The unmanned aerial vehicle (UAV) has been widely applied for data collection in Internet of things (IoT) networks due to its advantages of rapid deployment, flexible configuration, and high mobility. Therefore, we propose a multi-UAV cooperative path planning architecture based on machine learning algorithms. This architecture enhances the overall energy efficiency and task completion effectiveness of the data collection system by incorporating communication range constraints and co-optimizing the flight and hovering processes. Specifically, an optimization model is established with the objective of minimizing the weighted task completion time and total energy consumption, which is difficult to directly solve because of the high dimensional and strongly coupled characteristics. To deal with this problem, a multi-UAV cooperative path planning algorithm based on improved clustering and hybrid genetic algorithm (GA) and ant colony optimization (ACO) is proposed. First, the IoTDs are preliminarily clustered using the improved K-means algorithm, and the results are adaptively adjusted by incorporating the maximum UAV communication distance constraint. Second, considering the differences in data volume and priority among nodes within a cluster, a cluster head (CH) selection mechanism based on weighted normalized scoring is designed. Furthermore, the multi-UAV path planning problem is transformed into a traveling salesman problem for solution via a “flight-hover-flight” strategy. Simulation results demonstrate that, compared to traditional baseline schemes, the proposed algorithm fully leverages the positive feedback regulation of ant colony pheromones and the global search capability of the GA, achieving significant advantages in convergence speed and solution quality. Besides, the system’s comprehensive cost can be reduced by up to approximately 15%.

Deep neural networks (DNNs) have been widely applied in mobile intelligent applications. However, their high computational complexity poses significant challenges for resource-constrained mobile devices. To address this issue, this paper proposes a multi-uncrewed aerial vehicle (UAV)-assisted mobile edge computing architecture tailored for DNN inference tasks. By hierarchically partitioning the DNN model and distributing different sub-tasks between local devices and aerial servers for collaborative processing, the system effectively reduces the computational burden on user terminals. Taking into account the factors such as unbalanced network load and limited UAV energy, a task migration mechanism is introduced to support resource coordination and load balancing among multiple UAVs. The aim is to minimize total weighted energy consumption through joint optimization of user-UAV association, DNN partitioning, UAV trajectory, task migration, and computing resource allocation. Due to the dynamic and complex nature of the resulting optimization problem, we model it as a Markov decision process, and a soft actor-critic with prioritized experience replay (SAC-PER) is proposed to solve it. Furthermore, we integrate convex optimization techniques into SAC-PER as a subroutine to allocate computing resources to enhance the learning efficiency. Simulation results show that the proposed method achieves faster convergence and reduces the total weighted energy consumption by up to 14.6% compared with baseline methods.

The explosive growth of mobile data traffic has prompted operators to deploy a large number of base stations (BSs). However, due to the uneven traffic distribution, many BSs remain underutilized or idle during off-peak periods while still consuming substantial amounts of energy. To tackle this issue, we propose a Proactive Optimization-based (PO-based) BS sleeping strategy for large scale scenarios with hundreds of BSs. Specifically, by analyzing the Autocorrelation Function (ACF) of BS traffic in real-world scenarios, we identify multiple potential periods. Guided by this insight, we introduce multi-time-window mechanism and Graph Convolutional Network (GCN), designing Multi-Time-Window Spatio-Temporal Graph Convolutional Network (MTSGCN) to effectively capture the complex spatio-temporal dependencies present large-scale settings. The forecasted results acquired by MTSGCN serve as inputs to a multiple-BSs cooperative sleeping problem with the objective to minimize the total energy consumption. To tackle this huge problem efficiently, we first use K-means++ to divide the large region into several small cooperative clusters and then adopt the Integral Linear Programming (ILP) algorithm to solve each subproblem. Experimental results demonstrate that MTSGCN reduce the forecasting error by 10.9% compared with the state-of-the-art methods. Furthermore, the proposed MTSGCN-ILP algorithm achieves over 20% energy savings gains compared to the other typical strategies.

With the emergence of fluid antenna (FA) in wireless communications, the capability to dynamically adjust port positions offers substantial benefits in spatial diversity and spectrum efficiency, which are particularly valuable for mobile edge computing (MEC) systems. Therefore, we propose an FA-assisted MEC offloading framework to minimize system delay. This framework faces two severe challenges, which are the complexity of channel estimation due to dynamic port configuration and the inherent non-convexity of the joint optimization problem. Firstly, we propose Information Bottleneck Metric-enhanced Channel Compressed Sensing (IBM-CCS), which advances FA channel estimation by integrating information relevance into the sensing process and capturing key features of FA channels effectively. Secondly, to address the non-convex and high-dimensional optimization problem in FA-assisted MEC systems, which includes FA port selection, beamforming, power control, and resource allocation, we propose a game theory-assisted Hierarchical Twin-Dueling Multi-agent Algorithm (HiTDMA) based offloading scheme, where the hierarchical structure effectively decouples and coordinates the optimization tasks between the user side and the base station side. Crucially, the game theory effectively reduces the dimensionality of power control variables, allowing deep reinforcement learning (DRL) agents to achieve improved optimization efficiency. Numerical results confirm that the proposed scheme significantly reduces system delay and enhances offloading performance, outperforming benchmarks. Additionally, the IBM-CCS channel estimation demonstrates superior accuracy and robustness under varying port densities, contributing to efficient communication under imperfect CSI.

Edge Intelligence (EI) combines edge computing and artificial intelligence to deliver low-latency and resource-efficient services. Integrated Sensing and Communication (ISAC) further empowers EI by enhancing edge perception and accelerating intelligent model training. However, integrating ISAC into EI complicates the coordination of dynamically varying sensing, communication, and computation resources, especially under device mobility and unpredictable network conditions, leading to degraded service performance. To address these coordination challenges and sustain high-quality service under mobility and dynamics, we aim to design an adaptive service provision framework that tightly couples real-time perception with intelligent decision-making at the edge. Specifically, we propose an adaptive service provision architecture for ISAC-assisted EI, where Digital Twins (DTs) hosted on edge servers represent edge devices and their contexts to enable accurate perception and intelligent decision-making, thereby enhancing the efficiency of ISAC-enabled services. By dynamically migrating DTs across edge servers based on device mobility and resource availability, the system supports continuous decision-making and seamless service delivery. We further integrate convex optimization for efficient multi-resource coordination and a Time-Varying Contextual Bandit (TVCB) algorithm to enable adaptive, context-aware DT migration in dynamic environments. Extensive simulations demonstrate that our approach significantly improves service quality, reliability, and adaptability in ISAC-assisted EI systems, reducing migration oscillations and overhead while achieving lower latency and higher utility compared with representative baselines.

Through conducting equivalent model training within the paradigm of edge intelligence, the Digital Twin Edge Networks (DITEN) have been widely employed in the Industrial Internet-of-Things (IIoT) to facilitate the cost-effective execution without the operational disruption. However, due to the insufficient consideration of heterogeneity in computing and communication capabilities of distinct industrial terminals in the Digital Twin (DT) model training, the existing approaches of DT construction/update have unbalanced model training cost and loss in the whole life cycle of DT model, hindering the abilities of quick responding to complex and dynamic productions and ensuring the data consistency of virtual-real space. To address this issue, we define a global loss minimization problem with constraint, and propose an original approach of semi-asynchronous federated learning, named EDT-SaFL, as a promising solution. Considering the collaborative utilization of heterogeneous resources, and the contribution of local data quantity and quality to the global model update, the EDT-SaFL consists of three important operations,Terminal Selection for Model Training,Self-Adaptation of Local Training Iterations, andSemi-asynchronous Global Aggregation. With the analysis of convergence, complexity and communication overhead, the experiments have evidently demonstrated the superiority of EDT-SaFL on the datasets of CIFAR-10 and Industrial-Equipment.

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.

Reconfigurable intelligent surface (RIS) has emerged as a pivotal technology for enhancing wireless networks. Compared to terrestrial RIS deployed on building facades, aerial RIS (ARIS) mounted on quadrotor unmanned aerial vehicle (UAV) offers superior flexibility and extended coverage. However, the inevitable tilt and altitude variations of a quadrotor UAV during flight may lead to severe beam misalignment, significantly degrading ARIS’s performance. To address this challenge, we propose an Euler angles-based ARIS control scheme that jointly optimizes the altitude and trajectory of the ARIS by leveraging the UAV’s dynamic model. Considering the constraints on ARIS flight energy consumption, flight safety, and the transmission power of a base station (BS), we jointly design the ARIS’s altitude, trajectory, phase shifts, and BS beamforming to maximize the system sum-rate. Due to the continuous control nature of ARIS flight and the strong coupling among variables, we formulate the problem as a Markov decision process and adopt a soft actor-critic algorithm with prioritized experience replay to learn efficient ARIS control policies. Based on the optimized ARIS configuration, we further employ the water-filling and bisection method to efficiently determine the optimal BS beamforming. Numerical results demonstrate that the proposed algorithm significantly outperforms benchmarks in both convergence and communication performance, achieving approximately 14.4% improvement in sum-rate. Moreover, in comparison to the fixed-horizontal ARIS scheme, the proposed scheme yields more adaptive trajectories and significantly mitigates performance degradation caused by ARIS tilting, demonstrating strong potential for practical ARIS deployment.

As the sixth generation wireless technology evolves, applications such as, holography, autonomous driving, and telemedicine require enhanced data rates, reliability, and spectral efficiency. Unmanned Aerial Vehicles (UAVs) have gained attention due to their flexible deployment, line-of-sight transmission, and dynamic adaptability. However, UAV-assisted communication encounters challenges stemming from UAV position deviations caused by environmental factors such as wind and turbulence, which degrade transmission reliability. To address these problems, we propose a Non-Orthogonal Multiple Access-based full-duplex UAV relay protocol to improve the system transmission rate. The protocol utilizes successive interference cancellation for signal separation and maximal ratio combining for signal enhancement. Considering UAV position uncertainty, we formulate a robust optimization problem for joint UAV position optimization and power allocation. By employing the Bernstein-type inequality, we transform the probabilistic constraints into the deterministic constraints and solve the problem using a block coordinate descent-based algorithm. Simulation results demonstrate that, compared to the benchmark schemes, the proposed strategy improves system throughput and exhibits enhanced robustness, particularly under significant UAV position deviations.

Movable antennas (MAs) and reconfigurable intelligent surfaces (RISs) have emerged as two promising technologies for enhancing wireless communication performance, owing to their capability to dynamically reshape and manipulate the propagation environment. Motivated by this potential, this paper investigates the joint utilization of the additional degrees of freedom introduced by MAs (through antenna repositioning) and RIS (via optimized reflection) to effectively mitigate computation distortion in over-the-air computation (AirComp) systems. Specifically, we formulate an optimization problem aimed at minimizing the mean square error (MSE) between the target function values and their estimates, through jointly optimizing the receive beamformer at the access point, RIS reflection phase shifts, and transmit coefficients as well as antenna positions of AirComp users. To address the non-convex nature of the formulated problem, we develop a computationally efficient algorithm capitalizing alternating optimization technique, the penalty-dual decomposition method, and the particle swarm optimization enhanced by a dynamic neighborhood pruning mechanism. Next, we further extend the optimization framework to a more practical case with discrete MA positions. Extensive simulation results demonstrate that the joint optimization of RIS beamforming and MA positioning substantially reduces the computation MSE, compared to the separate MA-enhanced AirComp and RIS-aided AirComp schemes. Moreover, the proposed algorithm achieves comparable performance to the penalty function-based method, while incurring significantly lower computational complexity.

The joint design of sensing, communication, computing, and control (SC3) is crucial for supporting environment-aware Industrial Internet of Things (IIoT) applications. Considering the uncontrollable wireless propagation environments and limited spectrum resources, wireless communication performance often becomes the primary design bottleneck for such an integrated system. To address this challenge, this paper presents a design framework for reconfigurable intelligent surface (RIS)-enhanced semantic-aware SC3networks, where RIS and semantic communication technologies are employed to improve wireless communication efficiency. To facilitate real-time closed-loop control, we further formulate a weighted sum execution latency minimization problem, while imposing constraints on maximum execution latency and energy consumption of individual IoT device, as well as minimum information entropy to meet specific control requirements measured by linear quadratic regulator cost. In addition, the design framework aims at optimizing bandwidth allocation, RIS phase shift matrix, time scheduling, transmit power, and CPU-cycle frequency for IoT devices and the base station (BS). To handle the coupled multi-dimensional optimization variables, the block coordinate descent method is utilized to decompose the formulated problem into more tractable subproblems, which are then solved using a penalty-function-based approach and geometric programming technique. Simulation results demonstrate the performance advantages achieved by our proposed method compared to several benchmark approaches. Additionally, we explore the impact of various parameters on SC3systems, offering deeper insights and meaningful research observations.

The rapid adoption of 5G technology and Internet of things (IoT) devices has fueled significant growth in intelligent applications, increasing their complexity beyond simple task definitions. Scheduling intelligent applications modeled as directed acyclic graphs (DAGs) has thus emerged as a crucial challenge. Our proposed solution is a deep reinforcement learning (DRL) framework that uniquely integrates proximal policy optimization (PPO) with a transformer-based module for scheduling DAG applications. Unlike other approaches that rely on predefined priorities or static optimization algorithms, our approach enables agents to autonomously explore task execution orders and dynamically adapt to changing network resource conditions, learning optimal scheduling strategies. The algorithm leverages transformers to handle complex task dependencies, minimizing application duration and user energy consumption by jointly optimizing application processing order, task priorities, transmit power, offloading decisions, and computational frequency. Through a series of simulations, we prove the effectiveness of the proposed algorithm and demonstrate the performance comparison under different settings, providing a more flexible and robust solution for DAG scheduling in edge networks.

As a cornerstone for enabling intelligent transportation systems, traffic flow prediction provides a technical basis for urban planning, traffic management and travel management. Federated learning (FL) is frequently employed in traffic flow prediction to meet privacy requirements. However, the traditional FL paradigm is subject to several limitations, including data heterogeneity, unbalanced data volume, and limited available resources. To address these problems, we propose a three-tiered hierarchical federated meta-learning framework. Firstly, the client will use a soft gap sampler (SGS) to sample personalized parameters, thereby alleviating bandwidth pressure and obtaining the gradients required for backpropagation of the global model on the query set. Secondly, these parameters and gradients are respectively used in the regional server and the global server to obtain regional parameters with regional awareness ability and global parameters with learning ability. Finally, the regional server will fuse the two parameters for the initial parameters in the prediction stage. A large number of experiments prove that the proposed framework can cope with the integration of cross-regional models, the adaptation of models in data-scarce regions, and different bandwidth limitations of clients.

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.

