill this gap, we develop a customized simulator DRE based on OMNET++ simulation environment and INET framework, and measure 5 state-of-the-art multi-path routing algorithms in datacenter covering various topologies and metrics. Apart from evaluating the standard macro metrics, we propose three new micro metrics to explore path-level characteristics, which have not been studied before. Our simulator provides a user-friendly interface for users who are interested in datacenter measurements, and the measurement results can promote future multi- path routing algorithm designs.

Surfing the Internet with mobile phones is a popular fashion to kill time in subways. However, users usually meet the intermittent connectivity caused by the high- speed movement, leading to poor user experience. We observe that almost every city is covered by multiple cellular operators. In addition, more and more mobile phones support multiple SIM cards. These motivate us to leverage multiple cellular operators together for a reliable connectivity, which is also a trend for next generation cellular network. It is challenging to build collaborations between operators, because there is no interaction between different operators and the traditional handover methods would consume much time on cross-operator cellular detection. To address these challenges, we propose a Crowdsensing based Switch (CrowdSwitch) system between multiple cellular operators in subways. CrowdSwitch uses a large number of mobile phones to measure and collect wireless signals from different locations along subway tracks, uploads them to cloud servers for analysis, and finally recommends the optimal switch strategy to users. We implement CrowdSwitch in off-the-shelf mobile phones and conduct extensive experiments on Shanghai Metro. The results show that CrowdSwitch can depict the accurate LTE distribution and recommend the optimal operator for users to connect.

With the development of smart cities, the demand for artificial intelligence (AI) based services grows exponentially. The existing works just focus on cloud- edge or edge-device cooperative AI which suffers low learning efficiency of AI, while edge-to-edge cooperative AI is still an unresolved issue. Moreover, the existing researches concentrate on the computation offloading of the AI-based task, ignoring that it is a brain-like task performing sophisticated processing to raw data, which leads to the high latency and low quality of the learning services. To address these challenges, this paper proposes an on-demand learning offloading mechanism for edge-to-edge cooperative AI. Firstly, the principle of the learning capability and its offloading are proposed for the formal description of the learning resources migration. Secondly, the proposed mechanism realizes the bilateral learning offloading utilizing edge-to-edge and cloud-edge collaborations to handle AI-based tasks with high learning efficiency and resource utilization rate. Moreover, we model the edge-to-edge learning offloading allocation based on the concatenation of deep neural network (DNN) subtasks and their heterogeneous requirement of learning resources. Simulation results indicate the rationality and efficiency of the proposed mechanism.

Spectrum use will undoubtedly move to the terahertz (THz) frequencies in the beyond fifth-generation (B5G) mobile system era. With enormous bandwidth far greater than the amount available in the microwave and millimeter-wave bands combined, THz communication will open up new frontiers for exciting services and applications requiring ultra-broadband connectivity. In this work, we evaluate the performance of a candidate B5G scenario with THz-enabled massive MIMO access points mounted on street lampposts to serve pedestrian users. Using spectral efficiency (SE) and energy efficiency (EE) as metrics, we compared the performance of three precoding schemes, namely: analog-only beamsteering, hybrid precoding with baseband zero forcing and singular value decomposition precoding as upper bound. We also show the impacts of carrier frequency, bandwidth and antenna gain on the system performance. The simulation results reveal the optimal EE and SE points which are critical design goals for the green and sustainable operation of next-generation networks.

With the wide development of smart devices, fog computing has emerged as a promising solution to accommodate the ever-increasing computational demands in Internet of things (IoT). However, there are two major obstacles hindering the wide deployment of IoT fog computing, i.e., how to realize server recruitment under information asymmetry and reliable task assignment under information uncertainty. In this article, we develop a robust two-stage task offloading algorithm by integrating contract theory with computational intelligence. In the first stage, we propose a contract based server recruitment scheme to motivate servers to share residual computational resources. In the second stage, by leveraging multi-armed bandit (MAB), we develop a reliable volatile upper confidence bound (RV-UCB) algorithm to minimize the long-term delay of task assignment, which takes into account task awareness, occurrence awareness and location awareness. Finally, a series of stimulation results are carried out to validate the performance of the proposed algorithm.

With the emergence of Internet-of-Things (IoT) and ever-increasing demand for the newly connected devices, there is a need for more effective storage and processing paradigms to cope with the data generated from these devices. In this study, we have discussed different paradigms for data processing and storage including Cloud, Fog, and Edge computing models and their suitability in integrating with the IoT. Moreover, a detailed discussion on low latency and massive connectivity requirements of future cellular networks in accordance with machine-type communication (MTC) is also presented. Furthermore, the need to bring IoT devices to Internet connectivity and a standardized protocol stack to regulate the data transmission between these devices is also addressed, while keeping in view the resource-constraint nature of IoT devices.

Vehicular fog computing (VFC) has emerged as a cost-efficient solution for task processing in vehicular networks. However, how to realize stable and reliable task offloading under information uncertainty remains a critical challenge. In this paper, we propose a matching-learning-based task offloading algorithm to address this challenge. First, a low-complexity and stable task offloading mechanism is proposed to minimize the total network delay based on the pricing-based matching. Second, we extend the work to the scenario of information uncertainty, and develop a matching-learning-based task offloading algorithm by combining matching theory and upper confidence bound (UCB) algorithm. Simulation results demonstrate that the proposed algorithm can achieve bounded deviation from the optimal performance without the global information.

Smoke detection in Internet of Things (IoT) environment is a primary component of early disaster-related event detection in smart cities. Recently, several smoke and fire detection methods are presented with reasonable accuracy and running time for normal IoT environment. However, these methods are unable to detect smoke in foggy IoT environment, which is a challenging task. In this paper, we propose an energy-efficient system based on deep convolutional neural networks for early smoke detection in both normal and foggy IoT environments. Our method takes advantage of VGG-16 architecture, considering its sensible stability between the accuracy and time efficiency for smoke detection compared to the other computationally expensive networks, such as GoogleNet and AlexNet. Experiments performed on benchmark smoke detection datasets and their results in terms of accuracy, false alarms rate, and efficiency reveal the better performance of our technique compared to state-of-the-art and verifies its applicability in smart cities for early detection of smoke in normal and foggy IoT environments.

