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.

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.

Traditional reinforcement learning methodologies, primarily based on multi-layer perceptron (MLP) architectures, require extensive, fully connected layers for complex nonlinear representations. Such an approach increases computational demands and enhances the likelihood of model overfitting. In this paper, we present an innovative reinforcement learning approach, leveraging the Kolmogorov-Arnold Networks (KAN) framework, to enhance decision-making processes and resource management strategies in edge networks. We develop a KAN-based hybrid proximal policy optimization algorithm (KANH-PPO) to address this issue. This algorithm effectively addresses the challenge of hybrid action spaces within edge networks, which include discrete action spaces characterized by computational offloading decisions and continuous action spaces characterized by power allocation. Furthermore, the KANH-PPO algorithm innovatively integrates the KAN architecture, significantly reducing the number of training parameters and enhancing the algorithm's fitting capability and overall performance. Simulation experiments indicate that our proposed KANH-PPO algorithm outperforms the benchmark algorithm in terms of convergence speed and edge network system power, and it requires significantly fewer training parameters than benchmark algorithms. This helps to reduce the power consumption of communication and promote the development of green communication.

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.

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.

Vehicular Social Networks (VSNs), as an innovative mobile communication system, significantly enhance the driving experience and improve urban traffic management efficiency. To address the privacy issues that arise from the use of public channels in VSNs, fine-grained access control should be ensured. Nevertheless, existing schemes still face some practical challenges in the aspects of data source identification, key escrow, and dynamic vehicle management. Therefore, this paper proposes a registration-based bilateral fine-grained access control scheme (RBF-AC) in VSNs. Specifically, RBF-AC allows service providers to select target vehicles and offer tailored services, while allowing vehicles to identify the most suitable service providers based on their needs, and all of which are realized in a fine-grained level. Meanwhile, service providers and vehicles are capable of locally generating their own private and public keys without relying on any fully trusted authority. RBF-AC also keeps high flexibility and enables the vehicles to join, leave and update their attributes in a dynamic manner. Additionally, outsourced verification and decryption are provided to minimize the computation cost for vehicles. We present formal security proofs to validate the security of RFB-AC. The performance evaluation illustrates the practical applicability of RBF-AC in VSNs.

Metaverse is considered to be the evolution of the next-generation networks, providing users with experience sharing at the intersection between physical and digital. Augmented reality (AR) is one of the primary supporting technologies in the Metaverse, which can seamlessly integrate real-world information with virtual world information to provide users with an immersive interactive experience. Extraordinarily, AR has brought new opportunities for assisting safe driving. Nevertheless, achieving efficient execution of AR tasks and increasing system revenue are the main challenges faced by the Metaverse’s AR in-vehicle applications. To address these challenges, in this paper, we are the first to propose an efficient resource allocation framework for AR-empowered vehicular edge Metaverse to improve system utility. For this purpose, we formulate an optimization problem featuring multidimensional control to concurrently maximize data utility at the Metaverse operator side and minimize energy consumption at the vehicles’ side, which jointly considers the computational resource allocation on the Metaverse server, and AR vehicles’ CPU frequency, transmit power, and computation model size. Notwithstanding, the major impediment is how to design an efficient algorithm to obtain the solutions of the optimization. Wherefore, we do this by decoupling the optimization variables. We first derive the optimal computation model size by the binary search, followed by obtaining the optimal power allocation by the bisection method and finding a closed-form solution to the optimal CPU frequency of AR vehicles, and finally, attain the optimal allocation of computational resource on the server by the Lagrangian dual method. To estimate the performance of our proposed scheme, we establish three baseline schemes as a comparison, and simulation results manifest that our proposed scheme can balance the operator’s reward and the energy consumption of vehicles.