This paper tackles the challenges of high energy consumption, limited computational resources, and communication delays in Federated Learning (FL) for multi-UAV communication systems. We propose an innovative FL-based object detection training framework designed for UAV applications. The framework first introduces a lightweight modification to the YOLOv12 model, significantly reducing its parameter count and computational complexity, which enables efficient model training without compromising detection performance. Furthermore, the framework employs a joint optimization strategy for local computation and communication, thereby effectively reducing energy consumption and overall training time. Experimental results on the VisDrone2021 dataset demonstrate that, while maintaining a detection accuracy of 76.7% mAP, the model reduces its parameter count by 78.9% compared to the original YOLOv12 and lowers the global training cost by 19.58%, achieving an optimal balance between accuracy, latency, and energy efficiency.

Accompanied by the booming development of artificial intelligence technology, deep learning has been widely used in many fields of cancer, specially in cancer prediction. The dependence on training data for deep learning naturally raises privacy leakage concerns. Federated learning is a promising solution to these issues, but it is limited by the computational capacity of clients. Therefore, this paper has proposed a Split Federated Learning (SFL)-based breast cancer prediction scheme with dynamic client selection aggregation, by aggregating data from multiple healthcare organizations under the premise of privacy protection. First, the split learning is integrated into federated learning to predict breast canner, which contributes to protecting data privacy and reducing the computational burden on client devices. Then, the dynamic client selection aggregation is devised to lower the aggregation communication costs and improve communication efficiency by utilizing Long Short-Term Memory (LSTM) networks to evaluate the availability of each client devices in participating training. Finally, we have conducted extensive experiments on the CAMELYON16 dataset to evaluate the performance of our proposed scheme, and the experimental results have shown that our proposed scheme can converge faster and achieve the lower communication costs.

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.

Federated learning (FL) is a pivotal paradigm for decentralized model training while preserving data privacy. However, data heterogeneity among clients significantly degrades model performance and convergence efficiency. In response, we introduce a federated knowledge distillation mechanism, FedEXD, that addresses robustness and convergence in diverse client environments through a self-propelled learning architecture. FedEXD employs a novel density ratio-based data extraction algorithm, leveraging KLIEP to select representative data, enhancing global knowledge synthesis and local model adaptability while preserving privacy. Extensive evaluations on benchmark datasets demonstrate FedEXD's substantial improvements in efficiency and accuracy, demonstrating a substantial 1.51% accuracy improvement over state-of-the-art methods under firm heterogeneity while reducing communication rounds by over 46.3%. These findings underscore FedEXD's potential to advance FL systems' generalizability across complex, non-IID data distributions, offering a scalable solution for privacy-conscious, high-performance distributed learning.

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.

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.

Effective power line inspection is crucial for power grid maintenance and management. To address the issue of signal loss or signal weakness in remote suburbs and deep mountains, we presented a UAV patrolling system with hybrid communication modules and edge detection capabilities. This system first features the communication ability in different scenarios including the extreme signal loss case by incorporating the BeiDou Navigation Satellite System, short-message communication, the Internet of Things, and edge computing. Next, to accurately and timely detect the power line faults, such as exposed wire, thatch covering, and lead stem falling, under various conditions, an edge detection model employing YOLOv8 is proposed without the help of cloud centers and public communication networks. Finally, experiments are designed on our built UAV patrolling test bed with different use cases. Numerical results show that our proposed scheme could efficiently inspect the power line, especially in signal loss or weak areas, and have a high precision and low latency for line fault detection, compared to the YOLOv8 baseline algorithm.

With the rapid progress made in aerial computing technology and the increased popularity of fifth-generation (5G) networks, uncrewed aerial vehicles (UAVs) have been playing a crucial role in real-time data collection, processing, and transmission. However, due to the diversity in traffic generated by UAVs in various mission scenarios, there is a significant challenge posed in traffic classification. Therefore, a novel traffic classification model is proposed in this article on the basis of the spatial attention-enhanced convolutional neural network (SAE-CNN). This model proves effective in improving classification accuracy and latency, particularly in the context of various 5G services, such as enhanced mobile broadband (eMBB), ultrareliable low-latency communication (URLLC), and Internet service. Also, a 5G heterogeneous network platform is built to collect UAV-related aerial computing data, with extensive experiments performed to verify the superior performance of the SAE-CNN model compared to other state-of-the-art methods. The experimental results demonstrate that the proposed approach enables effective traffic management and classification for the application of UAV in complex 5G environments.

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.

The implementation of the Internet of Things (IoT) technology has the potential to unleash the capabilities of generative artificial intelligence (GAI). However, integrating GAI with IoT introduces a significant challenge in managing the limited resources of edge networks. In this article, we propose a resource optimization framework for GAI in IoT systems to address this issue, leveraging a heterogeneous computing framework. We focus on the system utility maximization problem, which jointly optimizes transmit power, heterogeneous computing allocation, CPU-cycle frequency, GPU-cycle frequency, and task scheduling under the latency constraint. The optimal CPU-cycle frequency, GPU-cycle frequency, and computing allocation are obtained by employing data parallelism analysis. In particular, we develop a hierarchical soft actor-critic with an intrinsic curiosity (HSAC-IC) algorithm to determine the task scheduling strategy. The HSAC-IC algorithm utilizes a hierarchical strategy structure and an intrinsic curiosity module (ICM) to improve learning efficiency and performance, particularly in environments characterized by sparse rewards, high-dimensional action spaces, and complex tasks. Our simulations benchmark the HSAC-IC algorithm against two existing deep reinforcement learning (DRL) algorithms and three reference schemes. The results illustrate that our scheme significantly outperforms these alternatives, ensuring AIGC user service requirements, while minimizing service generation costs, and optimizing resource allocation by configuring the image quality strategy on edge servers.

In this article, we consider deploying multiple unmanned aerial vehicles (UAVs) to enhance the computation service of mobile edge computing (MEC) through collaborative computation among UAVs. In particular, the tasks of different types and service requirements in MEC network are offloaded from one UAV to another. To pursue the goal of low-carbon edge computing, we study the problem of minimizing system energy consumption by jointly optimizing computation resource allocation, task scheduling, service placement, and UAV trajectories. Considering the inherent unpredictability associated with task generation and the dynamic nature of wireless fading channels, addressing this problem presents a significant challenge. To overcome this issue, we reformulate the complicated nonconvex problem as a Markov decision process and propose a soft actor-critic-based trajectory optimization and resource allocation algorithm to implement a flexible learning strategy. Numerical results illustrate that within a multi-UAV-enabled MEC network, the proposed algorithm effectively reduces the system energy consumption in heterogeneous tasks and services scenarios compared to other baseline solutions.

This article investigates a joint optimization framework for intelligent reflecting surface (IRS)-enhanced integrated sensing, communication, and powering systems. In this framework, the base station transmits signals for simultaneous radar sensing, as well as multi-user information and power transmissions. We aim at maximizing the minimum harvested power among all users, while satisfying beampattern gain requirements for multi-target sensing and signal-to-interference-plus-noise constraints of users. To tackle this strictly non-convex problem, we employ the block coordinate descent technique to iteratively optimize the transmit beamformer of the base station, the phase shift matrix of the IRS, and the power splitting ratios of users. The semi-definite relaxation method is utilized to obtain the optimal transmit beamformer of the base station, and the tightness of the rank-one relaxation is demonstrated. Furthermore, we develop a penalty function-based algorithm and use successive convex approximation techniques to determine the optimal phase shift matrix of the IRS. Additionally, closed-form expressions are derived for the optimal power splitting ratios. Moreover, by exploiting the Bernstein-type inequality, we further designed the robust beamforming and power splitting scheme for considered systems under stochastic channel estimation errors. Numerical results demonstrate that the proposed IRS-enhanced method outperforms several benchmark methods in terms of the minimum harvested power among all users.

As a cutting-edge technology of low-altitude Artificial Intelligence of Things (AIoT), autonomous aerial vehicle object detection significantly enhances the surveillance services capabilities of low-altitude AIoT. However, the difficulty of object detection is exacerbated by the high proportion of small and obscure objects in the captured images. To address the mentioned challenges, we present an efficient multiband infrared small object detection approach for low-altitude intelligent surveillance services. First, we propose the multiband infrared image fusion algorithm based on cascade-GAN (MIF-CGAN), which produces fused images with high information entropy and high contrast. Then, the Transformer-based multiscale dense small object detection (MsDSOD) algorithm is proposed. The algorithm consists of the global-local object detection (G-LOD) network, the object dense area extraction (O-DAE) module, and the weighted boxes fusion (WBF) module. It extracts small objects features at different scales from infrared images and fuses the global and local detection results to accurately identify small objects in dense scenes. Furthermore, compared to the traditional algorithms, the mean average precision (mAP) of MsDSOD is improved by 0.80% and the average precision in small object detection$({\mathrm { AP}}_{s})$is improved by 0.72%. The proposed algorithm is optimally suited to deal with complex scenes with dense small objects and background occlusion.

Vehicular edge intelligence (VEI) is a promising paradigm for enabling future intelligent transportation systems by accommodating artificial intelligence (AI) at the vehicular edge computing (VEC) system. Federated learning (FL) stands as one of the fundamental technologies facilitating collaborative model training locally and aggregation, while safeguarding the privacy of vehicle data in VEI. However, traditional FL faces challenges in adapting to vehicle heterogeneity, training large models on resource-constrained vehicles, and remaining susceptible to model weight privacy leakage. Meanwhile, split learning (SL) is proposed as a promising collaborative learning framework which can mitigate the risk of model wights leakage, and release the training workload on vehicles. SL sequentially trains a model between a vehicle and an edge-cloud (EC) by dividing the entire model into a vehicle-side model and an EC-side model at a given cut layer. In this work, we combine the advantages of SL and FL to develop an adaptive split FL scheme for VEC (ASFV). The ASFV scheme adaptively splits the model and parallelizes the training process, taking into account mobile vehicle selection and resource allocation. Our extensive simulations, conducted on nonindependent and identically distributed data, demonstrate that the proposed ASFV solution significantly reduces training latency compared to existing benchmarks, while adapting to network dynamics and vehicles’ mobility.

As the percentage of renewable energy in the Energy Internet (EI) gradually increases, how to deal with the uncertainty of renewable energy in the economic dispatch problem (EDP) becomes an important issue. This paper proposes a digital twin (DT) assisted economic dispatch strategy for EI with information entropy. First, we leverage the storage capacity of the DT and an extensive historical data set to provide a theoretical framework for quantifying uncertainty of renewable energy. Second, a renewable energy cost function based on the maximum entropy principle, confidence interval, and penalty factor is proposed to model the renewable energy resources considering the uncertainty. Further, we design a fully distributed Newton-surplus-based optimization algorithm. This algorithm achieves fast second-order convergence to ensure the real-time performance of the DT-assisted economic dispatch framework and overcome the asymmetry caused by the directed communication network. In addition, we give theoretical proof that the Newton-surplus-based algorithm can converge to the global optimal point. Finally, simulations validate the effectiveness of the proposed algorithm.Note to Practitioners—The essence of EDP is to minimize the total costs through optimal resource allocation while ensuring compliance with all operational constraints. With the increasing penetration of renewable energy resources, their strong stochasticity and uncertainty pose challenges to achieve reliable dispatch strategy. To address this issue, this paper presents the DT-assisted economic dispatch framework, model, and method to quantify the uncertainty of renewable energy resources and achieve distributed economic dispatch with fast convergence speed for EI. Our research is beneficial for practitioners to understand how to use the DT and information entropy to deal with the uncertain of renewable energy resources. The theory and simulation results demonstrate the correctness and effectiveness of the proposed method.

In this paper, we propose a multi-unmanned aerial vehicle (UAV)-assisted integrated sensing, communication, and computation network. Specifically, the treble-functional UAVs are capable of offering communication and edge computing services to mobile users (MUs) in proximity, alongside their target sensing capabilities by using multi-input multi-output arrays. For the purpose of enhance the computation efficiency, we consider task compression, where each MU can partially compress their offloaded data prior to transmission to trim its size. The objective is to minimize the weighted energy consumption by jointly optimizing the transmit beamforming, the UAVs’ trajectories, the compression and offloading partition, the computation resource allocation, while fulfilling the causal-effect correlation between communication and computation as well as adhering to the constraints on sensing quality. To tackle it, we first reformulate the original problem as a multi-agent Markov decision process (MDP), which involves heterogeneous agents to decompose the large state spaces and action spaces of MDP. Then, we propose a multi-agent proximal policy optimization algorithm with attention mechanism to handle the decision-making problem. Simulation results validate the significant effectiveness of the proposed method in reducing energy consumption. Moreover, it demonstrates superior performance compared to the baselines in relation to resource utilization and convergence speed.

This paper studies the application of cognitive radio inspired non-orthogonal multiple access (CR-NOMA) to reduce age of information (AoI) for uplink transmission. In particular, a time division multiple access (TDMA) based legacy network is considered, where each user is allocated with a dedicated time slot to transmit its status update information. The CR-NOMA is implemented as an add-on to the TDMA legacy network, which enables each user to have more opportunities to transmit by sharing other user’s time slots. A rigorous analytical framework is developed to obtain the expressions for AoIs achieved by CR-NOMA with and without re-transmission, by taking the randomness of the status update generating process into consideration. Numerical results are presented to verify the accuracy of the developed analysis. It is shown that the AoI can be significantly reduced by applying CR-NOMA compared to TDMA.Moreover, the use of re-transmission is helpful to reduce AoI, especially when the status arrival rate is low.

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.

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.

Federated Learning (FL) has made remarkable achievements in medical and e-healthcare services. Different healthcare institutions can jointly train models to facilitate intelligent diagnosis. However, the model gradients transmitted among these institutions may still leak private information about the local models and training datasets. Additionally, in the current FL schemes, institutions with different quantities or qualities of medical data usually get the same training models, which may significantly hamper their motivation. Therefore, ensuring privacy and fairness in collaborative training remains a challenge. To address this issue, we propose a privacy-enhanced and fair FL scheme (EPFFL) to support distributed large-scale data sharing of e-healthcare services. In the training process, participants upload the encrypted model gradients according to their sharing wishes to the blockchain while storing their training data locally. Hence, the FL initiator can only get the aggregated gradients from the blockchain rather than the local data of other participants. Moreover, EPFFL ensures fairness by evaluating the participants’ contributions, i.e., participants with different data qualities and sharing levels can obtain the final models with different accuracies at the end of the training. Through theoretical and simulation analysis, the scheme shows superior functionalities on privacy preservation and fairness with the ideal model accuracy.

The evolution of intelligent transportation systems necessitate the integration of advanced technologies to address challenges in data processing, real-time decision-making, and network coverage. In this paper, we present a novel intelligent transportation system architecture that synergizes Unmanned Aerial Vehicles (UAVs), Digital Twins (DTs), and an Integrated Sensing, Communication, and Computation (ISCC) framework. In this system, UAVs equipped with edge computing capabilities collaborate with the ground-based cloud center to process data collected by perception devices (PDs). Each UAV and PD is mirrored by a Digital Twin (DT) at the base station, enabling real-time monitoring and predictive analytics. We intend to maximize the network lifetime and minimize the economic overhead, which is achieved through the joint optimization of the association policies of UEs, input data caching decisions, UAVs’ flight trajectory and speed, task processing mode selection and data unload proportion allocation under joint processing. To tackle the inherent challenges of nonlinearity, dynamic network conditions, and heterogeneous data sources, the problem is modeled as a Markov Decision Process (MDP) and solved using an enhanced Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, adapted to handle both continuous and discrete action spaces. The experimental results show that compared to the baseline algorithm, this approach not only exhibits faster convergence but also achieves outstanding performance in maximizing system utility for intelligent transportation systems.