Following the tremendous success of 2G and 3G mobile networks and the fast growth of 4G, the next generation mobile networks (5G) was proposed aiming to provide infinite networking capability to mobile users. Differentiated from 4G, benefits offered by 5G is much more than the increased maximum throughput. It aims to involve and benefit from many current technical advances, including Internet of Things (IoT). As the IoT integrates many heterogeneous networks, such as wireless sensor networks, wireless local area networks, mobile communication networks (3G/4G/LTE/5G), wireless mesh networks, and wearable health care systems, it is critical to design self-organizing and smart protocols for heterogeneous ad hoc networks in various IoT applications, such as cyber-physical systems, cloud computing for heterogeneous ad hoc networks, large-scale sensor networks, data acquisition from distributed smart devices, green communication and applications, environmental monitoring and control, etc. Moreover, based on the survey conducted by the World Health Organization, the world will lack 12.9 million health care workers by 2035. Hence, it is important to develop wearable health care systems to perform self-health monitoring. In general, wearable health care systems demands low power consumption and high measurement accuracy. Smart technologies including green electronics, green radios, fuzzy neural approaches, and intelligent signal processing techniques play important roles for the developments of the wearable health care systems. This Special Issue aims at providing a forum to discuss the recent advances on 5G and beyond mobile technologies and applications for IoT.

Nowadays, the serious security threat of industrial control system and sensors has become a major challenge with the rapid development of Industrial Internet of Things (IIoT). Man-in-the-middle (MITM) attack is a very common intrusion method, which will make a great security threat in the application of IIoT. In IIoT scenario, the lightweight safety certification can play a very important role in the development of data-intensive and decentralized applications running on billions of sensors and devices, preserving their security. Therefore, in this paper, a low-latency high-reliability security mechanism is proposed to avoid the MITM attack in IIoT scenario. First, combining the radio frequency fingerprint (RFF) technology with IIoT applications, a lightweight IIoT security architecture is proposed. Based on the proposed IIoT security architecture, the process of device access authentication and communication service is illustrated. Second, according to the requirement of IIoT identification method, an access authentication method of the device is proposed based on the RFF. The method of feature extraction, classifier designing, and the access authentication process is discussed in detail. Finally, the simulation results show that the identification rate of devices can reach 95% under SNR = 6 dB, and can nearly reach 100% under SNR = 15 dB. Through the new process of access authentication, the access authentication rate can reach 95% under SNR = 15 dB. Therefore, according to the simulation results, the new security mechanisms based on RFF can be used to avoid the MITM attack in IIoT scenario.

Millimeter-wave (mm-wave) communication has attracted increasing attention as a promising technology for 5G networks. One of the key architectural features of mm-wave is the possibility of using large antenna arrays at both the transmitter and receiver sides. Therefore, by employing directional beamforming, both mm-wave base stations (MBSs) and mm-wave user equipments (MUEs) are capable of supporting multi-beam simultaneous transmissions. However, most of the existing research results have only considered a single beam. Thus, the potentials of mm-wave have not been fully exploited yet. In this context, in order to improve the performance of short-range indoor mm-wave networks with multiple reflections, we investigate the challenges and potential solutions of downlink multi-user multi-beam transmission, which can be described as a beamspace multi-user multiple-input multiple-output (MU-MIMO) technique. We first exploit the characteristic of MBS/MUEs supporting multiple beams simultaneously to improve the efficiency of multi-user BF training. Then, we analyze the inter-user interference to avoid beam selection conflicts. Furthermore, we propose blockage control strategies and multi-user multi-beam power allocation solutions for the beamspace MU-MIMO. The theoretical and numerical results demonstrate that the beamspace MU-MIMO compared with single beam transmission can largely improve the rate performance and robustness of mm-wave networks.

Currently, blockchain technology has been widely used due to its support of transaction trust and security in next generation society. Using Internet of Things (IoT) to mine makes blockchain more ubiquitous and decentralized, which has become a main development trend of blockchain. However, the limited resources of existing IoT cannot satisfy the high requirements of on-demand energy consumption in the mining process through a decentralized way. To address this, we propose a decentralized on-demand energy supply approach based on microgrids to provide decentralized on-demand energy for mining in IoT devices. First, energy supply architecture is proposed to satisfy different energy demands of miners in response to different consensus protocols. Then, we formulate the energy allocation as a Stackelberg game and adapt backward induction to achieve an optimal profit strategy for both microgrids and miners in IoT. The simulation results show the fairness and incentive of the proposed approach.

Network coding (NC)-enabled mobile small cells are observed as a promising technology for fifth-generation (5G) networks that can cover the urban landscape by being set up on-demand at any place and at any time on any device. Nevertheless, despite the significant benefits that this technology brings to the 5G of mobile networks, major security issues arise due to the fact that NC-enabled mobile small cells are susceptible to pollution attacks; a severe security threat exploiting the inherent vulnerabilities of NC. Therefore, intrusion detection and prevention mechanisms to detect and mitigate pollution attacks are of utmost importance so that NC-enabled mobile small cells can reach their full potential. Thus, in this article, we propose for the first time, to the best of our knowledge, a novel intrusion detection and prevention scheme (IDPS) for NC-enabled mobile small cells. The proposed scheme is based on a null space-based homomorphic message authentication code (MAC) scheme that allows detection of pollution attacks and takes proper risk mitigation actions when an intrusive incident is detected. The proposed scheme has been implemented in Kodo and its performance has been evaluated in terms of computational overhead.

Characterized by the ability to handle varying number of objects, tracking by detection framework becomes increasingly popular in multiobject tracking (MOT) problem. However, the tracking performance heavily depends on the object detector. Considering that data association optimization and association affinity model are two key parts in MOT, an online multipedestrian tracking method is proposed to formulate a more effective association affinity model. It includes a two-step data association taking advantage of rank-based dynamic motion affinity model. The rank-based dynamic motion affinity model is used to estimate the object state and refine the trajectory for each of target to achieve the noiseless trajectory. Both strategies are beneficial to eliminate ambiguous detection responses during association. To fairly verify the proposed method, three public datasets are adopted. Both qualitative and quantitative experiment results demonstrate the superiorities of the proposed tracking algorithm in comparison with its counterparts.

The papers in this special section provides a forum to present recent advances on 5G mobile communications f(5G) tactile Internet. The Internet, which was created to provide resilient and interoperable communication across the globe, evolved to transport a vast amount of content with which to enrich our real-life experience. Pervasive ultra-broadband, programmable networks, and cost reduction of IT systems are paving the way to new services and commoditization of telecommunications infrastructure while lowering entry barriers for new players and giving rise to new value chains. Today, it provides a depth of information and social sophistication that rivals the real world. The Tactile Internet, the next evolutionary step, will enable remote, real-time physical interactionwith real and virtual objects, creating a two-way interactive experience in which boundaries between the real world and virtual world will blur.