As an emerging class of internet of vehicles, vehicular social networks (VSNs) provide passengers, drivers, and vehicles with extensive data sharing services to improve traffic congestion and road safety. However, the insecure transmissions of shared data may disclose sensitive information, such as private data, location, and driving route. Although attribute-based encryption (ABE) is a promising technology to enable secure data sharing, the existing ABE solutions applied to VSNs encounter three-fold deficiencies: (1) the shared data stored in vehicular cloud server would be leaked in the event of key compromise; (2) relying on one or more fully trusted entities to generate keys for vehicles through secure channels; (3) private information leakage and misbehavior of data user vehicles are neglected. Motivated by these challenges, this paper proposes a puncturable registered ABE scheme called PR-ABE for VSNs with enhanced security and practicality. To be specific, our PR-ABE achieves flexible access control and precise data deletion. The former ensures that only registered vehicles with authorized attributes can obtain the shared data. The latter prevents data disclosure when key compromise happens. Meanwhile, PR-ABE enables vehicles to generate keys independently and eliminates the need for any fully trusted authority. In addition, hidden policy and traceability are fulfilled in PR-ABE to protect private information and deal with malicious vehicles, respectively. Finally, the rigorous security proof and performance evaluation demonstrate that PR-ABE is a practical and efficient solution.

With the continuous development of digitization evolutions, vehicular digital twin networks (VDTNs) facilitate traffic data and optimization results to be exchanged between the vehicle and digital twin as well as shared among a group of digital twins. However, the data exchange and group sharing processes take place in real-time over public communication channels, which suffer from various security and privacy threats. Key agreement technologies are promising to enable secure data communications for entities, but the existing key agreement schemes generally fail to fulfill the requirements of synchronization, privacy, and entity management for VDTNs. Therefore, we propose a blockchain-assisted privacy-preserving and synchronized key agreement scheme for VDTNs. In the proposed scheme, the anonymous vehicle and digital twin can negotiate a secret session key in the case of synchronization to achieve secure data exchange. Meanwhile, digital twins are capable of utilizing synchronized state information to dynamically establish a common group encryption key but hold individual decryption keys, which guarantee the security of group sharing. Additionally, the proposed scheme is able to protect identity privacy and manage vehicles and digital twins with the assistance of blockchain and smart contract. The security analysis demonstrates that the proposed scheme provides security and privacy assurances for VDTNs. The performance evaluation indicates that it has excellent expressions in terms of efficiency, practicality, and smart contract consumption.

Decentralized identity (DID) is rapidly emerging as a promising alternative to centralized identity infrastructure, offering numerous real-world applications. However, existing DID systems are confronted with trust concerns, as any distributed node can act as a credential issuer and be considered trusted, which is impractical. Effective trust management (TM) protocols are critical for system trustworthiness but face two primary challenges: preserving user feedback privacy to meet regulation requirements and building resilience against trust attacks to prevent manipulation. While privacy-preserving TM protocols effectively safeguard sensitive data, they often obscure feedback, hindering anomaly detection and complicating efforts to counter trust attacks. To address these issues, we propose DidTrust, a novel decentralized identity trust management protocol that bridges data privacy and resilience to trust attacks. DidTrust features a feedback data privacy preservation protocol that conceals feedback data while maintaining authorizability and verifiability. It also implements countermeasures against cooperative and individual trust attacks, improving detection accuracy without compromising privacy. To improve efficiency, we introduce a feedback compression module for large-scale sparse matrices. Rigorous analysis proves DidTrust to be universally composable (UC) secure under a malicious model, and experiments demonstrate its improved computational and storage efficiency while achieving higher trust attack detection rates compared to BC-Trust.

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.

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.

Federated Learning (FL) has been widely used to train shared machine learning models while addressing the privacy concerns. When deployed in wireless networks, bandwidth resources limitation is a key issue, thereby necessitating device scheduling and bandwidth allocation. It is challenging to carry out device scheduling due to the large combinatorial search space. Besides, the heterogeneous computing capabilities and uncertain channel states of wireless devices complicate the design of a bandwidth allocation method. In this paper, we propose a joint device scheduling and bandwidth allocation framework for implementing FL in wireless networks. Specifically, deep reinforcement learning (DRL) is employed to conduct device scheduling. To this end, the state space, action space, and reward function of DRL are carefully defined for a typical FL system. Long short-term memory (LSTM) is adopted as the DRL agent to analyze the sequential input data. Given the scheduled devices of each global iteration, the proposed bandwidth allocation method aims to minimize the weighted sum of the time delay and energy consumption. Numerical experiments on both independent and identically distributed (IID) and non-IID datasets demonstrate that the proposed framework enables FL to reach the desired accuracy with low time delay and energy consumption.