Internet of Vehicles (IoV) supported by terrestrial networks can satisfy the necessities of multiple computation-intensive applications. However, current terrestrial networks and resource management mechanisms may only partially guarantee vehicle and in-vehicle user equipment (VUE)’s quality of service due to the limited coverage of roadside units (RSU), especially in remote areas. This paper investigates vehicular edge computing (VEC) in satellite-terrestrial integrated networks with multiple low-earth orbit (LEO) satellites, ground RSUs, and VUEs. In remote areas without RSU coverage, VUEs can offload their partial tasks to satellites to save energy and guarantee latency. We aim to minimize VUEs’ weighted sum energy consumption by jointly optimizing VUEs’ association, data partition, computing resource allocation, power control, and bandwidth assignment under the constraints of maximum tolerant latency, maximum number of outage time slots, computation capacity at each satellite and each RSU, and maximum allowable transmission power at VUEs. Furthermore, we introduce an iterative algorithm by decomposing the original non-convex problem into several sub-problems. We efficiently solve each sub-problem by utilizing variable substitutions, the difference of convex functions algorithms, the Lagrangian dual method, and the Karush-Kuhn-Tucke conditions. Simulation results show that the introduced satellite-terrestrial integrated networks-enabled VEC scheme significantly reduces VUEs’ energy consumption compared to other schemes.

Benefiting from the outstanding advantages in speeding up task processing and saving energy consumption, vehicular edge computing has entered a period of rapid development. Given the sharp increase in application services, it is vital to fully utilize all available computation resources to guarantee personalized requirements from different users. Specially, a lot of idle vehicle resources can be exploited for task execution to improve the service experience. On the other hand, most works focus on the system performance and fail to guarantee diversified user demands. To this end, we propose a novel distributed collaborative computing scheme for task completion rate maximization (TCRM) in vehicular networks by taking into account both vertical and horizontal collaboration. The novelty of horizontal collaboration lies in the full use of available one-hop vehicle resources for task computing. In order to simultaneously guarantee the system-level performance and the user-level performance, TCRM aims to maximize the task completion rate while minimizing the energy consumption by intelligent resource optimization and task allocation. A TD3-based algorithm combined with the Dirichlet distribution is proposed to obtain the optimization decisions. Extensive simulations demonstrate that TCRM significantly improves performance compared to baseline algorithms.

In 6G-enabled vehicle-to-edge networks, through deploying computing power resources closer to mobile vehicles for providing low latency and highly reliable services, Mobile Edge Computing (MEC) as an emerging paradigm has promoting mobile vehicles with limited capacities to come with diversified artificial intelligence (AI) applications. Nevertheless, the computing power of MEC server is still finite, seeking the optimal resource pricing and allocation strategies for MEC servers, and determining the optimal computation offloading for intelligent vehicle applications, still remain challenging issues that are necessary to be reasonably solved to improve the service experience. To address this issue, a scenario that multiple intelligent vehicle applications cooperatively initiate computation offloading requests in 6G-enabled multi-server multi-access vehicle-to-edge computing systems is considered in this paper, and with the defined reasonable utilities for MEC servers and intelligent vehicle applications, a solution of bidding-enabled dynamic resource pricing for computation offloading is proposed. Concretely, through considering the relationship of resource supply and demand, and the bidding among MEC servers, a dynamic resource pricing scheme is designed for MEC servers, meanwhile, with the complete consideration of dynamic resource pricing and time-varying wireless channel interference in multi-cell networks, a Q-learning based offloading decision algorithm is proposed for intelligent vehicle applications. Simulation experiments finally are conducted to demonstrate the efficiency in achieving the win-win situation with guaranteed utilities for both MEC servers and intelligent vehicle applications.

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.

In unsupervised graph anomaly detection, existing methods usually focus on detecting outliers by learning local context information of nodes, while often ignoring the importance of global context. However, global context information can provide more comprehensive relationship information between nodes in the network. By considering the structure of the entire network, detection methods are able to identify potential dependencies and interaction patterns between nodes, which is crucial for anomaly detection. Therefore, we propose an innovative graph anomaly detection framework, termed CoCo (Context Correlation Discrepancy Analysis), which detects anomalies by meticulously evaluating variances in correlations. Specifically, CoCo leverages the strengths of Transformers in sequence processing to effectively capture both global and local contextual features of nodes by aggregating neighbor features at various hops. Subsequently, a correlation analysis module is employed to maximize the correlation between local and global contexts of each normal node. Unseen anomalies are ultimately detected by measuring the discrepancy in the correlation of nodes’ contextual features. Extensive experiments conducted on six datasets with synthetic outliers and five datasets with organic outliers have demonstrated the significant effectiveness of CoCo compared to existing methods.

Federated Learning (FL) allows multiple clients to collaboratively train machine learning models without the need to share their local private data. As a result, it can effectively address the issue of data fragmentation. Nevertheless, insufficient evaluation of individual contributions and the lack of protections for both the intellectual property rights (IPR) of models and client privacy can greatly reduce clients' motivations in federated training. To address these challenges, this paper introduces the Traceable Contribution Evaluation and Model Ownership Protection (TraCemop) framework for federated learning, which allows each client to swiftly assess the contributions of others in each round, with integrated support for the traceability of evaluation results. To safeguard the intellectual property of models, a collective watermark is embedded in the global model. Additionally, a secure mechanism for verifying model ownership is also available in case of disputes. Security analysis indicates that TraCemop is capable of resisting data reconstruction attacks as well as various types of model copyright infringements. Finally, we evaluate the proposed framework using two commonly-used datasets, and the experimental results show a significant improvement in the efficiency of contribution evaluation compared to existing methods. Meanwhile, IPR infringement tests on TraCemop reveal that the proposed framework is resilient against malicious efforts to monopolize model ownership.

Electrocardiogram(ECG) signal analysis is crucial in healthcare service computing. Ensuring accurate assessment of ECG signal quality is vital to prevent wastage of transmission bandwidth and ineffective analysis caused by noise. This enables the efficient utilization of service resources. However, existing ECG signal quality assessment(SQA) methods primarily focus on single-modal learning, overlooking the interrelation of ECG in a multimodal feature space and failing to effectively exploit available information for pattern mining. In this paper, we model the SQA for ECG as an anomaly detection problem and propose a multimodal unsupervised SQA method. It jointly explores the boundaries between high-quality ECG and noise in both the time and frequency domains by introducing time-frequency pseudo anomalies. Specifically, we first simulate real ECG noise from the time-domain using a combination of a series of noises and convert it to the frequency-domain to form time-frequency pseudo-anomalies. Next, we map the time-frequency pseudo anomalies onto hyperspheres and jointly refine the hyperspheres learned only from high-quality ECG samples in both feature spaces. Finally, the noise score is defined as the distance from the joint time-frequency features to the center of the hypersphere. Multiple experiments on various real-world ECG datasets validate the superior performance of our proposed method.

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.

Federated learning (FL) has received widespread attention from academia and industry because it overcomes traditional security limitations associated with model training data. However, the FL process is vulnerable to manipulation by locally malicious users, who can alter their local data, thus impacting the accuracy of the model’s training outcomes. Meanwhile, optimizing delay in FL needs to take individual client fairness into consideration. In this paper, we present a reputation-based model aggregation and resource optimization framework to enhance the efficiency and reliability of training in wireless FL systems. Particularly, we investigate a total delay minimization problem while ensuring fairness among clients, which jointly optimizes client scheduling, transmit rate, bandwidth proportion, and CPU frequency. Considering the non-convexity and high complexity of the objective function, we decoupled the optimal variables and designed an efficient algorithm. By doing this, the client scheduling policy is obtained by deep reinforcement learning. Then, the transmit rate allocation and bandwidth proportion are derived through the Lagrangian dual method. Finally, we attain the CPU frequency allocation via the adaptive harmony algorithm. Simulation results reveal that our algorithm can establish delay fairness among clients and balance convergence performance and delay.

Millimeter wave (mmWave) Device-to-Device (D2D) communication networks suffer high path loss and dynamic physical obstructions. Meanwhile, eavesdroppers can intercept confidential information by residing in the main or side lobe of the transmission beam. Fortunately, multiple distributed Reconfigurable Intelligent Surfaces (RISs) offer a valuable approach to support mobile D2D devices, mitigating blocking effects and enhancing data security. In this paper, we propose a deep reinforcement learning (DRL) based communication scheme for the multi-RIS aided dynamic mmWave D2D networks, which aims to maximize the total secrecy data volume of D2D users over each service period by jointly optimizing the RIS resource allocation and multi-RIS phase shift design. To implement the intelligent collaboration of the RISs, we design the DRL approach with a nested structure. Specifically, we adopt a proximal policy optimization (PPO) network with discrete actions to realize the RIS-User association. Subsequently, we integrate the multi-agent PPO (MAPPO) framework to derive the phase shift design, containing intricate dynamic competition and cooperation among RIS agents. In addition, we divide the RIS into multiple subarrays, each sharing the same reflection coefficient. This approach controls more RIS phases while ensuring training stability. Simulation results demonstrate that our scheme can effectively learn the communication strategy to enhance the secrecy performance of dynamic mmWave D2D networks.

Satellite Terrestrial Networks (STN) are known to enhance the quality of service and to provide a better user experience. However, STNs are primarily utilized as wide-range relays and are characterized by a lack of effective intersatellite collaboration. The communication efficiency and quality of near real-time remote sensing tasks in 6G space-air-ground integrated networks are enhanced by the proposed Orbital Edge Computing-Collaborative Offloading Scheme (OEC-COS), which utilizes Service Function Chains (SFC) and the processing and collaboration capabilities of satellite nodes to allocate near real-time remote sensing tasks to optimal satellite nodes for execution. The remote sensing task offloading problem has been formulated as a delay minimization problem, and the advantages of the OEC-COS scheme in terms of computational resource utilization ratio and latency are validated through simulation experiments by comparing it with average orbit allocation and co-orbit allocation schemes. The proposed OEC-COS scheme achieves the lowest average task computing delay among these methods.

Rapid advancements in wireless communication have led to a dramatic upsurge in data volumes within mobile edge networks. These substantial data volumes offer opportunities for training Artificial Intelligence-Generated Content (AIGC) models to possess strong prediction and decision-making capabilities. AIGC represents an innovative approach that utilizes sophisticated generative AI algorithms to automatically generate diverse content based on user inputs. Leveraging mobile edge networks, mobile AIGC networks enable customized and real-time AIGC services for users by deploying AIGC models on edge devices. Nonetheless, several challenges hinder the provision of high-quality AIGC services, including issues related to the quality of sensing data for AIGC model training and the establishment of incentives for big data sharing from mobile devices to edge devices amidst information asymmetry. In this paper, we initially define a Quality of Data (QoD) metric based on the age of information to quantify the quality of sensing data. Subsequently, we propose a contract theoretic model aimed at motivating mobile devices for big data sharing. Furthermore, we employ a Proximal Policy Optimization (PPO) algorithm to determine the optimal contract. Numerical results demonstrate the efficacy and reliability of the proposed PPO-based contract model.

The fronthaul network architecture is the key to dealing with the massive traffic effectively and providing high-quality service, and the multiple base stations (BSs) deployed by it face the gigantic data transmission, which has given the demand for high-capacity communication and information security in the vehicular network. In this paper, we combine the millimeter wave (mmWave) communication and non-orthogonal multiple access (NOMA) technologies to escalate the communication capacity of multiple vehicle users (VUs), and propose a blockage-based cooperative jamming strategy to solve potential security risks in the vehicular network. In particular, with the help of jam-mers selected by this strategy, transmission security is enhanced simultaneously without escalating the instability of connections caused by the time-varying nature of vehicular networks under the NOMA transmission mechanism when the base station (BS) does not fully understand the channel state information (CSI) of VUs. Then we comprehensively analyze the specific distribution of roadways and the distance distribution of VUs under the NOMA strategy, and derive the performance metrics of the network based on the stochastic geometry method. Numerical results show that the proposed cooperative jamming scheme can effectively improve the secrecy performance of the vehicular network.

Artificial Intelligence (AI) tasks represent a substantial portion of the current workload within edge networks. However, existing centralized task scheduling approaches in network environments fall short of meeting the performance requirements of a diverse range of tasks. In response to this challenge, this paper introduces a finely-grained distributed task scheduling framework tailored to manage AI tasks efficiently. Specifically, we address the joint optimization problem of minimizing task completion time and maximizing server resource utilization. This involves the coordinated adjustment of task scheduling decisions, CPU/GPU frequencies, bandwidth allocation, and task deployment, all while adhering to constraints such as latency and energy consumption. To tackle this non-convex optimization problem, we adopt a multi-resource-objective-based multi-agent reinforcement learning (MRO-MARL) algorithm. This algorithm demonstrates adaptability to complex and dynamic environments where multiple resources require management. Its inherent flexibility facilitates the efficient scheduling of AI tasks within edge networks. Simulation results confirm the superior convergence and task execution efficiency of the proposed algorithm compared to baseline algorithms.

Due to the challenges of traditional machine learning in terms of data privacy and transmission efficiency, an efficient and private distributed training framework, namely federated learning (FL), is emerged. In the FL training process, users only need to upload to the server, thus preserving user privacy data and improving transmission efficiency. The computing network can provide sufficient computing power support for federated learning training. However, FL still faces many difficulties, such as dynamic wireless channels, limited local computing resources, data heterogeneity, and malicious data attacks. To tackle these challenges, it is crucial to select reasonable clients to participate in training. In this paper, we propose a client selection strategy that considers data quality, computing capacity, and radio resources. We first define a data quality metric by measuring the heterogeneity and reliability of the local dataset. Based on this, we formulate a joint optimization problem of client selection and resource allocation to minimize the average time delay and power consumption while maximizing data quality. Considering the dynamic of wireless channels and computing frequency, an online learning algorithm based on multi-armed bandit (MAB) is developed to obtain the client selection. Finally, a large number of simulations are carried out to verify the effectiveness of the proposed algorithm. The evaluation in different scenarios shows that the DQ-UCB algorithm can discard the attacked clients and the clients with poor computing power or channel quality to achieve better performance.