Orthogonal frequency-division multiplexing with index modulation (OFDM-IM) has become a high-profile modulation scheme for the fifth-generation (5G) wireless communications and has thus been extended to multi-hop scenarios in order to improve the network coverage and energy efficiency. However, the extension of OFDM-IM to multi-relay cooperative networks is not trivial, since it is required that a complete OFDM block should be received and decoded as an entity in one node. This requirement prevents the employment of multiple relays to forward fragmented OFDM blocks on individual subcarriers. In this regard, we propose a distributed processing scheme for multi-relay assisted OFDM-IM, by which multiple relays are selected to forward signals in a per-subcarrier manner to provide optimal error performance for two-hop decode-and-forward (DF) OFDM-IM systems. Specifically, a single selected relay only needs to decode partial information carried on certain active subcarriers and forward just as for traditional OFDM systems without IM. After receiving all signals on active subcarriers forwarded by different relays, the destination can reconstruct the complete OFDM block and retrieve the full information. We analyze the average block error rate and modulation capacity of the two-hop OFDM-IM system employing the proposed distributed DF protocol and verify the analysis by numerical simulations.

Orthogonal frequency-division multiplexing with index modulation (OFDM-IM) has recently attracted many researchers' attention due to its superior spectrum efficiency and reliability compared to the traditional OFDM. Cooperative decode-and-forward relaying has been incorporated with OFDM-IM, which provides a higher energy efficiency and better network coverage. However, it might not be feasible in realistic applications, owing to the high system complexity and transmission delay rendered by complex decoding and channel estimation procedures. Therefore, in this paper, we propose a fixed-gain (FG) amplify-and-forward (AF) relay-assisted OFDM-IM system, which does not need to perform complex decoding and channel estimation at the relay, but only requires a pre-processing capability at the relay, e.g., cyclic prefix removal and re-insertion. Therefore, the system complexity can be reduced and the forwarding delay as well as power consumption caused by processing at the relay also decline. We analyze the average outage probability, block error rate, and achievable rate of the proposed system and verify all analysis by numerical results. The proposed FG AF relay-assisted OFDM-IM provides a simple solution to the implementation of OFDM-IM in new network paradigms, where nodes are simple, power limited, and/or complexity limited.

Inspired by the increasingly mature vehicle-to-everything (V2X) communication technology, we propose a multihop V2X downlink transmission system to improve users’ quality of experience (QoE) in hot spots. Specifically, we develop a cross-layer resource allocation algorithm to optimize the long-term system performance while guaranteeing the stability of data queues. Lyapunov optimization is employed to transform the long-term optimization problem into a series of instantaneous subproblems, which involves the joint optimization of rate control, power allocation, and mobile relay selection at each time slot. On one hand, the optimization of rate control is decoupled and carried out independently. On the other hand, a low-complexity pricing-based stable matching algorithm is proposed to solve the joint power allocation and mobile relay selection problem. Finally, simulation results demonstrate that the proposed algorithm can achieve superior performance and simultaneously guarantee queue stability.

Multi-Access Edge Computing (MEC) is an emerging technology that leverages computing, storage and network resources deployed at the proximity of users to offload terminal from computational- and delay-sensitive tasks. Various existing facilities including mobile devices with idle resources, vehicles, and MEC servers deployed at base stations or road side units, could act as edges in the network. Since offloading tasks incurs extra transmission energy consumption and transmission latency, two key questions to be addressed in MEC deployments are: (i) offload the workload to the edge or compute it in terminals? (ii) which edge, among the available ones, should the task be offloaded to? Hence, we propose a matching theory based task assignment mechanism which takes into account the devices' and MEC servers' computation capabilities, wireless channel conditions, and delay constraints. The main goal of our task assignment mechanism is to reduce overall energy consumption, while satisfying task owners' heterogeneous delay requirements and supporting good scalability. Simulations are conducted to evaluate the efficiency of our proposed mechanism.

Smart grid is a next generation electricity network that transmits electricity to end users and enable two way digital communication for providing remote reading and other advanced metering functions. For Smart grid domains to interact with each other, a reliable communication is required. This reliable two-way communication can be achieved with the use of small cells technology (femto, pico, etc), relay, or any of the transmission scheme proposed in 5G systems. With the use of femto cells and relay nodes in the network, the allocation of resources for the purpose of smart grid communication needs to be done efficiently. Power being one of the resource has been studied for its allocation in a femto cell network for Smart grid communication, however, transmission reliability and interference is generally ignored. In this paper, we investigate the problem of power allocation in a Neighborhood Area Network (NAN) with femto cell and relay technology as a communication mechanism. We formulate the problem as a power minimization problem such that the transmission reliability is ensured. Our performance evaluation shows that the average transmit power required for transmission through femto cell is 99% less as compared with cooperative transmission.

Flow monitoring is widely applied in softwaredefined networks for monitoring network performance. Especially, the detection on heavy hitters can prevent the Distributed Denial of Service attack. However, many existing approaches fall in one of two undesirable extremes: (i) inefficient collection where only accuracy is concerned in the method; (ii) low accuracy caused by the sacrifice with fast detection. As a result, we aim to find a balance between the accuracy and efficiency of flow monitoring, where the network resources can be saved and the error rate can also be confined simultaneously. In this paper, we present ReFeR, a novel “Report-FeedbackReport” scheme to improve the detection efficiency of heavy item detecting while ensuring low error rate of the measurement. ReFeR leverages the binary order of magnitude of item measurement to replace the long statistical information shared between switches and controller; after the items are analyzed with the magnitude, only fewer uncertain items are involved in further detection, where their information (i.e., significant digits) is provided for final judgment. Theoretical analysis and simulated evaluation have proved the effectiveness of our solution. ReFeR keeps the error rate under 1% and the saving rate larger than 20% in most cases as the selecting fraction α> 524, which guarantees both high efficiency and low error rate compared with existing methods.

Content caching at small base stations has been considered as an efficient way to alleviate the use of expensive backhauling. In this paper, we study the caching content update for a cache- enabled heterogeneous network. Through analyzing a benchmark content update policy, the problem of content consistency, which is caused by the distinct update time at each caching entity, is firstly revealed. As well, the close form representation of the consistency probability in a mobile environment is derived. Furthermore, a deterministic content update strategy is investigated, where the trade-off between consistency probability and storage cost is characterized. Detailed simulations are provided to support our analysis, as well as present the impacts of moving speed of mobile terminals and transmission bandwidth for content delivery on the consistency performance.