Compared with cloud computing, edge computing can provide users with computing services that lower response latency and reduce bandwidth pressure, enabling jobs to be processed in the proximity of users. Due to diverse user requirements and limited network resources, it is crucial to guarantee user experience through efficient task scheduling. However, the complexity of network environments and the inherent dependencies between different tasks from each job make it challenging. Given the dynamicity of network conditions, computation resources and service requirements in wireless networks, we have provided a two-level elastic scheduling scheme for dependent task processing. First, a novel scheduling architecture is presented to decouple executor deployment and task assignment, facilitating the increase of resource utilization and the flexibility and elasticity of task scheduling. Then, an optimization problem has been formulated to minimize the execution delay of all jobs with consideration of the characteristics of network networks and task dependencies. After that, an enhanced first come first serve strategy has been employed to make task assignments based on the devised task sorting algorithm. Finally, extensive simulations have been done to evaluate the performance of our proposed algorithm, and the results have shown that our proposed algorithm performs better than benchmark algorithms.

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.

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.

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.

In the paradigm of ubiquitous edge computing, with those advantages, e.g., high mobility, fast response, flexibility and controllability, and low cost of use, Unmanned Aerial Vehicles (UAVs) could be used not only as relays to assist with data collection, but also as computing power nodes to process uncomplicated computational workloads from ground users. Especially, UAVs could be employed to provide alternative computing power resources in field, lake, post-disaster and other complex regional environments. In this paper, to address the issue of computing power scheduling in UAVs empowered aerial computing systems, a scenario where multiple UAVs from the same departure station cooperatively fly over hovering points and achieve the data collection and computation in a decentralized manner is investigated. Nevertheless, due to limited onboard battery capacities of UAVs and diverse service requests of ground users, it is necessary to optimize energy efficiency and service fairness for improving mission execution capabilities of UAVs and the quality of service (QoS) experienced by ground users, and a joint optimization problem of energy efficiency and service fairness is formulated. Through considering complex coupling associations among the departure station, flight paths and hovering points of UAVs, the problem is investigated from the trajectory planning of UAVs and the location planning for both the departure station and hovering points. Proving investigations to be Markov decision processes (MDP), multi-agent cooperation approaches are proposed as promising solutions, and simulation results have been shown to demonstrate that the performance achieved by the proposal outperforms that achieved by schemes commonly used in literatures.

Vehicular digital twin networks (VDTNs) offer great opportunities for driver safety enhancements. By leveraging digital twin (DT) technology, VDTNs can collect and analyze traffic data to optimize driving routes, and allow the out-of-field vehicles to share traffic data via their DTs. However, the real-time data sharing process over a public channel raises concerns about security and privacy. Existing data sharing schemes cannot be directly adopted for VDTNs because they rarely consider dual (data and identity) privacy, synchronization, and flexibility, while also imposing a significant cost on resource-limited entities. To address these challenges, we propose a secure and flexible data sharing scheme with dual privacy protection for VDTNs. In the proposed scheme, a signature of knowledge protocol is developed for protecting the vehicle’s real identity and ensuring authentication, smart contract algorithms are designed to assist in realizing accountability, and a verification control mechanism is devised for allowing the vehicle to flexibly share the traffic data. Additionally, DT with consistent states is capable of removing sensitive information from the shared data, which guarantees synchronization and data privacy. The security analysis demonstrates that the proposed scheme is resilient against potential security threats in VDTNs. Furthermore, the performance evaluation indicates that the proposed scheme not only outperforms the state-of-the-art schemes but also achieves feasible blockchain consumption and data authentication delay.

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.

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.

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.

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.

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.

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.