Unmanned aerial vehicles (UAVs), known for their high flexibility and maneuverability, are regarded as the aerial platforms of future integrated sensing and communication (ISAC) networks. The communication and sensing functions of ISAC share the same spectrum and signal waveform, which often results in communication information being embedded within the sensing waveforms, thereby increasing the risk of information leakage. To enhance the security of UAV-assisted ISAC, we propose a beamforming strategy based on the mutual cooperation between communication and sensing. Specifically, by utilizing the sensing function to process echo signals, we estimate the positions of potential eavesdroppers and obstacles, which supports subsequent obstacle avoidance trajectory planning and physical layer security design. To ensure the transmission secrecy, we introduce artificial noise into the system. By designing the UAV transmit beamforming and the covariance matrix of the artificial noise, we formulate an optimization problem that aims to minimize the signal-to-noise ratio (SNR) received by the eavesdropper. To address this non-convex optimization problem, we propose an optimization algorithm that combines Dinkelbach's transform and semidefinite relaxation (SDR). Simulation results demonstrate that the SNR of eavesdropper remains at a low level throughout the flight of UAV, validating the effectiveness of the proposed scheme.

Ensuring reliable communication can be incredibly challenging in emergencies due to the breakdown of conventional infrastructure. However, a promising solution is on the horizon: the integration of reconfigurable intelligent surfaces (RIS) onto unmanned aerial vehicles (UAV), known as UAV-RIS. This innovative approach holds the potential to offer agile and adaptable communication services during crises, overcoming the limitations of traditional systems. This paper establishes an innovative UAV-RIS system with an active RIS to enhance the uplink communication between ground devices (GDs) and the air base station (ABS). We present an advanced communication strategy utilizing deep reinforcement learning (DRL) for UAV-RIS-supported uplink communication in dynamic emergencies. This scheme is designed to optimize the energy efficiency of the UAV-RIS communication system while adhering to quality of service (QoS) constraints for all GDs. It achieves this by jointly optimizing the trajectory of the UAV-RIS and the phase of the active RIS, ensuring efficient and reliable communication in challenging environments. To optimize the performance of the system, we propose a hierarchical Proximal Policy Optimization (H-PPO) algorithm and the upper and lower layers of H-PPO optimize the trajectory and phase control, respectively. Simulation results demonstrate that our scheme can effectively learn the communication strategy to enhance the performance of dynamic emergency communication networks.

The massive data transmission in vehicular networks has given rise to the demand for high-capacity communication and information security. In this paper, we combine the millimeter wave (mmWave) communication and non-orthogonal multiple access (NOMA) technology to escalate the communication capacity of multiple vehicle users (VUs), and design artificial noise (AN)-based secure transmission schemes for this new NOMA-mmWave communication architecture. The AN beamforming matrix is derived from the mmWave discrete angular channel model to fully exploit the characteristics of mmWave propagation and facilitate the analysis. Then we comprehensively analyze the specific distribution of roadways and the distance distribution of VUs under the NOMA strategy, and derive the analytical expressions for the performance metrics. Numerical results demonstrate that the proposed scheme can effectively improve the secrecy performance of the NOMA-mm Wave vehicular communications.

The convergence of transformative technologies, including the Internet of Things (IoT), Big Data, and Artificial Intelligence (AI), has driven private edge cloud systems to the forefront of research efforts. The access to massive terminals and the emergence of personalized services pose serious challenges for efficient resource management in power private edge cloud systems. To address the challenge of inequitable resource allocation in the private edge cloud, this work proposes an intelligent resource allocation strategy with a slicing and auction approach. By formalizing the resource allocation problem as a Mixed Integer Nonlinear Programming (MINLP) puzzle, the method transforms it into a hierarchical allocation challenge for Mobile Network Operators (MNOs), Mobile Virtual Network Operators (MVNOs), and power terminals. The proposed Multi-hop Progressive Auction Algorithm (MPAA) addresses the sliced resource allocation problem between MNOs and MVNOs. Furthermore, a Terminal Resource Allocation Strategy (TRAS) based on improved particle swarm optimization is proposed to solve the spectrum resource allocation problem between MVNOs and power terminals. Extensive simulation results show that the bidding overhead of MPAA is reduced by 6.12% and the average terminal satisfaction of TRAS is improved by about 1.3% compared to conventional methods, thus improving the utilization of wireless resources within the power AIoT.

The blockchain-based Internet of Things (IoT) has served widely across various industries for authentication, cooperation, and data sharing but faced the severe challenge of storage scalability. The storage burden gets worse for IoT devices with limited resources. The state data is essential for efficient transaction issuance and validation. This article proposes the Verkle accumulator-based stateless transaction validation (VA-STV) scheme for permissionless blockchains to decrease the storage burden of the state data on each node with the acceptable overhead of computation and communication. In the scheme, the current state is summarized as the commitment maintained in the latest block header, and one witness is generated for each token to guarantee its validity. State transitions are realized by updating the commitment and witnesses so that no state is stored on nodes acting as validators and miners. Only the nodes acting as traders should maintain the tokens controlled by themselves and the witnesses locally. The VA-STV is based on the Verkle accumulator (VA), which is a combination of the Verkle tree (VT) and the KZG polynomial commitment scheme. Simulation results show that the VA-STV provides a smaller witness size ($0.6\times $–$0.74\times $) and faster commitment generation ($6\times $–$14\times$) than the existing stateless schemes in the same settings, which indicates the advantages of VA-STV in succinctness and efficiency. Besides, a tradeoff between the communication and computation requirements can be achieved by adjusting the branching factor, which improves the adaptability of the proposed scheme for different IoT scenarios.

Vehicle-to-Everything (V2X) is an important application scenario in 6G, where secure transmission is crucial in vehicular networks. Thus, this paper explores the application of an intelligent reflecting surface (IRS) in secure multipleinput multiple-output (MIMO) communication systems, which is subject to an eavesdropper equipped with multiple antennas. We formulate the secrecy rate maximization problem by jointly designing the transmit beamforming and the IRS phase-shift. Due to the coupling of the variables, the formulated problem is non-convex and thus we split the original problem into two sub-problems. For the two sub-problems, we first relax the sub-problem into a semi-definite program problem and solve it with the CVX tools. To further provide more insights into the calculation of the IRS phase-shift, we proposed the Riemannian manifold optimization (RMO) and majorization minimization (MM) algorithms to derive the closed-form solution of this subproblem. The numerical results validate that: 1) Through the proposed RMO and MM algorithms, the computation complexity is effectively reduced; and 2) the secure performance is significantly improved by the IRS.

Unmanned aerial vehicle (UAV) assisted data collection has been extensively employed in various application scenarios, e.g., nonterrestrial networks for disaster management, agricultural crop protection, environmental monitoring. However, data collection and transmission model in different applications are not universal, and the timeliness of large-scale data collection and transmission also has been remained as a challenge. To address this issue, artificial intelligence (AI)-empowered intelligent search algorithms for path planning in UAV-assisted data collection networks are investigated in this article. With the constraints, including energy consumption, transmission distances, and full coverage of sensors, a data collection model using UAV in hovering mode is first established for minimizing the flight distances of UAVs, and an adaptive full coverage algorithm (AFCA) is proposed to optimize the Quality of Service through using the model. Subsequently, for optimizing the path planning of UAVs, an intelligent path planning algorithm (IPPA) is proposed through considering the loop and noncrossing characteristics presented by the optimal paths. In six testing cases with different sensor sizes, the experimental results have been shown to demonstrate that the proposed solution outperforms the traditional algorithms.

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.

For multiple unmanned-aerial-vehicles (UAVs)-assisted mobile-edge computing (MEC) networks, we study the problem of combined computation and communication for user equipments deployed with multitype tasks. Specifically, we consider that the MEC network encompasses both communication and computation uncertainties, where the partial channel state information and the inaccurate estimation of task complexity are only available. We introduce a robust design accounting for these uncertainties and minimize the total weighted energy consumption by jointly optimizing UAV trajectory, task partition, as well as the computation and communication resource allocation in the multi-UAV scenario. The formulated problem is challenging to solve with the coupled optimization variables and the high uncertainties. To overcome this issue, we reformulate a multiagent Markov decision process and propose a multiagent proximal policy optimization with Beta distribution framework to achieve a flexible learning policy. Numerical results demonstrate the effectiveness and robustness of the proposed algorithm for the multi-UAV-assisted MEC network, which outperforms the representative benchmarks of the deep reinforcement learning and heuristic algorithms.

The traditional data security systems have the problems, such as poor adaptability, technical barriers, and closed interfaces, which cannot meet the development requirements of beyond 5G (B5G) and the Internet of Things (IoT). In this article, we design a novel deployment network framework for adaptive security by using blockchain technology in the software defined network (SDN)-enabled mobile edge computing (MEC) system. The blockchain is deployed on multiple SDN servers, ensuring decision consistency and data security. The distributed SDN controllers can schedule and combine atomic security functions (ASFs) from the security resource pool of the MEC system to provide comprehensive security services. Furthermore, we developed a multiagent deep deterministic policy gradient (MADDPG) scheduling optimization algorithm to enhance the utility of our model while optimizing latency and energy cost. Simulations indicate that the algorithm successfully maximizes the overall utility of the MEC system, while adhering to the constraints of latency and energy cost.

Metaverse is a visually rich virtual space in which users can interact with each other. By introducing metaverse into vehicular networks, vehicular metaverse can provide users real-time immersive experiences based on augmented technologies. Vehicular edge computing is a desirable approach to support computation-intensive vehicular metaverse services by network resource collaboration. User collaboration needs to reach a consensus on perception information, operation control and so on to realize user autonomy. However, the existing consensus algorithms often require computational proof or frequent communication, making them unsuitable for dynamically changing vehicular edge metaverse with low latency and energy restrictions. In this paper, we have proposed a reputation model maintained in the vehicular edge metaverse to score the vehicles, so the vehicles with a high reputation can be selected to participate in practical Byzantine fault tolerant (PBFT) consensus, which improves the probability of success and credibility of consensus without increasing the number of participating vehicles. Meanwhile, an optimization problem is formulated for each vehicle to allocate its computation and communication resources to reach a PBFT consensus. Also, the optimized communication time interval of each phase in the PBFT consensus can be used as a reference for setting the agreed upper time, which reduces the waiting time of vehicles and the probability of re-consensus. Simulation results have demonstrated that the proposed scheme effectively achieves PBFT information consensus with lower latency and energy consumption, and thus is more scalable and efficient.

To support emerging environmentally-aware intelligent applications, a massive amount of data needs to be collected by sensor devices and transmitted to edge/cloud servers for further computation and analysis. However, due to the high deployment and operational cost, only depending on terrestrial infrastructures cannot satisfy the communication and computation requirements of sensor devices in the unexpected and emergency situations. To tackle this issue, this paper presents a digital twin-enabled space-air-ground integrated sensing, communication and computation network framework, where unmanned aerial vehicles (UAVs) serve as aerial edge access point to provide wireless access and edge computing services for ground sensor devices, and satellites provide access to cloud data center. In order to tackle the complex network environments and coupled multi-dimensional resources, the digital twin technique is utilized to realize real-time network monitoring and resource management, and the mapping deviation is also considered. To realize real-time data sensing and analysis, we formulate a maximum execution latency minimization problem while satisfying the energy consumption constraints and network resource restrictions. Based on the block coordinate descent method and successive convex approximation technique, we develop an efficient algorithm to obtain the optimal sensing time, transmit power, bandwidth allocation, UAV deployment position, data assignment strategy, and computation capability allocation scheme. Simulation results demonstrate that the proposed method outperforms several benchmark methods in terms of maximum execution latency among all sensor devices.

To satisfy the heterogeneous service requirements in future internet of things (IoT), this paper investigates the novel framework for intelligent reflecting surface (IRS)-aided wireless powered over-the-air computation (AirComp) and communication networks, where the IoT devices first harvest energy from the downlink signal sent by the base station, and then conduct the information transmissions and AirComp in the uplink. In particular, the IRS is used to improve the efficiency of wireless energy transfer, and alleviate the harmful interference between the communication and AirComp signals. To balance the performance of such an integrated system, we present two joint beamforming and reflection optimization problems via minimizing the computation distortion and maximizing the sum rate, respectively. To solve the non-convex problems, we develop the alternating optimization framework with proved convergence, in which the penalty function-based method and variable substitution technique are exploited to acquire the optimal solutions of beamformers and reflection parameters. Finally, simulation results show that the proposed method realizes significantly higher computation accuracy and communication rate, in comparison with several existing benchmark methods.

Multimodal retrieval has received widespread consideration since it can commendably provide massive related data support for the development of computational social systems (CSSs). However, the existing works still face the following challenges: 1) rely on the tedious manual marking process when extended to CSS, which not only introduces subjective errors but also consumes abundant time and labor costs; 2) only using strongly aligned data for training, lacks concern for the adjacency information, which makes the poor robustness and semantic heterogeneity gap difficult to be effectively fit; and 3) mapping features into real-valued forms, which leads to the characteristics of high storage and low retrieval efficiency. To address these issues in turn, we have designed a multimodal retrieval framework based on web-knowledge-driven, calledunsupervised and robust graph convolutional hashing(URGCH). The specific implementations are as follows: first, a “secondary semantic self-fusion” approach is proposed, which mainly extracts semantic-rich features through pretrained neural networks, constructs the joint semantic matrix through semantic fusion, and eliminates the process of manual marking; second, a “adaptive computing” approach is designed to construct enhanced semantic graph features through the knowledge-infused of neighborhoods and uses graph convolutional networks for knowledge fusion coding, which enables URGCH to sufficiently fit the semantic modality gap while obtaining satisfactory robustness features; Third, combined with hash learning, the multimodality data are mapped into the form of binary code, which reduces storage requirements and improves retrieval efficiency. Eventually, we perform plentiful experiments on the web dataset. The results evidence that URGCH exceeds other baselines about$1\%$–$3.7\%$in mean average precisions (MAPs), displays superior performance in all the aspects, and can meaningfully provide multimodal data retrieval services to CSS.

Data mining, integration, and utilization are the inevitable trend of the Internet of Medical Things (IoMT) in the context of Big Data. With the increasing demand for data privacy, federated learning has emerged as a new paradigm, which enables distributed joint training of medical data sources without leaving the private domain. However, federated learning is suffering from security threats as the shared local model will reveal original datasets. Privacy leakage is even more fatal in healthcare because medical data contains critically sensitive information. In addition, open wireless channels are susceptible to malicious attacks. To further safeguard the privacy of IoMT, we propose a comprehensive privacy-preserving federated learning scheme with a tactful dropout handling mechanism. The proposed scheme leverages blind masking and certificateless proxy re-encryption (CL-PRE) for secure aggregation, ensuring the confidentiality of the local model and rendering the global model invisible to any parties other than clients. It also provides authentication of uploaded models while protecting identity privacy. Compared with other relevant schemes, our solution has better performance on functional features and efficiency, and is more applicable to IoMT systems with many devices.

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.