The next generation of mobile telephony aims to attain the increasing demands of users in terms of flexibility, bandwidth, spectral efficiency, energy efficiency, low latency, and quality of service. In this context, adaptations to the transmission system are needed in order to be able to cope with the exponential high-speed data increase with reliability, but also to cope with the expected applications, such as the Internet of Things (IoT) and tactile Internet. Researches have studied the adaptations that should be performed and one of the modifications is the physical layer used to transmit the signal. This paper aims to analyze and optimize the Bit Error Rate (BER) performance of Orthogonal Frequency Division Multiplexing (OFDM) and General Frequency Division Multiplexing (GFDM) which is a technique among the different types of modulation studied for 5G, operating in an Additive White Gaussian (AWGN) channel. This work also presents the performance of the possible received power levels according to the receiver and Ultra High Frequency (UHF) channels in the case of digital television. Fourier Transform is used as the mathematical model for performance evaluation and analyzes. The obtained results show that GFDM performs better in terms of interferences when Zero Forcing Receiver (ZFR) equalization is considered, which plays a key role in digital transmission.

LoRaWAN is a Low Power Wide Area Network (LPWAN) protocol designed to allow low power battery operated nodes to communicate with each other. Though LoRaWAN provides end-to-end security, however vulnerabilities exist in the security mechanism of LoRaWAN join procedure. A jammer can be used to launch a denial of service (DOS) attack by permanently disconnecting the LoRa end nodes from the LoRaWAN network. In this paper, we propose a novel LoRaWAN based Intrusion Detection System (LIDS) for jamming attacks. A real experimental testbed is developed and deployed and LIDS is trained on real join request data. We propose two LIDS algorithms based on Kullback Leibler Divergence (KLD) and Hamming distance (HD). The algorithms are extensively tested on realworld dataset. Receiver Operating Characteristic (ROC) based performance evaluations show that KLD and HD can achieve detection rates as high as 98% and 88% respectively with 5% false positive rate.

In this paper, we investigate how to achieve reliable content distribution in device-to-device (D2D) based cooperative vehicular networks by combining big data based vehicle trajectory prediction with coalition formation game based resource allocation. Firstly, vehicle trajectory is predicted based on global positioning system (GPS) and geographic information system (GIS) data, which is critical for finding reliable and longlasting vehicle connections. Then, the determination of content distribution groups with different lifetimes is formulated as a coalition formation game. We model the utility function based on the minimization of average network delay to guarantee the end-to-end quality of service (QoS), which is transferable to the individual payoff of each coalition member according to its contribution. The merge and split process is implemented iteratively based on preference relations, and the final partition is proved to converge to a Nash- stable equilibrium. Finally, we evaluate the proposed algorithm based on real-world map and realistic vehicular traffic.

Low-Iatency communication is crucial to satisfy the strict requirements on latency and reliability in 5G communications. In this paper, we firstly consider a contract-based vehicular fog computing resource allocation framework to minimize the intolerable delay caused by the numerous tasks on the base station during peak time. In the vehicular fog computing framework, the users tend to select nearby vehicles to process their heavy tasks to minimize delay, which relies on the participation of vehicles. Thus, it is critical to design an effective incentive mechanism to encourage vehicles to participate in resource allocation. Next, the simulation results demonstrate that the contract-based resource allocation can achieve better performance.

MAC layer adaptation is very crucial for supporting dense and diverse data requirements of sensor networks in smart cities, powered by energy harvesting. In this paper, we perform MAC layer optimization for maximizing throughput subject to application-specific needs and energy availability in Solar Energy Harvesting Wireless Sensor Networks (EH-WSNs). In contrast to previous schemes that limit energy consumption based on current availability only, we propose Energy Prediction based Energy Management algorithm (EPEM). This algorithm exploits energy prediction and sets threshold rate of energy consumption to ensure accumulation of sufficient energy for non- energy harvesting period. Our analysis shows that MAC optimization (MO) along with EPEM algorithm not only improves performance by 72% but also avoids energy scarcity during non-energy harvesting period.

In this paper, we investigate how to apply industrial unmanned aerial vehicles (UAVs) for autonomous power line inspection in smart grid from an energy efficiency perspective. Firstly, the energy consumption minimization problem is formulated as a joint optimization problem, which involves both the large-timescale optimization and the small-timescale optimization. Then, the NP-hard joint optimization problem is transformed to a two- stage optimization problem based on energy consumption magnitude and optimization timescale differences. Next, the first-stage and second-stage problems are solved by exploring dynamic programming (DP) and auction matching, respectively. Finally, the proposed algorithm is verified based on realistic power grid topology. Simulation results demonstrate that the proposed scheme achieves significant energy consumption reduction.

The increasing adoption of the Internet of Things has led to the need for systems with higher spectral and energy efficiency (EE) in order to enable communication. Larger data rate demands had led researchers to look at millimeter wave (mmWave) bands to boost network rates. This paper investigates the downlink performance of a three-tier heterogeneous network that consists of sub-6 GHz macrocells overlaid with small cells operating on both the mmWave and sub-6 GHz bands. A model is developed using tools from stochastic geometry to analyze the coverage, rate, area spectral efficiency, and EE of such a network. Various deployment strategies and their impacts on the considered metrics are studied. Simulation results are used to verify the validity of the proposed model.

Multi-beam concurrent transmission is one of promising solutions for a millimeter-wave (mmWave) network to provide seamless handover, robustness to blockage, and continuous connectivity. Nevertheless, one of the major obstacles in multi-beam concurrent transmissions is the optimization of beam pair selection, which is essential to improve the mmWave network performance. Therefore, in this paper, we propose a novel heterogeneous multi-beam cloud radio access network (HMBCRAN) architecture which provides seamless mobility and coverage for mmWave networks. We also design a novel acquirement method for candidate beam pair links (BPLs) in HMBCRANs architecture, which reduces user power consumption, signaling overhead, and overall time consumption. Based on HMBCRANs architecture and the resulted candidate BPLs for each user equipment, a beam pair selection optimization problem aiming at maximizing network sum rate is formulated. To find the solution efficiently, the considered problem is reformulated as a non-operative game with local interaction, which only needs local information exchanging among players. A decentralized algorithm based on HMBCRANs architecture and binary log-linear learning is proposed to obtain the optimal pure strategy Nash equilibrium of the proposed game, in which a concurrent multi-player selection scheme and an information exchanging protocol among players are developed to reduce the complexity and signal overheads. The stability, optimality, and complexity of the proposed algorithm are analyzed via theoretical and simulation method. The results prove that the proposed scheme has better convergence speed and sum rate against the state-of-the-art schemes.