Vehicles are hesitant to upload data to edge servers in vehicle edge computing (VEC) as many vehicle data collected and perceived by various on-board sensors contain sensitive and personal information and lack economic incentive. Instead of free access to shared data, encrypted data trading will alleviate security and privacy concerns and provide an incentive for vehicle owners to share their data. The edge server needs to pay the price in data trading, and reputation management is a great method to help it trade with reliable and available vehicles. In this paper, we propose a multi-armed bandit (MAB)-based reputation management scheme, so the edge servers can select the high reputation vehicles for data trading, which can ensure the credibility and reliability of the data. The encryption scheme is applied to achieve the required transmission security level and defend the rights and interests of the edge server. On the other hand, implementing security measures will consume the computation and communication resources of the vehicles. We formulate an optimization problem that maximizes the revenue of vehicles in data trading under the constraints of time delay, energy consumption, and security level. Simulation results demonstrate that the proposed scheme is effective and efficient for vehicle reputation management, data trading selection, and resource allocation.

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.

As the rapid proliferation of Internet of Things (IoT) and edge computing, large amounts of data are needed to be stored and transmitted in the online storage system. Data deduplication can be adopted to improve communication efficiency and minimize storage space. However, in edge computing, data deduplication brings security and functionality requirements that are still unsatisfied. Most existing schemes are vulnerable to brute-force attacks and single-point attacks. Moreover, they impose a heavy burden on resource-constrained edge nodes and do not support cross-domain deduplication. Blockchain is a promising technology because the programmable smart contract can be utilized to perform cross-domain deduplication and guarantee the traceability of data. In this article, an efficient dynamic cross-domain deduplication scheme in blockchain-enabled edge computing is proposed to solve the above problems. Specifically, the smart contract is employed to assist cross-domain deduplication, which also can reduce the storage pressure of edge nodes. Meanwhile, a hash proof system-based oblivious pseudorandom function is created to reduce the time cost of key generation and achieve the security requirements of resistance to brute-force attacks and single-point attacks. The technology of accumulators is adopted to achieve Proofs of Ownership (PoO), which can prevent duplicate-faking attacks. The security analysis demonstrates that the proposed scheme has a higher security level. The performance evaluation shows that the proposed scheme significantly reduces computation cost and communication overhead, compared with other existing schemes. The smart contract is implemented in the Ethereum test network (i.e., Rinkeby), which shows acceptable gas cost even the functions are called frequently.

Federated learning (FL) is a popular edge learning approach that utilizes local data and computing resources of network edge devices to train machine learning (ML) models while preserving users’ privacy. Nevertheless, performing efficient learning tasks on the devices and achieving longer battery life are primary challenges faced by federated learning. In this paper, we are the first to study the application of heterogeneous computing (HC) and wireless power transfer (WPT) to federated learning to address these challenges. Especially, we propose a heterogeneous computation and resource allocation framework based on a heterogeneous mobile architecture to achieve effective implementation of FL. To minimize the energy consumption of smart devices and maximize their harvesting energy simultaneously, we formulate an optimization problem featuring multidimensional control, which jointly considers time splitting for WPT, dataset size allocation, transmit power allocation and subcarrier assignment during communications, and processor frequency of processing units (central processing unit (CPU) and graphics processing unit (GPU)). However, the major obstacle is how to design a proper algorithm to solve this optimization problem efficiently. For this purpose, we decouple the optimization variables so as to achieve high efficiency in deriving its solution. Particularly, we first compute the optimal processor frequency and dataset size allocation via employing the Lagrangian dual method, followed by finding the closed-form solution to the optimal time splitting allocation, and finally attain the optimal subcarrier assignment as well as transmit power for transmissions through an iteration algorithm. To evaluate the performance of our proposed scheme, we set up four baseline schemes as comparison, and simulation results show that the proposed scheme converges quite fast and better enhance the energy efficiency of the wireless powered FL system compared with the baseline schemes.

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.