In this paper, we propose a platoon access authentication system for initial access process in autonomous vehicle platoons (AVPs) in which post-quantum encryption and signal processing techniques are employed to protect against both active and passive cyber-physical attacks. To avoid passive quantum cyber attacks, a quasi-cyclic moderate-density parity-check code is used to encode and decode AVP messages. Moreover, an independent component analysis-based signal separation technique is employed to eliminate the effect of high-power active cyber attacks on AVP messages. To measure the reliability of the system, we derive an analytical expression for the system failure probability, taking into account the influence of both the cyber and physical planes. The simulations show that the proposed system is effective against attacks and can help reduce system failures caused by intentional and unintentional adverse cyber-physical effects. The proposed system offers a potential solution to the challenge of protecting initial access while maintaining ultra-reliable low-latency communications between AVPs and the infrastructure.

This paper examines integrating jamming and secondary signals for covert communications in cognitive radio networks (CRNs), aiming to enhance covertness by using jamming and secondary signals in an overlay cooperative CRN. The scenario involves a primary base station (PBS) transmitting to a primary user (PU), with a secondary user transmitter (SU-Tx) acting as a cooperative jammer to obscure the message from a malevolent secondary user named “Willie.” During idle intervals on the primary channel, the SU-Tx opportunistically accesses it to transmit secondary signals, reinforcing the covert communication of primary signals. The study quantifies the detection error probability (DEP) experienced by Willie, considering perfect and statistical channel state information (CSI) scenarios. In the perfect CSI scenario, optimization has two phases. Phase I aims to maximize the signals-to-interference-plus-noise ratio (SINR) of the PU, subject to the warden DEP exceeding a specified threshold. Phase II uses an iterative search algorithm to optimize beamforming vectors, enhancing SINR. In the statistical CSI scenario, the goal is to maximize effective transmission throughput (ETT), measuring the information transmitted from PBS to PU under covert constraints. Numerical results validate the theoretical analysis.

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.

Federated learning has been combined with graph learning for modeling spatial-temporal data while maintaining data confidentiality and safety. However, there are still several issues: 1) In practical usage, some clients may be unable to participate in the model inference due to poor network signal, malicious attacks, etc. 2) In the communication process, the uploaded information is easily disturbed by noise. The performance of the graph model will be seriously affected by its low robustness. Additionally, the assumption of identical distribution between the training and testing domain does not hold in practical scenarios, resulting in overfitting and poor generalization ability of the trained models. 3) The relations that exist among clients may change dynamically over time and manually constructing the graph structure of clients may not accurately represent the relations among clients. In this paper, we address all the above limitations by proposing a robust hierarchical split-federated graph model named DCSFG. Specifically, DCSFG combines split-federated learning and spatial-temporal graph model to better capture the spatial-temporal dependencies. We propose a Dropclient method and introduce the uncertainty estimation to enhance the robustness and generlization ability of the model. We also design a dual-sub-decoders structure for clients so that they can perform predictions locally and independently when they are unable to participate in the inference process. A novel hierarchical graph message passing structure is proposed to enable each client to perceive the global and local information. The extensive experimental results demonstrate the effectiveness of DCSFG.

Utilizing the stable underlying and cloud-native functions of vehicle platoons allows for flexible resource provisioning in environments with limited infrastructure, particularly for dynamic and compute-intensive applications. To maximize this potential, we propose the creation of a trading market to encourage interactions between service supporters (vehicle platoons) and requesters (task vehicles). Current trading decisions based on game and negotiations can lead to unpredicted handover costs and increased communication overhead in dynamic environments. Moreover, existing research tends to overlook a mutually beneficial trading philosophy by focusing on either the service supporters’ profitability or the user experience of resource-restrained requesters. Addressing these issues, we introduce a multi-objective optimization problem to model environmental dynamics and uncertainty, aiming to maximize both platoons’ and task vehicles’ long-term utilities while maintaining a satisfactory service access ratio. To tackle the problem within acceptable time frames, we develop a global-local training architecture, incorporating a hybrid action space and prioritized sampling into a multi-agent reinforcement learning algorithm that utilizes a twin delayed deep deterministic gradient (GL-HPMATD3). This approach facilitates consensus in the trading market on key issues, including service request selection, resource allocation, and trading pricing. Through extensive experimentation and comparison, we demonstrate our mechanism’s superior performance in convergence, service access ratio, player utility, execution latency, and trading pricing relative to several state-of-the-art and baseline methods.

Cyber-physical systems (CPS) offer integrated resolutions for various applications by combining computer and physical components and enabling individual machines to work together for much more excellent benefits. The ad hoc –based CPS provides a promising architecture due to its decentralized nature and destructive-resistance. A growing number of information leakage events in CPSs and the following serious consequences have aroused ubiquitous concern about information security. In this article, we combine physical layer security solutions and millimeter-wave (mmWave) techniques to safeguard the ad hoc network and investigate the reliability-security tradeoff by taking user demands for the network into account, where eavesdroppers attempt to intercept messages. For the secrecy enhancements, we adopt an artificial noise (AN) assisted transmission scheme, in which AN is employed to create non-cancellable interference to eavesdroppers. The reliability and security are correspondingly characterized by the connection outage probability and secrecy outage probability, and their analytical expressions of them are attained through theoretical analysis for the purpose of the tradeoff issue discussion. Our results reveal that secrecy performance in mmWave ad hoc networks gains significant improvement through the use of AN. It also shows that given total transmit power, there exists a tradeoff between reliability and security to achieve optimal outage performance.

Since the introduction of Bitcoin in 2008, blockchain technology has found widespread applications across various domains. While blockchain offers convenience and immense research value, it also raises privacy and security concerns among users and society at large. Notably, numerous studies have demonstrated the vulnerability of blockchain anonymity. Existing solutions based on bloom filters and SGX(Software Guard Extensions) may safeguard users' access patterns but remain susceptible to novel attacks, including protocol-level and side-channel attacks. To address these issues, we propose a Tree-based Oblivious Random Access Protocol (Tree-ORAP) that not only provides access pattern protection in privacy-preserving blockchain systems but also preserves the original blockchain performance. Furthermore, we design a Tree-ORAP State Version Controller to manage state synchronization across nodes in a multi-client blockchain network. We also analyze the system's security and implement a Tree-ORAP prototype, conducting a series of experiments to demonstrate its efficiency and technical feasibility. In summary, our protocol offers enhanced protection for blockchain systems against a wider range of attacks compared to previous methods, all while maintaining superior security performance and equal or better efficiency.

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%.

Joint detection and communication (JDC) systems can implement both functionalities simultaneously using the same hardware and software resources. This feature proves advantageous in reducing the size and power consumption of underwater vehicles. This paper develops a JDC system based on multi-input multi-output sonar by using orthogonal linear frequency modulation (OLFM) waveforms. Here, the proposed OLFM-based JDC system (OLFM-JDC) considers the detection functionality as the primary task. Therefore, OLFM-JDC exploits the mainlobe of transmit beam to detect targets and the sidelobes to communicate with the remote receivers. To enhance the information embedding capacity, the waveform diversity and the combination of index and phase modulations are utilized. Particularly, we propose a low-complexity two-step decoder to simplify the information decoding. Furthermore, the two-step decoder effectively utilizes multipath information to improve the performance of index decoding, and it can even outperform the maximum likelihood scheme when assessed within a simulated South China Sea acoustic channel. The numerical results demonstrate that OLFM-JDC achieves higher data rates and lower error rates compared to the JDC systems that only utilize phase modulation. Additionally, the simultaneous transmission of multiple waveforms facilitates target detection by utilizing the generalized high-resolution range profile synthesis technique. Performance analysis indicates that OLFM-JDC exhibits similar resolution performance to systems implementing a wideband waveform.

With the continuous development of mobile communication networks and artificial intelligence technology, the number of smart mobile devices has shown an exponential growth trend, and artificial intelligence (AI) jobs have developed unprecedentedly. However, it is difficult for resource-constrained mobile devices to meet the computational demands of these jobs. How to make full use of the dynamic resources in the wireless network to achieve efficient execution of AI jobs is the evolution direction of the next-generation network. To achieve this goal, we model the job as a directed acyclic graph (DAG), partition it into executors based on the type of task, and minimize the execution time of all jobs in 6G wireless networks by optimizing executors deployment. Considering the dynamic features of channel states and DAG topology, the optimization problem is addressed by deep reinforcement learning, i.e., Deep Q-Network (DQN). In the simulation, we manifest the performance of the DQN-based DAG task scheduling in terms of convergence and latency.

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.

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.

Millimeter wave (mmWave) technology provides abundant high-capacity channel resources for vehicular communications. However, the mobility of vehicles and the blocking effect of mmWave propagation brings new challenges to communication security. From the perspective of cooperative secure communication, this paper proposes a deep reinforcement learning (DRL)-based joint relay and jammer selection scheme in mmWave vehicular networks. The mmWave base station selects idle vehicles as relay transmission nodes to overcome the severe blocking attenuation of the multi-user downlink legitimate transmissions. Moreover, to ensure secure transmission, a cooperative vehicle is selected to transmit jamming signals to the eavesdropper while the users are not disturbed. We utilize the asynchronous advantage actor-critic (A3C) learning algorithm to optimize the cooperative vehicle selection with the objective of maximizing the total secrecy capacity. Besides, we set the secrecy rate punishment mechanism to guarantee the secrecy performance of each vehicle. We demonstrate that the proposed scheme can rapidly adapt to the highly dynamic vehicular networks and effectively improve secrecy performance.

High mobility of Internet of Vehicles (IoV) brings rapidly changing network topology, and massive data produced by vehicles aggravate heavy burden to the network. These may lead unreliable and high latency of data transmission and processing, which is not facilitate the application and popularization of automatic driving. Evolutions of intelligent vehicles and edge intelligence promising technologies enable vehicles as agents. Vehicles have abilities to act as aided Mobile Edge Computing (MEC) servers to support ultra-low communication and computing latency and super-high reliability data transmission and processing. In this paper, we elect some vehicles as aided MEC servers and design an improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm to involve more scattered vehicles for clustering. We adopt a Multi- Multi matching algorithm to pair vehicles and the aided MEC server, and designed a multi -agent- based task offloading mechanism to reduce latency and improve resource utilization efficiency. Furthermore, a reward mechanism is proposed to stimulate vehicles to be aided MEC servers instead of refusing to provide services. Evaluation results verify the proposed offloading method could effectively reduce the average delay, improve computing resources utilization and increase the benefit of aided MEC servers and service providers.

Despite the high directionality of antennas in terahertz communication, there remains a risk of confidential message interception when eavesdroppers are within the beam coverage area. This paper proposes a blockage-based cooperative jamming scheme to enhance the security of terahertz communication. Due to significant signal attenuation caused by blockages in the terahertz frequency band, we select idle users with blockages between them and the typical user in the indoor three-dimensional (3D) space to act as cooperative jammers. Thus, the jamming signal can deteriorate the reception of eavesdroppers while effectively minimizing interference to the typical user. Taking into account the influence of terahertz channel characteristics, blockage, and 3D antenna model, we derive analytical expression for the secrecy outage probability (SOP). Besides, we analyze the effects of access point (AP) density, blockage density, and user idle factor on network performance. Our results demonstrate that the blockage-based cooperative jamming scheme effectively improves the secrecy performance of the terahertz network.

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.

As a promising approach to extending cloud resources and services, blockchain-enabled Internet of Things (IoT)-based smart grid edge computing has attracted much attention. However, the edge node’s resource-constraint nature makes it difficult to store the entire chain as the sensing IoT data volume increases. To address this issue, we propose an FS scheme, a fast and secure multithreshold trapdoor Chameleon hash scheme which serves as the basis for block substitution at the edge nodes to solve the storage limitation problem. The FS scheme is used to achieve a consensus-based block substitution, which allows$t$-out-of-$n$edge nodes to compute a hash collision collaboratively to reliably substitute a historical block without leaking the randomness$R$. Also, inspired by the rationale of fast polynomial interpolation, we optimize the FS scheme to FS-I to reduce the time complexity from$\mathcal {O}(nt)$to$\mathcal {O}(t{\mathrm{ log}}^{2}t)$. In addition, we further optimize FS-I to FS-II by using a fast Fourier transform (FFT) to dramatically improve the computational efficiency of Lagrange interpolation, which leads to a significant improvement in terms of block substitution performance. Finally, We provide security analysis and evaluate the performance through comprehensive experiments and the results show that FS can achieve up to several magnitudes better than DTTCH. The results also demonstrate that the FS scheme can provide high service quality for large-scale IoT-based smart grid systems.

Intelligent reflecting surface (IRS) is envisioned as a promising technique to improve the performance of full-duplex wireless-powered communication networks (FD-WPCNs). This article investigates the joint phase beamforming design and resource management for IRS-assisted FD-WPCNs, where multiple wireless devices (WDs) can harvest downlink radio-frequency energy and transmit uplink information to the hybrid access point (HAP) over the same band with the aid of IRS. We first formulate a total transmission time minimization problem subject to the minimum transmit rate and energy causality constraints of WDs. In particular, the random phase error of IRS is integrated into our optimization model. Furthermore, we develop an alternating optimization method to obtain the optimal solution of the formulated nonconvex problem by iteratively solving two subproblems. For the phase beamforming optimization subproblem, we first convert the random phase errors to a deterministic expression, and then utilize the successive convex approximation method to solve the phase beamforming optimization problem. For the transmit power and time-slot allocation subproblem, the optimal transmit power of WDs is derived in closed-form expressions, and the approximation method and variable substitution technique are adopted to obtain the optimal time-slot allocation and transmit power of HAP. Finally, numerical results are provided to evaluate the performance of our proposed method and reveal the benefits introduced by the IRS technique as compared to benchmark methods.

Federated learning (FL) is an emerging technique to support privacy-aware and resource-constrained machine learning, where a base station (BS) will coordinate a set of distributed Internet of Things (IoT) devices to train a shared machine learning model with their local data sets. Nevertheless, due to the frequent interactions between BS and distributed IoT devices for the aggregating/distributing learning model parameters, the performance of FL is fundamentally restricted by the randomness of channel condition. To address this issue, we utilize the intelligent reflecting surface (IRS) to improve the efficiency of learning model aggregation/distribution. In addition, we consider two transmission protocols to enable the model aggregation from IoT devices to BS, i.e., frequency division multiple access (FDMA) and nonorthogonal multiple access (NOMA). For both protocols, we formulate the total training latency minimization problem under the available energy constraints of IoT devices, to jointly optimize the phase shifts of IRS, communication resource scheduling, and transmit power and local computing frequencies of IoT devices. Moreover, we further develop the efficient multidimensional resource management algorithms to solve the formulated training latency minimization problems. Numerical results demonstrate that the proposed IRS-assisted FL systems can achieve significant latency reduction as compared with other benchmark methods, and the NOMA-based model aggregation method exhibits a lower total training latency than the FDMA-based counterpart.