With the increasing popularity of unmanned aerial vehicles (UAVs), it is foreseen that they will play an important role in broadening the horizon of mobile crowd sensing (MCS). Specifically, UAV-aided MCS allows autonomous data collection anytime and anywhere due to the capability of fast deployment and controllable mobility. However, the on-board battery capacity of UAVs imposes a limitation on their endurance capability and performance. In this paper, we consider the fixed-wing UAV-aided MCS system and investigate the corresponding joint route planning and task assignment problem from an energy efficiency perspective. The formulated joint optimization problem is transformed into a two-sided two-stage matching problem, in which the route planning problem is solved in the first stage based on either dynamic programming or genetic algorithms, and the task assignment problem is addressed in the second stage by exploring the Gale-Shapley algorithm. We provide a comprehensive theoretical analysis, and elaborate the procedures of practical implementation. Numerical results demonstrate that significant performance improvement can be achieved by the proposed scheme.

Following the tremendous success of 2G and 3G mobile networks and the fast growth of 4G, the next generation mobile networks (5G) was proposed aiming to provide infinite networking capability to mobile users. Differentiated from 4G, a benefit offered by 5G is much more than the increased maximum throughput. It aims to involve and benefit from many current technical advances including Industrial Internet of Things (IIoT). As the IIoT integrates many heterogeneous networks, such as Wireless Sensor Networks (WSNs), Wireless Local Area Networks (WLANs), Mobile Communication Networks (3G/4G/LTE/5G), Wireless Mesh Networks (WMNs) and wearable health care systems, it is critical to design self-organizing and smart protocols for heterogeneous ad hoc networks in various IoT applications, such as cyber-physical systems, cloud computing for heterogeneous ad hoc networks, large-scale sensor networks, data acquisition from distributed smart devices, green communication and applications, environmental monitoring and control, etc. Moreover, based on the survey conducted by the World Health Organization, the world will lack 12.9 million healthcare workers by 2035. Hence, it is important to develop wearable healthcare systems to perform self-health monitoring. In general, wearable healthcare systems demands low power consumption and high measurement accuracy. Smart technologies including green electronics, green radios, fuzzy neural approaches and intelligent signal processing techniques play important roles in the developments of the wearable healthcare systems. Therefore, this special issue provides a forum to discuss the recent advances on 5G and beyond mobile technologies and applications for IIoT.

By analogy with Internet of things, Internet of vehicles (IoV) that enables ubiquitous information exchange and content sharing among vehicles with little or no human intervention is a key enabler for the intelligent transportation industry. In this paper, we study how to combine both the physical and social layer information for realizing rapid content dissemination in device-to-device vehicle-to-vehicle (D2D-V2V)-based IoV networks. In the physical layer, headway distance of vehicles is modeled as a Wiener process, and the connection probability of D2D-V2V links is estimated by employing the Kolmogorov equation. In the social layer, the social relationship tightness that represents content selection similarities is obtained by Bayesian nonparametric learning based on real-world social big data, which are collected from the largest Chinese microblogging service Sina Weibo and the largest Chinese video-sharing site Youku. Then, a price-rising-based iterative matching algorithm is proposed to solve the formulated joint peer discovery, power control, and channel selection problem under various quality-of-service requirements. Finally, numerical results demonstrate the effectiveness and superiority of the proposed algorithm from the perspectives of weighted sum rate and matching satisfaction gains.

With the evolutionary development of automobile industry, modern transportation systems cause a series of critical problems, such as increased energy consumption and air pollution. To make green cities a reality, an ever expanding and evolving vehicular heterogeneous network infrastructure is required to enable fine-granularity data collection and reliable service delivery. In this paper, we investigate how to realize energy-efficient vehicular heterogeneous networks for green cities by exploring cooperative two-hop device-to-device-based vehicle-to-vehicle (D2D-V2V) transmission. We propose a two-stage energy-efficient resource allocation algorithm. In the first stage, an auction-matching-based joint relay selection, spectrum allocation, and power control algorithm is derived, which employs an English-auction approach for matching preference updating and conflict avoidance, and optimizes the energy efficiency of two-hop D2D-V2V and cellular links simultaneously in an iterative fashion. In the second stage, a nonlinear fractional programming based power control algorithm is developed to maximize the energy efficiency of the base station. Theoretical properties in terms of convergence, stability, and complexity are analyzed. Finally, the proposed algorithm is evaluated based on real-world road topology and realistic vehicular traffic. Numerical results demonstrate that the proposed algorithm achieves superior performance in terms of energy efficiency and network coverage compared to other heuristic algorithms.

Driven by the evolutionary development of automobile industry and cellular technologies, dependable vehicular connectivity has become essential to realize future intelligent transportation systems (ITS). In this paper, we investigate how to achieve dependable content distribution in device-to-device (D2D)-based cooperative vehicular networks by combining big data-based vehicle trajectory prediction with coalition formation game-based resource allocation. First, vehicle trajectory is predicted based on global positioning system and geographic information system data, which is critical for finding reliable and long-lasting vehicle connections. Then, the determination of content distribution groups with different lifetimes is formulated as a coalition formation game. We model the utility function based on the minimization of average network delay, which is transferable to the individual payoff of each coalition member according to its contribution. The merge and split process is implemented iteratively based on preference relations, and the final partition is proved to converge to a Nash-stable equilibrium. Finally, we evaluate the proposed algorithm based on real-world map and realistic vehicular traffic. Numerical results demonstrate that the proposed algorithm can achieve superior performance in terms of average network delay and content distribution efficiency compared with the other heuristic schemes.

The Industrial Internet of Things (IIoT) is the use of Internet of Things (IoT) technologies in manufacturing. The vehicular ad hoc networks (VANETs) are a typical application of IIoT. Benefiting from Dedicated Short‐Range Communication (DSRC) technology, vehicles can communicate with each other through wireless manner. Therefore, road safety is able to be greatly improved by the broadcast of safety messages, which contain vehicle’s real‐time speed, position, direction, etc. In existing DSRC, safety messages are broadcasted at a fixed frequency by default. However, traffic conditions are dynamic. In this way, there are too many transmission collisions when vehicles are too dense and the wireless channel is underused when vehicles are too sparse. In this paper, we address broadcast congestion issue in DSRC and propose lightweight adaptive broadcast (LAB) control for DSRC safety message. The objectives of LAB are to make full use of DSRC channel and avoid congestion. LAB meets two key challenges. First, it is hard to adopt a centralized method to control the communication parameters of distributed vehicles. Furthermore, the vehicle cannot easily acquire the channel conditions of other vehicles. To overcome these challenges, channel condition is attached with safety messages in LAB and broadcast frequency is adapted according to neighboring vehicles’ channel conditions. To evaluate the performance of LAB, we conduct extensive simulations on different roads and different vehicle densities. Performance results demonstrate that LAB effectively adjusts the broadcast frequency and controls the congestion.