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 technology has been widely used in many fields. However, the proof of work (PoW) problem in the mining process of mobile devices requires a large amount of computing resources and energy consumption, which brings huge challenges to mobile devices. Mobile edge computing (MEC) can effectively solve the above problems, allowing mobile devices to offload tasks to edge servers to relieve the pressure of limited computing resources on mobile devices. Nonorthogonal multiple access (NOMA) is good at improving spectrum efficiency, so that the system can accommodate more users. In this paper, we propose a new NOMA-based MEC-enabled blockchain framework. Under the conditions of a given task execution deadline, the decision of offloading, local computing resource allocation, user clustering and admission control, and transmit power control is jointly optimized to minimize the total cost of the system. Since the problem is hard to solve, we decouple it into subproblems for low-complexity solutions. First, we propose two heuristic algorithms to obtain the binary offloading decision and user association, and then closed-form solutions of local resource allocation and transmit power control are obtained under the required delay constraints. Simulation results show that our proposed algorithms perform good in cost reduction compared with other baseline algorithms.

Mobile-edge computing (MEC) is a promising paradigm to improve the quality of computation experience of mobile devices because it allows mobile devices to offload computing tasks to MEC servers, benefiting from the powerful computing resources of MEC servers. However, the existing computation-offloading works have also some open issues: 1) security and privacy issues; 2) cooperative computation offloading; and 3) dynamic optimization. To address the security and privacy issues, we employ the blockchain technology that ensures the reliability and irreversibility of data in MEC systems. Meanwhile, we jointly design and optimize the performance of blockchain and MEC. In this article, we develop a cooperative computation offloading and resource allocation framework for blockchain-enabled MEC systems. In the framework, we design a multiobjective function to maximize the computation rate of MEC systems and the transaction throughput of blockchain systems by jointly optimizing offloading decision, power allocation, block size, and block interval. Due to the dynamic characteristics of the wireless fading channel and the processing queues at MEC servers, the joint optimization is formulated as a Markov decision process (MDP). To tackle the dynamics and complexity of the blockchain-enabled MEC system, we develop an asynchronous advantage actor–critic-based cooperation computation offloading and resource allocation algorithm to solve the MDP problem. In the algorithm, deep neural networks are optimized by utilizing asynchronous gradient descent and eliminating the correlation of data. The simulation results show that the proposed algorithm converges fast and achieves significant performance improvements over existing schemes in terms of total reward.

The application of blockchain to mobile edge computing (MEC) systems has attracted great interests. However, the design and optimization of blockchain and MEC in most existing works are done separately, which will result in sub-optimal performance. In this paper, we propose a joint optimization framework for blockchain-enabled MEC systems to achieve the optimal trade-off between the performance of the MEC system and the performance of the blockchain system. Specifically, both MEC and blockchain are considered as services in the framework, where energy consumption and delay/time to finality (DTF) are the performance metrics for the MEC system and the blockchain system, respectively. We formulate an optimization problem to achieve the optimal trade-off through jointly optimizing user association, data rate allocation, block producer scheduling, and computational resource allocation. To solve the problem, we decouple the optimization variables for efficient algorithm design. In addition, we develop an iterative algorithm for user association and data rate allocation and a bisection algorithm for computing resource allocation. Simulation results show the convergence of the proposed algorithms, and the proposed scheme can achieve the optimal trade-off between energy consumption and DTF.

Mobile edge computing (MEC) has emerged as a potential paradigm to enhance the processing capabilities of mobile user equipments (MUEs), while edge caching has become a promising means of alleviating traffic in the backhual. In this paper, we formulate a stochastic optimization problem to maximize the average economical profit of MEC server by jointly optimizing offloading decision and caching decision making, and the allocation of radio, computing, and caching resources in a cellular network, with network stability taken into account. To tackle this problem, we develop an online algorithm referred to as dynamic joint computation offloading, resource allocation, and content caching algorithm (DJORC) based on Lyapunov optimization theory. Specifically, the proposed DJORC only needs the current states of the system, and without requiring any prior-knowledge. By further using 0-1 integer programming and linear programming, the closed-form solution of the formulated problem is obtained. Simulation results are presented to verify the performance of DJORC under different parameter settings, as well as the performance gains obtained by DJORC over other existing schemes.