Secure ultrareliable low-latency communication (URLLC) has become a crucial requirement of mission-critical Internet of Things (IoT) applications and use cases, including automotive driving, remote surgery, and many others. However, it is still challenging to protect initial access of massive IoT devices over wireless channels, especially when malicious quantum adversaries paralyze the initial access by overhearing and tampering critical wireless messages, i.e., preambles. We propose a nested hash access system with post-quantum encryption to solve this issue. The system performs random repetition coding and nested hash coding on multidomain physical-layer resources to encode and decode preambles precisely and resiliently. Particularly, a subtle compression and encryption mechanism based on quasi-cyclic (QC)-moderate-density parity-check (MDPC) code is proposed between repetition and hashing operations to avoid passive eavesdropping during the preamble encoding process. We show that the code information can be maintained at 128-bit or higher privacy level, depending on the length of repetition code. Besides, the preamble decoding process can be proved secure agaisnt active attacks with a tolerable loss of decoding errors. Then, we formulate two nonconvex integer programming problems, each problem corresponding to the minimization of upper bound of preamble decoding error in an example application scenario. Finally, we can derive the expressions of system failure probability to evaluate the reliability of URLLC system under mission-critical IoT scenarios. Simulation results show the effectiveness of our proposed scheme despite attack.

Internet of Things (IoT) applications have penetrated into all aspects of human life. Millions of IoT users and devices, online services, and applications combine to create a complex and heterogeneous network, which complicates the digital identity management. Distributed identity is a promising paradigm to solve IoT identity problems and allows users to have soverignty over their private data. However, the existing state-of-the-art methods are unsuitable for IoT due to continuing issues regarding resource limitations for IoT devices, security and privacy issues, and lack of a systematic proof system. Accordingly, in this article, we propose SmartDID, a novel blockchain-based distributed identity aimed at establishing a self-sovereign identity and providing strong privacy preservation. First, we configure IoT devices as light nodes and design a Sybil-resistant, unlinkable, and supervisable distributed identity that does not rely on central identity providers. We further develop a dual-credential model based on commitment and zero-knowledge proofs to protect the privacy of sensitive attributes, on-chain identity data, and linkage of credentials. Moreover, we combine the basic credential proofs to prove the knowledge of solutions to more complex problems and create a systematic proof system. We go on to provide the security analysis of SmartDID. Experimental analysis shows that our scheme achieves better performance in terms of both credential generation and proof generation when compared with CanDID.

Wireless virtual reality (VR) is expected to be a technology that revolutionizes human interaction and perceived media, where the quality of experience (QoE) is an important indicator to measure user service perception. However, existing schemes only consider general and time-invariant QoE optimization, which may suffer performance degradation. Moreover, it is also necessary to ensure the fairness of the individual user’s performance in wireless VR. To address these challenges, we employ digital twin technology to investigate a max-min QoE-optimal problem for wireless VR systems in this paper. Specifically, we maximize the QoE of the worst-case head-mounted displays (HDMs) client, where the QoE model is the linear weighting combination of video quality, service delay, and energy efficiency. The formulated optimization problem is characterized by multidimensional control, which jointly optimizes model selection, transmit power, computation time, and GPU-cycle frequency. Due to the mixed combinatorial features of the optimization problem, we give a low-complexity algorithm design by decoupling the optimization variables. Notably, we first obtain the allocation of the transmit power by employing the generalized fractional programming theory and the Lagrangian dual decomposition, followed by attaining the optimal allocation of GPU-cycle frequency in VR mode is derived by the proposed adaptive modified harmony search algorithm, and finally achieve the computation time by the barrier method. Meanwhile, we devise a greedy-style heuristic algorithm for mode selection. In the simulation, three baseline schemes are established as comparisons to assess the effectiveness of the proposed scheme. Meanwhile, the simulation results manifest that the proposed algorithms have good convergence performance and better increase the QoE of the DT-enabled wireless VR system compared to benchmark solutions.

Road hazards (RH) have always been the cause of many serious traffic accidents. These have posed a threat to the safety of drivers, passengers, and pedestrians, and have also resulted in significant losses to people and even to the economies of countries. Hence, road hazards detection (RHD) could play an essential role in intelligent transportation systems (hypertarget ITSITS). The cooperative vehicle-infrastructure systems (CVIS) coordinate the communication between vehicles and roadside infrastructures. Onboard computing devices (OCD), then, make fast analyses and decisions based on road conditions. In this study, an RHD solution based on CVIS is proposed. Firstly, a high-performance heavy action detection model is selected. Using a meta-learning paradigm, critical features are generalized from a few-shot RH data. Secondly, we designed a lightweight RHD model to ensure its smooth inference on an OCD. Thirdly, we use a knowledge distillation (KD) framework to progressively distill the features of the complex model and the privileged information of the data into the lightweight one. Experimental results demonstrate that the model can effectively detect RH and obtain an accuracy of 90.2% with an inference time of 14.7ms.

Blockchain technology is developing rapidly and has been widely applied in the field of Internet of Vehicles (IoV) to solve trust and security problems. However, due to the high security requirements in IoV scenarios, the security threats of blockchain itself become a big challenge for its applications in IoV. As the largest distributed platform supporting smart contract, Ethereum becomes one of the popular blockchain platforms that has been applied in IoV applications. In Ethereum, the local world state (stored on Road Side Units (RSUs) in IoV) is applied to facilitate account query and transaction verification. However, previous works showed that the local database can be easily tampered, so attackers may issue invalid transactions based on the modified world state, which is not acceptable for IoV applications. In this paper, the success probability and expected time for such an attack are first analyzed theoretically, including the effect of portion of tampered RSUs and the number of required confirmation blocks. Then experiment evaluation verifies the correctness of the theoretical analysis and shows that the attack would succeed with a higher probability within a shorter time when the local database on more RSUs are attacked. On the contrary, increasing of confirmation blocks can effectively reduce the success probability of a single attack and extend the confirmation time of the invalid transaction. Finally, efficient attack detection and recovery methods are proposed based on a novel hierarchical architecture with hybrid RSUs, and the effectiveness and complexity are verified by theoretical analysis and experiments.

The mobile edge computing (MEC) technology can simultaneously provide high-speed computing services for multiple vehicular users (VUs) in vehicular edge computing (VEC) networks. Nevertheless, due to the open feature of the wireless offloading channels and the high mobility of the vehicles, the security and stability of the offloading process would be seriously degraded. In this paper, by utilizing the physical layer security (PLS) technique and spectrum sharing architecture, we propose a deep reinforcement learning based joint secure offloading and resource allocation (SORA) scheme to improve the secrecy performance and resource efficiency of the multi-user VEC networks, where the VU offloading links share the frequency spectrum preoccupied with the vehicle-to-vehicle (V2V) communication links. We use Wyner’s wiretap coding scheme to obtain the achievable secrecy rate and guarantee that confidential information cannot be decoded by multiple mobile eavesdroppers. We aim at minimizing the system processing delay while securing the wireless offloading process, by jointly optimizing the transmit power, the frequency spectrum selection and the computation resource allocation. We formulate the optimization problem as a multi-agent collaborative optimal decision problem and solve it with a double deep Q-learning algorithm. Besides, we set a punishment mechanism for the rate degradation to guarantee the communication quality of each V2V link. Simulation results demonstrate that multiple VU agents adopting the SORA scheme can rapidly adapt to the highly dynamic VEC networks and cooperate to improve the system delay performance while increasing the secrecy probability.

Vehicular Edge Computing (VEC) is enjoying a surge in research interest due to the remarkable potential to reduce response delay and alleviate bandwidth pressure. Facing the ever-growing service applications in VEC, how to effectively aggregate and flexibly schedule ubiquitous network resources for implementing diverse tasks and meeting differentiated demands from numerous vehicular users remains haunting. Toward this end, we investigate collaborative task computing and on-demand resource allocation. The collaborative computing framework in VEC is provided to support deep collaboration and intelligent management of heterogeneous resources widely distributed in vehicles, edge servers and cloud. Based on this framework, the joint optimization problem of distributed task offloading and multi-resource management is formulated with the aim to maximize the system utility by making the optimal task and resource scheduling policy, the novelty of which lies in the exploration of available vehicle resources and the consideration of service migration. In view of the dynamics, randomness and time-variant of vehicular networks, the asynchronous deep reinforcement algorithm is leveraged to find the optimal solution. Extensive simulation experiments are implemented to demonstrate the superiority of our proposed algorithm in terms of response latency compared with full offloading and random offloading.

With the large-scale deployment of the Internet of Vehicles (IoV) and 5G technologies, it is inevitable to share data frequently for superior in-vehicle services. However, due to the dynamically changing and widely distributed Vehicular Ad-hoc Networks (VANETs), data sharing still faces challenges in security, efficiency, and reliability. In this paper, we propose a secure and reliable data-sharing scheme (SDSS) for edge-enabled IoV networks. It assigns multiple attribute authorities to alleviate the management burden and support a large attribute universe catering to the various services in IoV. To enhance efficiency and flexibility, edge computing is introduced for quickly responding to vehicles’ requests and assisting resource-constrained vehicle computation. And an online/offline mechanism is designed to further alleviate the computational pressure of sharing data online. In addition, we put forward a cooperative key generation approach to guarantee the security of users’ private keys. The security analysis proves that SDSS ensures resistance to collusion attacks and indistinguishability under chosen-ciphertext attacks (IND-CCA). Moreover, it can avoid the single point of failure and resist denial of service (DoS) attacks with the help of multiple distributed edge nodes. The experiment demonstrates SDSS is practicable for IoV data sharing.

Vehicular Edge Computing (VEC) has gained increasing interest due to its potential to provide low latency and reduce the load in backhaul networks. In order to meet drastically increasing computation demands from emerging ever-growing vehicular applications, e.g., autonomous driving, abundant computation resources of individual vehicles can play a crucial role in task execution in a VEC scenario, that can further contribute in considerably improving user experience. This is however an extremely challenging task due to high mobility of vehicles that can easily lead to intermittent connectivity, thereby disrupting on-going task processing. In this paper, we propose a task offloading scheme by exploiting multi-hop vehicle computation resources in VEC based on mobility analysis of vehicles. In addition to the vehicles within one hop from the task vehicle that generates computation tasks, certain multi-hop vehicles that meet the given requirements in terms of link connectivity and computation capacity, are also leveraged to carry out the tasks offloaded by the task vehicle. An optimization problem is formulated for the task vehicle to minimize the weighted sum of execution time and computation cost of all tasks. A semidefinite relaxation approach with an adaptive adjustment procedure is proposed to solve the formulated optimization problem for obtaining the corresponding offloading decisions. The simulation results show that our proposed offloading scheme can achieve significant improvement in terms of response delay by at least 34% compared with the other algorithms (e.g., local processing and random offloading).

Air-to-ground (A2G) communication based on Unmanned aerial vehicle (UAV) is an important part of the future communication system. In this paper, an A2G channel model with UAV three-dimensional (3D) wobbles (pitch, roll, and yaw) based on the geometry-based stochastic model (GBSM) is proposed. On this basis, the UAV’s internal vibration is modeled as a sinusoidal random process, and the UAV wobble caused by the atmospheric flow is modeled as the uniform distribution random process. We derive the channel temporal correlation function (CF) with UAV 3D wobbles, analyze the variation of the temporal CF with different carrier frequencies, and amplitudes of the wobble angles. It is found that, even if the UAV wobbles slightly, the channel temporal correlation will be significantly affected. Numerical results show that the channel CF will decrease rapidly with the increase of the amplitudes of wobble angles and the carrier frequency. Therefore, the coherence time of millimeter wave (mmWave) band is significantly less than that of sub-6 GHz band. The consistency of simulation results and measurement results in published papers ensures the availability of the proposed model. For the MUAVs scenario, when the distance between different UAVs is much greater than the wavelength, the A2G channels between different UAVs and user equipment (UE) on the ground are not correlated to each other, and the temporal auto-correlation function (ACF) of each UAV is the same as that of the SUAV scenario. This work contributes to the theoretical exploration and system design of A2G communication based on UAV.

The aim of this paper is to present a groundwork on the delay-minimized routing problem in a vehicular ad-hoc network (VANET) where some of the vehicles are equipped with full-duplex (FD) radios. We first give the generalized delay calculation model for a multi-hop path, and prove that the Dijkstra algorithm is unable to get the delay-minimized routing path from source to destination. Then we propose two routing methods: graph-based method and deep reinforcement learning (DRL)-based method. In the graph-based method, the network topology is reformulated as an equivalent graph and then an evolved-Dijkstra algorithm is proposed. In the DRL-based method, the deep Q network (DQN) is employed to learn the shortest end-to-end path, wherein the delay is modeled as the rewards for routing actions. The graph-based method can achieve the exact minimum end-to-end delay, while the DRL-based method is more feasible due to its acceptable complexity. Finally, extensive simulations demonstrate that the DRL-based approach with proper hyper-parameters can achieve near minimum end-to-end delay, and the achieved delay has a notably decline as the number of FD nodes increases.

Accurate traffic flow forecasting is crucial to improving traffic safety and alleviating road congestion for intelligent transportation network management. Recently, spatial-temporal graph-based deep learning methods have achieving significant performance improvements in traffic flow forecasting. However, they only consider spatial-temporal correlation of traffic network but ignore a mass of semantic correlation. In addition, they need to centralize data for training models, leading to privacy leakage concern. To tackle these problems, we introduce a federated learning-based intelligent traffic flow forecasting model that integrates our proposed spatial-temporal graph-based deep learning model into the devised Multilevel Federated Learning framework(MFL), named MFVSTGNN. This MFL is used to allow data collaboration among different data owners to train an efficient model without sharing their private data, while achieving the trade-off between communication overhead and computation performance. The proposed spatial-temporal graph-based deep learning model is composed of two phases. The first phase utilizes Variational Graph Autoencoder (VGAE) to dynamically generate adjacency matrix that contains both the spatial and semantic dependencies, contributing to preserving valuable information for improving prediction accuracy, and the second phase employs general spatial-temporal graph neural network to conduct prediction. We evaluate the performance of MFVSTGNN with two large-scale traffic datasets from California and Los Angeles County. The experimental results demonstrate the superior performance of MFVSTGNN in reducing communication overhead, and improving prediction accuracy, validating the effectiveness of our proposed model.

The Internet of Medical Things (IoMT) and Artificial Intelligence (AI) have brought unprecedented opportunities to meet massive behavioral data access and personalization requirements for Internet of Behavior (IoB). They facilitate the communication and computing resource allocation to guarantee low delay and energy consumption demands in healthcare. This article presents an improved offloading algorithm for Mobile Edge Computing (MEC) based on Deep Q Network (DQN) and Simulated Annealing (SA) for IoB. Firstly, we analyze the network model and establish a task cost function based on processing delay and energy consumption. Secondly, we define a Distributed Optimization Problem (DOP) to maximize individual utilities and system utility, which is proved to be a potential countermeasure. Thirdly, we conduct Markov modeling for the current offloading strategy-making scheme and define the objectives and constraints of the optimization function. At the same time, the SA is introduced into the DQN Algorithm, which improves the capacity of the algorithm by focusing on the exploration in the early stage and following the experience value in the later stage. From the simulation results, we can see that compared with the traditional scheme, the proposed strategy can maximize the utilization of the system and reduce processing delay and energy consumption.