The foresighted enormous increase in mobile traffic over the coming years will result in severe congestion on available radio spectrum. The use of additional spectrum in future fifth generation (5G) mobile networks will be inevitable. LTE-Unlicensed (LTE-U) is an evolving technology, which effectively utilizes available unlicensed spectrum to increase the capacity of unified 5G network. However LTE-U causes severe interference to existing WiFi networks, which needs to be addressed to take full advantage LTE-U. In this paper we thrive to provide a sub-optimal resource allocation for co-existing LTE-U and WiFi networks to maximize throughput and hence minimize the interference. We formulated an optimization problem for Joint User Association and Power Allocation for Licensed and Unlicensed Spectrum (JUAPALUS) with objective to maximize sum rate of LTE-U/WiFi heterogeneous network (HetNet) in multi-operator scenario, subject to minimum rate guarantee and co-channel interference threshold. We propose mesh adaptive direct search (MADS) algorithm as solution to optimization problem to obtain $\epsilon$-optimal results. The performance of proposed algorithm is shown in terms of network key performance indicators (KPIs) such as throughput, number of users accommodated. We also benchmark the results from MADS against outer approximation algorithm (OAA).

Cellular networks with hyper-dense deployment of small cells have been identified as the performance-optimizing architecture for fifth generation (5G) mobile systems. A thousand-fold capacity increase is projected when such networks benefit from aggressive spatial multiplexing and huge bandwidth, realizable with massive antenna arrays and millimeter-wave (mmWave) spectrum, respectively. As a precursor to the 5G projections, we show in this paper that network performance (cell capacity, user throughputs and spectral efficiency) can be significantly increased by overlaying mmWave small cells on microwave (μWave) macrocells, due to the elimination of cross-tier interference. This is without any increase in bandwidth or antenna configuration of legacy dense networks. Dramatic performance gains can further be achieved by employing more antenna arrays and larger bandwidth. In addition, we demonstrate that such networks are density-limited. Increasing the number of small cells per macrocell beyond the optimal cell density threshold leads to performance degradation. Adequate consideration should, therefore, be given to this limit in the design, planning and operation of future cellular networks.

In this paper, we present an Internet of Things (IoT)-based health care system implementation scheme using Hidden Markov Model (HMM) chain and ElectroCardioGram (ECG) sensors within the context of e-Health. The scheme aims to facilitate improved monitoring and timely intervention for Cardio Vascular Diseases (CVD) patients thereby enhancing medical services for such patients. As real-time monitoring of patients from different locations remains a critical challenge for IoT-based health care systems, this implementation employs patient path estimator, patient table and alert management schemes within the hospital to facilitate the localisation and timely intervention for the treatment of CVD patients.

Device-to-device (D2D) cooperative relay can assist users with inferior channel conditions to implement multi-hop transmissions, improving network coverage and throughput. However, energy efficiency is an important issue to be optimized because of the limited battery capacity of handheld equipments. Considering a two-hop D2D relay communication scenario, this paper proposes a resource management approach that jointly optimizes relay selection, spectrum allocation, and power control, so that the total energy efficiency of D2D links is maximized while guaranteeing the quality of service (QoS) requirements of D2D and cellular links at the same time. Since the formulated joint optimization problem involves a four-dimensional matching that is NP-hard, we propose a pricing-based two-stage matching algorithm to reduce dimensionality and provide a tractable solution. In the first stage, the spectrum resources reused by relay-to-receiver links are determined by a two-dimensional matching. Then, a three- dimensional matching is conducted to match users, relays, and the spectrum resources reused by transmitter-to-relay links. The optimal transmit power is solved during the preference establishment process in the second stage. As shown in simulation results, the proposed algorithm not only performs good on energy efficiency, but also enhances the average number of served users in comparison to the case without any relay.

Smart grid introduces a wealth of promising applications for upcoming fifth-generation mobile networks (5G), enabling households and utility companies to establish a two-way digital communications dialogue, which can benefit both of them. The utility can monitor real-time consumption of end users and take proper measures (e.g., real-time pricing) to shape their consumption profile or to plan enough supply to meet the foreseen demand. On the other hand, a smart home can receive real-time electricity prices and adjust its consumption to minimize its daily electricity expenditure, while meeting the energy need and the satisfaction level of the dwellers. Smart Home applications for smart phones are also a promising use case, where users can remotely control their appliances, while they are away at work or on their ways home. Although these emerging services can evidently boost the efficiency of the market and the satisfaction of the consumers, they may also introduce new attack surfaces making the grid vulnerable to financial losses or even physical damages. In this paper, we propose an architecture to secure smart grid communications incorporating an intrusion detection system, composed of distributed components collaborating with each other to detect price integrity or load alteration attacks in different segments of an advanced metering infrastructure.

Millimeter wave (mmWave) communications have recently attracted large research interest, since the huge available bandwidth can potentially lead to the rates of multiple gigabit per second per user. Though mmWave can be readily used in stationary scenarios, such as indoor hotspots or backhaul, it is challenging to use mmWave in mobile networks, where the transmitting/receiving nodes may be moving, channels may have a complicated structure, and the coordination among multiple nodes is difficult. To fully exploit the high potential rates of mmWave in mobile networks, lots of technical problems must be addressed. This paper presents a comprehensive survey of mmWave communications for future mobile networks (5G and beyond). We first summarize the recent channel measurement campaigns and modeling results. Then, we discuss in detail recent progresses in multiple input multiple output transceiver design for mmWave communications. After that, we provide an overview of the solution for multiple access and backhauling, followed by the analysis of coverage and connectivity. Finally, the progresses in the standardization and deployment of mmWave for mobile networks are discussed.

For the potential of providing rates of multiple Giga-bps in a single channel, millimeter wave (mmWave) communications have recently attracted substantial research interest. While mmWave technology is already being used in stationary scenarios such as indoor hotspots or backhaul, it is challenging to use mmWave frequencies in mobile networks, where transmitting/receiving nodes may be moving, channels may have a complicated structure, and the coordination among multiple nodes is difficult. To fully exploit the high potential rates of mmWave in mobile networks, many significant technical challenges must be tackled. The main objective of this IEEE JSAC Special Issue on “Millimeter wave communications for future mobile networks” is to collect the most recent technical advances in mmWave for future mobile networks. The response from the community to the call has been overwhelming. We received 96 submissions with a call period short than 4 months. Many of the submissions are from the most well known research groups in the field. After a strict review process, we decided to accept 38 papers, which will be published in two issues. The papers were selected based on the technical relevance and merits. Unfortunately, due to space limitations, a number of interesting papers were not selected, despite the merits that they had. We sincerely hope those papers can find other publishing venues.