Internet of things (IoT) are envisioned to be an essential in our daily lives, but most IoT devices (IDs) are battery powered and have limited resource. Recently, fog computing (FC) has been proposed to support IoT systems, where part or all of the data are offloaded from IDs to fog nodes for processing or computation. In this paper, we propose to optimise the partial computation offloading in an OFDMA based FC IoT system, while ensuring fairness among IoT links with respect to their energy consumption. In particular, we minimize the energy consumption of the worst-case link by jointly optimizing the size of offloaded data and the assignment of subcarriers, while guaranteeing the rate requirement. The formulated min-max energy efficiency optimization problem (MEP) is solved using Lagrangian dual decomposition and subgradient projection, bases on which we propose an iterative algorithm. Our simulation results show that the proposed resource allocation algorithm is more energy efficient than the existing algorithms for FC supported IoT, while achieving fairness among IoT links.

In this paper, we develop computation offloading scheme based on device-to-device (D2D) communications. The scheme is proposed for effective computation execution where some mobile devices (MDs) could offload their computation intensive tasks to appropriate nearby MDs where necessary, with the assistance of the base station. Accordingly, we formulate a stochastic optimization problem to minimize the average expenses (e.g., wireless communication expense, computation service expense) of MDs in task offloading, while considering the computation resource budget constraint to restraint the behavior of the overuse of computation resource and guarantee mobile users' motivation for collaboration not impaired. To solve this problem, we propose an algorithm that does not need any prior- knowledge of available resources of MDs, referred to as the SEEP. To address a couple and mixed combinational subproblem in the SEEP, we decouple optimization variables for suboptimal. By doing so, both task scheduling and subcarrier assignment are obtained in closed forms, while power allocation is solved by developing efficient iterative algorithm that exploits D.C. (difference of convex functions) structure. Simulation results show the convergence of the SEEP, and illustrate SEEP can flexibly coordinate the tradeoff between expenses and delay, and can substantially reduce expenses of MDs against other existing schemes.

Cooperation between the fog and the cloud in mobile cloud computing environments could offer improved offloading services to smart mobile user equipment (UE) with computation intensive tasks. In this paper, we tackle the computation offloading problem in a mixed fog/cloud system by jointly optimizing the offloading decisions and the allocation of computation resource, transmit power, and radio bandwidth while guaranteeing user fairness and maximum tolerable delay. This optimization problem is formulated to minimize the maximal weighted cost of delay and energy consumption (EC) among all UEs, which is a mixed-integer non-linear programming problem. Due to the NP-hardness of the problem, we propose a low-complexity suboptimal algorithm to solve it, where the offloading decisions are obtained via semidefinite relaxation and randomization, and the resource allocation is obtained using fractional programming theory and Lagrangian dual decomposition. Simulation results are presented to verify the convergence performance of our proposed algorithms and their achieved fairness among UEs, and the performance gains in terms of delay, EC, and the number of beneficial UEs over existing algorithms.

In this paper, we propose a novel concept of virtual resource chain for Software Defined Campus WLANs (SD-WLANs). A typical SD-WLAN is composed of three layers, i.e., infrastructure, access control and application layers. Firstly, the network control plane is decoupled from the forwarding plane, and soft-defined access points (SD-APs) merely execute the forwarding rules according to the instructions from access controllers (ACs). Secondly, with a global view of the network, AC could abstract, encapsulate, and virtualize the physical resources (e.g., computing, storage, and radio resources). Finally, by binding all the resources between the infrastructure and access control layers, isolated virtual resource chains are built up and simultaneously mapped to northbound interface (NBI) to accommodate various services at the application layer. Benefiting from the virtual resource chain, a user-oriented load balance scheme is presented in this paper. In SD-WLANs, the load of APs could be balanced according to the user's demands, and our proposed user-oriented load balance scheme could be easily invoked only by coding at the application layer. Experiment results demonstrate that our scheme is able to distribute mobile stations among all the APs and increase the average system throughput, and thus to increase the flexibility and availability of networks.