Vehicular Edge Computing (VEC) is a promising new paradigm that has attracted much attention in recent years, which can enhance the storage and computing capabilities of vehicular networks to provide users with low latency and high-quality services. Due to the open access and unreliable wireless channels, some appropriate security measures should be implemented in the VEC to ensure information security. However, the operation of the security mechanism dominates supererogatory computing resources, thus affecting the performance of VEC systems. The scarcity of computation and energy resources of the vehicles conflicts with the requirement of tasks for time delay and information security. In this paper, taking the driving velocity and position of the vehicles, the number of lanes, the model and density of the attackers, and security strength into consideration, we formulate a max-min optimization problem to jointly optimize offloading decision, transmit power, task computation frequency, encryption computation frequency, edge computation frequency, and block length to obtain optimal secure information capacity and local computation delay. The formulated optimization problem is a mixed integer nonlinear programming (MINLP), which is intractable. We apply the generalized benders decomposition (GBD)-based method to solve it. The simulation results show that our proposed algorithms have convergence and effectiveness and achieve fairness among vehicles on the road.

A lack of well-designed security solutions within the millimeter-Wave (mmWave) cellular vehicle-to-everything (V2X) communications significantly impedes the development of applications within the intelligent transportation system. Cooperative jamming is envisioned as a potential technology that can enhance physical layer security performance for plane networks by selectively choosing jammers from the perspective of the legitimate receiver. We propose a blockage-and-power-based jammer selection strategy to address potential security pitfalls in a mmWave cellular V2X network. With the help of jammers whose interference power falls within the acceptance range of legitimate receivers, transmission confidentiality is secured simultaneously without escalating the instability of connections caused by the time-varying nature of V2X networks. We derive the theoretical expression of secrecy outage probability and secrecy throughput based on our preliminary analysis of association probability from the stochastic geometry approach. Numerical results demonstrate that the proposed secure transmission scheme outperforms other cooperative jamming schemes in terms of secrecy throughput.

Running computer vision applications, such as 3-D simultaneous localization and mapping (SLAM), on mobile devices requires low-latency responses and a massive amount of computation. Edge computing has been introduced to move Cloud features closer to end users, providing necessary computing and network resources for end devices. The heterogeneous edge devices, with different hardware architectures (e.g., CPUs and GPUs) and runtime environments, provide diverse resources to support processing tasks from end devices, resulting in different costs and quality of services. How to partition these computing tasks and distribute them over these heterogeneous hardware nodes is still an open research question. Considering these inherently heterogeneous hardware architectures, new approaches for service orchestration and task scheduling are required to meet the service-level agreement and reduce the overall cost of the system (e.g., facility utilization cost). This article presents a system framework, EDGE VISION, for computer vision applications partitioning and orchestration on heterogeneous edge computing platforms considering both CPUs and GPUs. EDGE VISION abstracts the heterogeneous hardware resources and the task runtime environments and divides the application into separate tasks to be orchestrated and deployed into the heterogeneous edge nodes. We also propose two scheduling algorithms in our framework, minimum latency task scheduling and minimum cost task scheduling, aiming to minimize the processing latency and the overall system cost. We evaluate our framework by implementing the edge-based 3-D SLAM application in our real testbed with ten heterogeneous edge devices. Evaluations show that EdgeVision can efficiently minimize the processing latency and the system overall cost and achieve up to 30% decrease in task processing latency and 15% more cost saving compared to the State-of-the-Art baselines.

Millimeter wave (Mmwave) communication can realize high rate service for the upcoming Internet of Things (IoT) networks. Although directional multiantenna gains can help enhance security, randomly distributed eavesdroppers can still intercept confidential messages by residing in both the main-lobe and side-lobe areas of the beam signal. Considering the unique propagation features of mmWave, this article explores the potential of physical layer security in mmWave multiple-input–multiple-output (MIMO) systems. We propose an artificial noise (AN)-aided capacity threshold on–off secure transmission scheme to resist the eavesdropping threat. Taking into account the influence of mmWave channel characteristics, random blockage, and multiantenna gains, we first derive the closed-form expressions of transmission probability (TP) and secrecy outage probability (SOP) in a noncolluding eavesdropping scenario. Then, the lower bound of SOP with AN and closed-form expression of SOP without AN is derived in a colluding eavesdropping scenario. Theoretical analysis evaluates the impacts of various system parameters on secrecy performance and verifies the effects of AN interference on inhibiting side-lobe eavesdropping. Simulation results validate the theoretical results and indicate that the combination of capacity threshold on–off transmission scheme, AN interference, and multiantenna directional gains can effectively reduce the security threats of mmWave MIMO systems. Besides, the optimal power allocation ratio of AN in noncolluding scenarios is demonstrated and its rule is summarized, which depends on whether legitimate communication links are in blockage.

Network virtualization (NV) is a technology with broad application prospects. Virtual network embedding (VNE) is the core orientation of VN, which aims to provide more flexible underlying physical resource allocation for user function requests. The classical VNE problem is usually solved by the heuristic method, but this method often limits the flexibility of the algorithm and ignores the time limit. In addition, the partition autonomy of physical domain and the dynamic characteristics of virtual network request (VNR) also increase the difficulty of VNE. This article proposed a new type of VNE algorithm, which applied reinforcement learning (RL) and graph neural network (GNN) theory to the algorithm, especially the combination of graph convolutional neural network (GCNN) and RL algorithm. Based on a self-defined fitness matrix and fitness value, we set up the objective function of the algorithm implementation, realized an efficient dynamic VNE algorithm, and effectively reduced the degree of resource fragmentation. Finally, we used comparison algorithms to evaluate the proposed method. Simulation experiments verified that the dynamic VNE algorithm based on RL and GCNN has good basic VNE characteristics. By changing the resource attributes of physical network and virtual network, it can be proved that the algorithm has good flexibility.

In this paper, a novel message authentication model using the same key over wiretap channel is proposed to achieveinformation-theoretic security. Specifically, in the proposed model, there is a discrete memoryless channelW1:X→Ybetween transmitter Alice and receiver Bob, while an attacker Oscar is connected with Alice via discrete memoryless channelW2:X→Z. Alice encodes messageMto codeword (S,Xn), using an encoding function with secret keyK. Then,Sis sent to Bob over a one-way noiseless channel (fully controlled by Oscar), andXnis sent over the wiretap channel, sayX→(Y,Z). Building on this model, a new message authentication scheme is proposed. The scheme incorporates a secure channel coding, which uses random coding techniques to detect man-in-the-middle (MITM) attacks. The authentication channel capacity is studied in a specific channel model whenW2is not less noisy thanW1. We theoretically demonstrate that the authentication channel capacity is much larger than the secrecy capacity, since Bob does not need to recover information transmitted over the noisy channel.

The global healthcare industry and artificial intelligence have promoted the development of the diversified intelligent healthcare applications. Internet of Things (IoT) will play an important role in meeting the high throughput requirements of diversified intelligent healthcare applications. However, the mobile IoT-enabled healthcare networks are diverse and open, the healthcare big data transmission is vulnerable to a potential attack, which can cause network outages and serious healthcare security issues. To process the complex healthcare security event in real time, security performance prediction is critical for mobile IoT-enabled healthcare networks. In this article, we first analyze the security performance, and derive the novel expressions for the security performance in a closed form. Then, to analyze the security performance in real time, a security performance intelligent prediction algorithm is proposed. An improved convolutional neural network (CNN) model is designed, which combines the four-layer convolution and a four-branch inception block, and can adopt different convolution kernels in the same layer. The four-branch inception block can increase the width of the CNN while reducing the parameters. The improved CNN model can not only increases the width of the CNN, extract different sizes of healthcare data features, but also increases the adaptability to the nonlinear healthcare big data. Compared with different methods, the proposed intelligent algorithm can obtain better security performance prediction. In particular, for prediction precision, the proposed intelligent algorithm is increased by 20%.

Recently, recommender systems are applied to provide personalized recomendation for healthcare wearables. However, due to the sparsity problem, traditional recommendation algorithms are difficult to achieve desired performance. Considering that consumers often buy and rate other types of items on E-commerce platforms, we can leverage significant information in the auxiliary domains to improve the recommendation performance of healthcare wearables, which can be regarded as cross-domain recommendation. However, traditional cross-domain recommendation model cannot fully represent user's characteristics and fail to consider the leaks of original auxiliary domain ratings during the information transfer process. To overcome the two shortcomings, this paper proposes a Privacy-Preserving Cross-Domain Healthcare Wearables Recommendation algorithm (PPCDHWRec). Firstly, user's characteristics are divided into domain-dependent features and domain-independent features, which complement each other and fully depict the user's characteristics. Secondly, inspired by the latent factor model, we factorize the original rating information of each auxiliary domain by Funk-SVD and Orthogonal Nonnegative Matrix Tri-Factorization (ONMTF) model, to obtain user's domain-dependent and domain-independent features, respectively. Finally, the Factorization Machine algorithm is used to fuse the obtained user's features with the target domain information to provide the recommendation results. By hiding the item latent factors obtained in the factorization process, PPCDHWRec ensures that the original information cannot be inferred from the transferred user hidden vector. Hence, PPCDHWRec is a privacy-preserving recommendation model. Experiments on two groups of auxiliary domains, having high and low correlations with target domain, show the effectiveness of PPCDHWRec.

Considering the high mobility and uneven distribution of vehicles, an efficient routing protocol should avoid that the sent packets are forwarded within road segments with ultra-low density or serious data congestion in vehicular networks. To this end, in this paper, we propose a Traffic aware and Link Quality sensitive Routing Protocol (TLRP) for urban Internet of Vehicles (IoV). First, we design a novel routing metric, i.e., Link Transmission Quality (LTQ), to account for the impact of the number, quality and relative positions of communication links along a routing path on the network performance. Then, to adapt to the dynamic characteristics of IoV, a road weight evaluation scheme is presented to assess each road segment using the real-time traffic and link information quantified by the LTQ. Next, the path with the lowest aggregated weight is selected as the routing candidate. Extensive simulations demonstrate that our proposed protocol achieves significant performance improvements compared to the state-of-the-art protocol MM-GPSR, the typical junction-based scheme E-GyTAR, and the classic connectivity-based routing iCAR, in terms of packet delivery ratio and average transmission delay.

Space-air-ground integrated network (SAGIN), as an integration of interconnected space, air, and ground network segments, is expected to see prevalent usage as part of intelligent transportation systems (ITS), providing an enhanced service provision in terms of coverage, flexibility and reliability. However, restricted by the limited and unbalanced network resources, the efficiency and security of the underlying connectivities of SAGIN are of utmost concern for ITS applications. In this paper, a weighted fractional Fourier transform (WFRFT) based parallel complex spreading (PCS) approach is proposed to improve the communication efficiency and security of SAGIN at the physical (PHY-) layer. The concept of WFRFT along with the direct sequence spread spectrum technology establish the security kernel of the proposed scheme. The practicability of the complex-valued WFRFT-spreading architecture is verified by studying the correlation properties of the WFRFT-spreading signals. Taking advantages of the signal uniqueness of WFRFT, the proposed scheme is capable of providing more flexibility in signal characteristic control. Moreover, the complex-valued WFRFT-spreading processing makes the proposed scheme inherently robust against the large Doppler shift distortions in SAGIN. Simulation results demonstrate the superiority of the proposed WFRFT-PCS scheme in terms of communication efficiency and PHY-layer security. Finally, as a proof of concept, an all-digital FPGA prototype system is designed to show the practicability and the performance enhancement of the proposed scheme.

As a distributed deep learning paradigm, federated learning (FL) provides a powerful tool for the accurate and efficient processing of on-board data in vehicular edge computing (VEC). However, FL involves the training and transmission of model parameters, which consumes the vehicles’ precious energy resources and takes up much time. It is a departure from many applications with severe real-time requirements in VEC. And the capabilities and data quality of each vehicle are distinct that will affect the performance of training the model. Therefore, it is crucial to select the appropriate vehicles to participate in learning tasks and optimize resource allocation under learning time and energy consumption constraints. In this paper, taking the vehicle position and velocity into consideration, we formulate a min-max optimization problem to jointly optimize the on-board computation capability, transmission power, and local model accuracy to achieve the minimum cost in the worst case of FL. Specifically, we propose a greedy algorithm to select vehicles with higher image quality dynamically, and it keeps the system’s overall cost to a minimum in FL. The formulated optimization problem is a nonlinear programming problem, so we decompose it into two subproblems. For the resource allocation problem, we use the Lagrangian dual problem and the subgradient projection method to approximate the optimal value iteratively. For the local model accuracy problem, we develop an adaptive harmony algorithm for heuristic search. The simulation results show that our proposed algorithms have well convergence and effectiveness and achieve a tradeoff between cost and fairness.

Traditional ground wireless communication networks cannot provide high-quality services for artificial intelligence (AI) applications such as intelligent transportation systems (ITS) due to deployment, coverage and capacity issues. The space-air-ground integrated network (SAGIN) has become a research focus in the industry. Compared with traditional wireless communication networks, SAGIN is more flexible and reliable, and it has wider coverage and higher quality of seamless connection. However, due to its inherent heterogeneity, time-varying and self-organizing characteristics, the deployment and use of SAGIN still faces huge challenges, among which the orchestration of heterogeneous resources is a key issue. Based on virtual network architecture and deep reinforcement learning (DRL), we model SAGIN’s heterogeneous resource orchestration as a multi-domain virtual network embedding (VNE) problem, and propose a SAGIN cross-domain VNE algorithm. We model the different network segments of SAGIN, and set the network attributes according to the actual situation of SAGIN and user needs. In DRL, the agent is acted by a five-layer policy network. We build a feature matrix based on network attributes extracted from SAGIN and use it as the agent training environment. Through training, the probability of each underlying node being embedded can be derived. In test phase, we complete the embedding process of virtual nodes and links in turn based on this probability. Finally, we verify the effectiveness of the algorithm from both training and testing.

With artificial intelligence prevailing in intelligent transportation system, pavement crack detection with deep learning has aroused wide attentions in both academia and transportation sector. Nevertheless, it still remains a challenge to accomplish crack detection due to the complexity in pavement background. Motivated by latest advents in computer vision research, a fractional integral-based filtering method is advocated to remove pavement noise, and a fractal dimension estimation method has also emerged to present shape feature at pixel level, with the multi-scale feature architecture. Therefore, we try to propose a deep learning method, integrating fractional integral with fractal dimension, for crack detection in transportation service management. Firstly, the crack image is taken as input in the bottom-up architecture to extract fractal dimension on multi-scale levels, and a per-level feature unit is built to incorporate maps to make context information flow. Secondly, after fed into a convolutional filter for dimension resizing, all the resized feature maps are next fused at each level to comprise a group network. Finally, extensive experiments are executed on different crack datasets, exhibiting that the proposed method surpasses existing cutting-edge ones in terms of both generalizability and accuracy, with the benefits from not only fractional integral filtering, but also multi-scale fractal dimension features.