Device-to-device (D2D) communication can effectively meet the demanding high data rate by providing direct links among mobile users in cellular networks. In this paper, we analyze the transmission capacity of relay-assisted D2D communication coexisting with cellular networks in both overlay and underlay modes. D2D users can use the delicate spectrum resources in the overlay mode, while reuse the cellular resources in the underlay mode. Based on stochastic geometry, cellular users, D2D transmitters, and relay nodes (RNs) in the networks are all modeled as Poisson point process. Then, we calculate the RN existence probability and the expectation of relay link distance to obtain the successful transmission probabilities for D2D communication. According to two relay mechanisms for enhancing D2D transmission distance, we further obtain the transmission capacities of D2D communication with the assistance of RNs in both two modes, which reflect the influence from D2D density and power. In addition, the D2D transmission capacities with variable D2D link distance are also analyzed in two modes. Simulation results verify that D2D transmission capacity can be enhanced by relay transmission and influenced by a multitude of factors, including the user density, power, the D2D link distance, and the way of using RNs.

Other than separately investing the energy efficiency (EE) merits of information-centric networking’s (ICN’s) caching and sharing (CS) mechanism in wireless communications, here we comprehensively compare the EE performances of ICN’s CS mechanism in different scenarios. A modified system model is first proposed while introducing the CS mechanism into the in-network router, base station (BS), and neighboring user sides. Afterwards, the system achievable sum rate as well as the power consumptions in wireless and wired sections is investigated. The EE performances of different scenarios are finally obtained by dividing the achievable sum rate by the consumed power. While comparing the three scenarios, numerical results demonstrate that the optimal place to cache the content is mainly determined by the distance and hub number of the core routers that passed.

Device-to-Device (D2D) communication capability within a cellular network is an integral part of the LTE-Advanced standards since 3GPP release 12. D2D communication is being targeted for traffic loading applications and proximity based services, and has the potential to enhance spectrum efficiency, overall system throughput and energy efficiency, whilst reducing the system communication delay. However, interference is still a key challenge in D2D-based communications. This will require an analytical toolkit that measure and evaluate the impact of interference on system performance, as well suggest guidelines for operators towards proper network planning. In this paper, we investigate outage probability (OP) as a key performance indicator, considering the signal-to-interference-plus-noise ratio (SINR) threshold for a D2D system. In this context, we exploit stochastic geometry, Laplace transforms, and probability density function (pdf) to calculate the outage probability in a tractable manner. The results of the mathematical analysis are verified through computer simulation.

Due to the upsurge of context-aware and proximity aware applications, device-to-device (D2D) enabled mobile cloud (MC) emerges as next step towards future 5G system. There are many applications for such MC based architecture but mobile data offloading is one of the most prominent one especially for ultra dense wireless networks. The proposed system exploits the short range links to establish a cluster based network between the nearby devices, adapts according to environment and uses various cooperation strategies to obtain efficient utilization of resources. We proposed a novel architecture of MC in which the total coverage area of a eNB is divided into several logical regions (clusters). Furthermore, UEs in the cluster are classified into Primary Cluster Head (PCH), Secondary Cluster Head (SCH) and Standard UEs (UEs). Each cluster is managed by selected PCH and SCH. An algorithm is proposed for the selection of PCH and SCH which is based on signal-to-interference-plus-noise (SINR) and residual energy of UEs. Finally each PCH and SCH distributes data in their respective regions by efficiently utilizing D2D links. Simulation results demonstrate that the proposed D2D-enabled MC based approach yields significantly better gains in terms of data rate and energy efficiency as compared to the classical cellular approach.

This paper focuses on one of the key enabling technology that will compose future 5G network, the Direct‐LTE communication underlying a cellular infrastructure, also commonly known as Device‐to‐Device (D2D). Energy efficiency algorithms are proposed for the communication between D2D users and cellular users (CUs) and, following the Lagrangian duality theory, an optimal power and rate control solution is given for D2D users, while satisfying the interference limits related to CUs. Finally, the new algorithm is used to achieve proportional fairness between D2D users and CUs and to show with numerical results that the interference to CUs can be limited to always be under a predefined threshold.

To further enhance the energy efficiency (EE) performance of fifth generation (5G) Internet of Things systems, an integrated structure is proposed in this paper. That is, other than prior studies that separately study the wireless and wired parts, the wireless and wired parts are holistically combined together to comprehensively optimize the EE of the whole system. The integrated system structure is introduced beforehand with the proposed unified control center components for better deployment of the select-and-sleep mechanism. In addition, in the wireless part, one cellular partition zooming (CPZ) mechanism is proposed. In contrast, in the wired part, a precaching mechanism is introduced. With these proposals, the proposed system EE performance is investigated. Comprehensive computer-based simulation results demonstrate that the proposed schemes display better EE performance. This is due to the fact that system power consumption is further reduced with these schemes as compared to the prior work.

In this paper, the energy-efficient resource scheduling is investigated considering quality of service (QoS) constraints for OFDMA based heterogeneous network (HetNet). The energy efficiency (EE) is measured as “throughput (bits) per Joule”, while the power consumption model includes RF transmit and circuit power (i.e. feeder loss, cite cooling, signal processing). Compared to existing literature on QoS aware method which mainly consider sum throughput or total transmission power as objective function for optimization, we investigate and model an optimization problem to maximize the EE subject to users' QoS requirements. Given QoS (data rate) requirement for maximizing EE, a constrained based optimization problem is devised. Since the optimization problem is non-convex in nature, we reconstruct the optimization problem as a convex one and devise a pragmatically efficient novel resource assignment algorithm for maximizing achievable EE with quick convergence. The considered optimization problem is transformed into a convex optimization problem by redefining the constraint using the cubic inequality satisfying certain conditions to prove non-negativity, which leads to the problem to be solve as a convex optimization method and results in an efficient iterative resource allocation algorithm. In each iteration, the transformed problem is solved by using Lagrangian dual decomposition with a projected gradient method. Analytical insights and numerical results exhibit the potency of the devised scheme for the targeted complex wireless systems. Our analysis also provides design guidelines for the future design and deployment of green wireless networks as approach the 5G era.