As a key use case of Industry 4.0 and the Smart City, the Internet of Vehicles (IoV) provides an efficient way for city managers to regulate the traffic flow, improve the commuting performance, reduce the transportation facility cost, alleviate the traffic jam, and so on. In fact, the significant development of Internet of Vehicles has boosted the emergence of a variety of Industry 4.0 applications, e.g., smart logistics, intelligent transforation, and autonomous driving. The prerequisite of deploying these applications is the design of efficient data dissemination schemes by which the interactive information could be effectively exchanged. However, in Internet of Vehicles, an efficient data scheme should adapt to the high node movement and frequent network changing. To achieve the objective, the ability to predict short-term traffic is crucial for making optimal policy in advance. In this article, we propose a novel data dissemination scheme by exploring short-term traffic prediction for Industry 4.0 applications enabled in Internet of Vehicles. First, we present a three-tier network architecture with the aim to simply network management and reduce communication overheads. To capture dynamic network changing, a deep learning network is employed by the controller in this architecture to predict short-term traffic with the availability of enormous traffic data. Based on the traffic prediction, each road segment can be assigned a weight through the built two-dimensional delay model, enabling the controller to make routing decisions in advance. With the global weight information, the controller leverages the ant colony optimization algorithm to find the optimal routing path with minimum delay. Extensive simulations are carried out to demonstrate the accuracy of the traffic prediction model and the superiority of the proposed data dissemination scheme for Industry 4.0 applications.

Federated learning is a distributed machine learning technology that can protect users’ data privacy, so it has attracted more and more attention in the industry and academia. Nonetheless, most of the existing works focused on the cost optimization of the entire process, while the cost of individual participants cannot be considered. In this article, we explore a min-max cost-optimal problem to guarantee the convergence rate of federated learning in terms of cost in wireless edge networks. In particular, we minimize the cost of the worst-case participant subject to the delay, local CPU-cycle frequency, power allocation, local accuracy, and subcarrier assignment constraints. Considering that the formulated problem is a mixed-integer nonlinear programming problem, we decompose it into several sub-problems to derive its solutions, in which the subcarrier assignment and power allocation are obtained by utilizing the Lagrangian dual decomposition method, the CPU-cycle frequency is obtained by a heuristic algorithm, and the local accuracy is obtained by an iteration algorithm. Simulation results show the convergence of the proposed algorithm and reveal that the proposed scheme can accomplish a tradeoff between the cost and fairness by comparing the proposed scheme with the existing schemes.

Mobile-edge computing (MEC) is a promising technology, which allows reducing latency and energy consumption, thereby making the user experience better. Although MEC can support various types of services, differentiated Quality-of-Service (QoS) requirements bring difficulties and challenges to the allocation of radio resources and computing resources of the MEC system. In this article, we jointly optimize subchannel allocation, as well as the local central processing unit (CPU) speed scaling, user association, subcarrier assignment, power allocation, and video quality decision for MEC systems to study the total cost saving problem. Considering the traffic variations, we develop an online algorithm by using the Lyapunov optimization technique to solve this problem, referred to as dynamic subchannel allocation and resource allocation (DSARA). Particularly, the proposed DSARA algorithm only needs to track the state of the current network without requiring any prior knowledge. Besides, we prove that our proposed algorithm can asymptotically achieve the minimum total cost value (such as minimizing the power consumption and maximizing quality satisfaction). Simulation results show that the DSARA can achieve a good tradeoff between the total cost and delay, and outperforms the existing schemes in terms of the total cost expenditure.

Mobile-edge computing (MEC) plays a significant role in enabling diverse service applications by implementing efficient data sharing. However, the unique characteristics of MEC also bring data privacy and security problem, which impedes the development of MEC. Blockchain is viewed as a promising technology to guarantee the security and traceability of data sharing. Nonetheless, how to integrate blockchain into MEC system is quite challenging because of dynamic characteristics of channel conditions and network loads. To this end, we propose a secure data sharing scheme in the blockchain-enabled MEC system using an asynchronous learning approach in this article. First, a blockchain-enabled secure data sharing framework in the MEC system is presented. Then, we present an adaptive privacy-preserving mechanism according to available system resources and privacy demands of users. Next, an optimization problem of secure data sharing is formulated in the blockchain-enabled MEC system with the aim to maximize the system performance with respect to the decreased energy consumption of MEC system and the increased throughput of blockchain system. Especially, an asynchronous learning approach is employed to solve the formulated problem. The numerical results demonstrate the superiority of our proposed secure data sharing scheme when compared with some popular benchmark algorithms in terms of average throughput, average energy consumption, and reward.

Blockchain constructs a distributed point-to-point system, which is a secure and verifiable mechanism for decentralized transaction validation and is widely used in financial economy, Internet of Things, large data, cloud computing, and edge computing. On the other hand, artificial intelligence technology is gradually promoting the intelligent development of various industries. As two promising technologies today, there is a natural advantage in the convergence between blockchain and artificial intelligence technologies. Blockchain makes artificial intelligence more autonomous and credible, and artificial intelligence can prompt blockchain toward intelligence. In this paper, we analyze the combination of blockchain and artificial intelligence from a more comprehensive and three-dimensional point of view. We first introduce the background of artificial intelligence and the concept, characteristics, and key technologies of blockchain and subsequently analyze the feasibility of combining blockchain with artificial intelligence. Next, we summarize the research work on the convergence of blockchain and artificial intelligence in home and overseas within this category. After that, we list some related application scenarios about the convergence of both technologies and also point out existing problems and challenges. Finally, we discuss the future work.

Cooperative content dissemination allows vehicles to directly retrieve content from each other by vehicle-to-vehicle (V2V) communications. It can release the downlink traffic pressure caused by repetitive of popular content downloads. A critical but open issue is how to motivate vehicles to participate in content dissemination. Most of the existing incentive mechanisms are proposed under a centralized architecture that suffers from the single point attack and trustless third-platform. In this paper, we propose a decentralized incentive mechanism for cooperative content dissemination in vehicular networks. By introducing the Directed Acyclic Graph (DAG) based blockchain technology, a decentralized architecture is proposed, which can improve incentive efficiency and reliability by removing the third-platform. Next, using the contract theory, the vehicles are divided into different types according to their route feature, and a series of contracts are designed for different types of vehicles to provide suitable incentives, as well as maximize the content generators' profit. Simulation results demonstrate the effectiveness and efficiency of our solution.

The role of fog computing in future vehicular networks is becoming significant, enabling a variety of applications that demand high computing resources and low latency, such as augmented reality and autonomous driving. Fog-based computation offloading and service caching are considered two key factors in efficient execution of resource-demanding services in such applications. While some efforts have been made on computation offloading in fog computing, a limited amount of work has considered joint optimization of computation offloading and service caching. As fog platforms are usually equipped with moderate computing and storage resources, we need to judiciously decide which services to be cached when offloading computation tasks to maximize the system performance. The heterogeneity, dynamicity, and stochastic properties of vehicular networks also pose challenges on optimal offloading and resource allocation. In this paper, we propose an intelligent computation offloading architecture with service caching, considering both peer-pool and fog-pool computation offloading. An optimization problem of joint computation offloading and service caching is formulated to minimize the task processing time and long-term energy utilization. Finally, we propose an algorithm based on deep reinforcement learning to solve this complex optimization problem. Extensive simulations are undertaken to verify the feasibility of our proposed scheme. The results show that our proposed scheme exhibits an effective performance improvement in computation latency and energy consumption compared to the chosen baseline.

In privacy-preserving machine learning, individual parties are reluctant to share their sensitive training data due to privacy concerns. Even the trained model parameters or prediction can pose serious privacy leakage. To address these problems, we demonstrate a generally applicable Distributed Privacy-Preserving Prediction (DPPP) framework, in which instead of sharing more sensitive data or model parameters, an untrusted aggregator combines only multiple models' predictions under provable privacy guarantee. Our framework integrates two main techniques to guarantee individual privacy. First, we introduce the improved Binomial Mechanism and Discrete Gaussian Mechanism to achieve distributed differential privacy. Second, we utilize homomorphic encryption to ensure that the aggregator learns nothing but the noisy aggregated prediction. Experimental results demonstrate that our framework has comparable performance to the non-private frameworks and delivers better results than the local differentially private framework and standalone framework.

Recently, vehicular ad hoc networks (VANETs) have been attracting significant attention for their potential for guaranteeing road safety and improving traffic comfort. Due to high mobility and frequent link disconnections, it becomes quite challenging to establish a reliable route for delivering packets in VANETs. To deal with these challenges, an artificial spider geographic routing in urban VAENTs (ASGR) is proposed in this paper. First, from the point of bionic view, we construct the spider web based on the network topology to initially select the feasible paths to the destination using artificial spiders. Next, the connection-quality model and transmission-latency model are established to generate the routing selection metric to choose the best route from all the feasible paths. At last, a selective forwarding scheme is presented to effectively forward the packets in the selected route, by taking into account the nodal movement and signal propagation characteristics. Finally, we implement our protocol on NS2 with different complexity maps and simulation parameters. Numerical results demonstrate that, compared with the existing schemes, when the packets generate speed, the number of vehicles and number of connections are varying, our proposed ASGR still performs best in terms of packet delivery ratio and average transmission delay with an up to 15% and 94% improvement, respectively.

Due to the random delay, local maximum and data congestion in vehicular networks, the design of a routing is really a challenging task especially in the urban environment. In this paper, a distributed routing protocol DGGR is proposed, which comprehensively takes into account sparse and dense environments to make routing decisions. As the guidance of routing selection, a road weight evaluation (RWE) algorithm is presented to assess road segments, the novelty of which lies that each road segment is assigned a weight based on two built delay models via exploiting the real-time link property when connected or historic traffic information when disconnected. With the RWE algorithm, the determined routing path can greatly alleviate the risk of local maximum and data congestion. Specially, in view of the large size of a modern city, the road map is divided into a series of Grid Zones (GZs). Based on the position of the destination, the packets can be forwarded among different GZs instead of the whole city map to reduce the computation complexity, where the best path with the lowest delay within each GZ is determined. The backbone link consisting of a series of selected backbone nodes at intersections and within road segments, is built for data forwarding along the determined path, which can further avoid the MAC contentions. Extensive simulations reveal that compared with some classic routing protocols, DGGR performs best in terms of average transmission delay and packet delivery ratio by varying the packet generating speed and density.

Internet of Vehicles (IoV) has drawn more and more attention. However, due to high vehicle movement and frequent topology changing, it is quite challenging to design an efficient routing protocol in the complex urban IoV. In this paper, we propose a Connectivity aware Transmission quality guaranteed Geographic Routing in urban IoV (CTGR). First, we give the connectivity model in case of disconnected link and the transmission quality (TQ) model when the network is connected, respectively. Then, using the two models, with the assistance of road weight evaluation scheme (RWE), each road segment can be assigned with a suitable weight. Based on the weight information, the road segment can be dynamically selected one by one to comprise the optimized routing path, avoiding the local maximum and data congestion. An improved next hop selection strategy is further proposed to forward the packet along the selected road segment, guaranteeing fast and reliable packet transmission. Simulation results show that our proposed protocol achieves higher packet delivery ratio and lower transmission delay compared with the existing protocols.

Vehicular Ad Hoc Networks (VANETs) have been attracting more and more interest. Designing one efficient routing protocol is one of the most important issues for urban VANETs. However, fast node movement, dynamic topology changes and complicated channel environments make it quite challenging. In this paper, a Delay-aware and Backbone-based Geographic Routing (DBGR) protocol for urban VANETs is proposed. This protocol comprehensively exploits the real-time traffic information in case of link connection and the historical traffic information when the link is disconnected to make a route selection for packet forwarding. Based on the current traffic condition, using the road weight evaluation scheme (RWE), each road segment can be assigned with an appropriate weight associated with the corresponding transmission delay, by which the weight matrix of the network topology can be built. Using the matrix, the optimized route with the minimum delay can be selected. Simulation results show that the proposed protocol outperforms existing protocols in terms of packet delivery ratio and end-to-end delay.

Vehicular Ad Hoc Networks (VANETs) have been attracting more and more attention. However, due to the fast movement of vehicles and dynamic topology change, designing an efficient routing protocol in the complex urban environment is quite challenging. In this paper, an Intersection-based Geographic Routing with Transmission Quality guaranteed in Urban VANETs (IGRTQ) is proposed. As a selection guidance of the best route, each road segment is assigned with a weight based on the collected information related to the delay and connectivity of each road segment. Based on the weight information, the road segment can be dynamically selected one by one to form the optimized routing path, avoiding the local maximum and data congestion. An improved greedy strategy is further proposed to forward the packet along the selected road segment, ensuring the fast and reliable packet transmission. Simulation results show that the proposed protocol provides higher packet delivery ratio and lower end-to-end delay compared to the existing protocols.

In recent years, the rapid improvement of sensor and wireless communication technologies powerfully impels the development of advanced cooperative driving systems, generating the demands to form the Internet of Vehicles (IoV). With the assistance of cooperative communication among vehicles, the road safety can be greatly enhanced in the IoV. In this paper, we propose a cooperative driving scheme for vehicles at intersections in the IoV. First, the driver’s intention is modeled by the BP neural network trained with driving dataset. Then, the identified intention is used as the control matrix of the Kalman filter model, by which the vehicle trajectory can be predicted. Finally, by collecting the information of vehicles’ trajectories at the intersections, we develop a collision probability evaluation model to reflect the conflict level among vehicles at intersections. Through obtained collision probability, the driver or the autonomous control unit can determine the next step to avoid the possible collisions. Numerical results show that our proposed scheme has high accuracy in terms of driver’s intention identification, trajectory prediction and collision probability evaluation.

Vehicular Ad Hoc Networks (VANETs) have been paid significant increasing attentions. However, the inherent characteristics of VANETs, such as the dynamic network topology, uneven distribution of vehicles, frequent disconnections etc., bring great challenges for designing an efficient routing protocol in urban environment. In this paper, we first introduce a new routing metric called link transmission quality (LTQ), which exploits the effect of links' position along the routing path and also takes both transmission cost and forwarding reliability into consideration, to evaluate the performance of a multi-hop link. Next, with the help of back-bone nodes selected based on specified criterions, road segments are assigned different weights according to the value of calculated LTQ. Finally, a novel geographic routing protocol based on LTQ in urban VANETs, named as LTQGR, is proposed, where the routing path with the lowest aggregated weights is selected for packets transmission. When data packets are forwarded along the optimized route, one forwarding strategy is presented to guarantee the efficient transmission. Simulation results show that our proposed LTQGR outperforms GPSR and another ETX-based protocol HLAR in terms of the average transmission delay, packet delivery ratio and routing overheads.