Information paths in current communication networks can often be modelled by set of serial links interconnected by intermediate nodes. These kind of scenarios are called line networks. If the links between nodes experience connection problems, burst erasures of information can happen. In this article, hybrid serial concatenated network codes are proposed, which consist in the serial concatenation of a 'classical' coding scheme and a network code. A novel way to decrease complexity of this approach is that the 'classical' coding scheme is not performed on a link level. The new coding schemes improve performance of the communications in line networks in terms of error- correcting capability. The evaluations involve MATLAB simulations and analytical analysis.

Traditional packet scheduling is mainly designed for increasing spectral efficiency (SE) but not for the energy efficiency (EE). Self-organized network (SON) has prospective for self- configuring, self-optimizing self-healing and minimizes the energy consumption in the network. We consider self-optimizing and self-healing property of SON and investigate a novel energy efficient scheduling algorithm for LTEA. We first compare the state the of art scheduling in view of energy efficiency, then explain the tradeoff between EE and SE. System level simulation (SLS) analysis shows that the investigated SON approach achieves notable energy gain over traditional scheduling algorithm.

The increasing energy consumption driven by striking growths in the number of users and data usage turns out the focal point for mobile operators in fulfilling requirements on cost reduction and environmental impact targets. As a step towards incorporating more energy friendly mobile platforms in future networks, 3GPP LTE-Advanced has adopted coordinated multipoint (CoMP) transmission/reception due to its ability to mitigate and/or coordinate inter-cell interference (ICI). The major CoMP techniques which already existed are joint transmission (JT) and coordinated scheduling/ beamforming (CS/CB). In this paper we propose a novel energy-efficient design (NEED) for heterogeneous network (HetNet) CoMP architecture composing both JT and CS/CB which provides a realistic trade-off for green wireless networks. A feedback based ICI coordination scheme is investigated in terms of different performance metrics (throughput; cell average and cell-edge energy efficiency; radio signaling and backhaul overhead comparison; and power consumption ratio) which eventually helps to reach a decision in favor of the proposed architecture in terms of performance trade-off.

Interference management is an important subject in Device to Device (D2D) communication when underlying a LTE-A cellular band. By default, in LTE-A there is negligible intra-cell interference due to the orthogonality of the subcarriers but this orthogonality will be lost when D2D communication takes place under cellular users (CU). Therefore, one of the key aspects of D2D communication is the set of spectrum bands in which D2D communication takes place. Hence, in this paper we will propose two novel resource allocation (RA) schemes: the first RA scheme (cell level) mitigates the interference between D2D and CU and the second RA scheme (user level or scheduling) schedules the resources in an energy efficient way between D2D and CU. These RA schemes increase the throughput and reduce the overall energy cost per bit of the system. Afterwards, these schemes are compared with the conventional methods and the simulation results show that the proposed schemes obtain higher throughput and save significant amount of energy per bit.

Coordinated Multipoint systems (CoMP) have spurred considerable research activity in recent years as they enable both capacity and coverage improvement over conventional cellular systems with open issues in the design of radio resource management algorithms for this type of systems with the challenge of multiple sub-carriers resources available in the OFDMA (orthogonal frequency division multiple access) based systems. To partially solve these issues this paper proposes a user scheduling and resource allocation algorithm for distributed broadband wireless systems based on OFDMA (orthogonal frequency division multiple access) for the 3D video streaming service. The study considers a Manhattan network which is one of the most promising scenarios for CoMP. The algorithm combines the concepts of maximum carrier-to-interference scheduling and antenna selection to increase throughput and ensure zero intra-cell interference. The results show that the use of CoMP can provide significant advantages over conventional deployments in the 3D video streaming services.

Multiuser MIMO (MU-MIMO), in which the base station transmits multiple streams to multiple users, has received significant importance as a way of achieving spectral efficiency (SE) and energy efficiency (EE). However, these two important design criteria conflict with each other and a careful study of their trade-off is mandatory for designing future wireless communication systems. This paper investigates the trade-off between EE and SE in downlink MU-MIMO system which is adopted by 3GPP LTE-Advanced to meet IMT-Advanced targets. The EE is measured as, “throughput (bits) per Joule” while both RF transmit power and circuit power consumptions are considered. In this paper, given the SE requirement, a constrained optimization problem where constraints redefined with cubic inequality is formulated to maximize EE. Then, a novel resource allocation algorithm is proposed to achieve maximum EE. Simulation results demonstrate the effectiveness of the proposed scheme and illustrate the fundamental trade-offs between energy efficient and spectral efficient transmission. Our analytical results shed light on future “green” network planning in downlink MU-MIMO systems.

In an era where spectral resources are at a premium, the underutilization of LTEA spectrum on the Uplink (UL) band due to the asymmetric nature of internet traffic is a major concern. On the other hand, Ad-hoc mode, Device to Device (D2D) or Direct-LTEA communication underlying a cellular infrastructure has recently started gaining attention as a new technology that supports machine-to-machine scenarios that can be network unassisted providing a tentative solution towards filling in the spectral gaps. Although D2D communication can share the same licensed frequency band with cellular users, it requires stringent interference management to avoid diminishing the communication quality with primary users. In this paper, we consider secondary D2D users that operate over LTEA UL bands. D2D users sense the pathloss between their location and LTEA eNB. Based on this sensing information, a novel resource allocation scheme is applied to mitigate the interference with the LTEA system. Simulation results show that there is about a 50

Human movement is strongly affected by the needs of humans to socialize or cooperate with each other, in one form or another, which results in some degree of clustering and correlation with their movements. In this paper, we exploit the movement correlations, and propose a new community-based sequential paging algorithm to reduce the paging signaling overhead. Our simulation results demonstrate that given the same location update scheme employed in the LTE-A network, the proposed algorithm is capable of reducing 20% of the paging messages in the best scenario when having high moving rate.

Coordinated MultiPoint (CoMP) transmission / reception techniques have been adopted by the 3GPP community for LTE-Advanced (Long Term Evolution-Advanced) to increase spectral efficiency and cell-edge throughput. Energy efficiency (EE) was previously ignored by most research efforts and was not considered by 3GPP as an important performance indicator until very recently. EE concerns have spurred considerable research in recent years as they enable both energy consumption and CO2 reduction over conventional cellular systems. As the “green evolution” becomes a major trend, energy-efficient transmission becomes more and more important. In this paper we investigate energy efficiency i.e., the number of corrected bits that can be transmitted throughout the channel per joule of energy consumed of different downlink CoMP techniques in LTE-Advanced. In addition, we use system level simulation to evaluate the performance.