In vehicular communication and mobile edge computing (MEC) networks, limited storage resources and challenges related to vehicles data security pose significant concerns. To address these issues while reducing communication latency and enhancing network security, blockchain technology is introduced. Additionally, deep reinforcement learning (DRL) is leveraged to optimize content caching strategies. By formulating a Markov decision process (MDP) model and applying the proximal policy optimization (PPO) algorithm, efficient cache management and optimal resource allocation are achieved. Simulation results demonstrate that the proposed approach effectively improves cache hit rates and significantly reduces latency in vehicular communication environments, yielding superior performance.

In this study, we propose a mobile edge computing (MEC)-enabled vehicular Metaverse system designed for augmented reality (AR) services, where vehicles on the road can access the Metaverse service through nearby Metaverse service providers (MSPs) equipped with MEC servers. In this system, vehicles are charged for their use of computational and communication resources. Due to varying positions and viewing angles, vehicles may have different content preferences. To minimize cost while ensuring optimal quality of experience (QoE), we formulate an optimization problem that adjusts content resolution and resource allocation to match individual vehicle needs. To address this optimization problem, we introduce a deep reinforcement learning (DRL) algorithm integrated with active inference theory to solve the decision-making problem with the performance of low latency and high efficiency. Simulation results demonstrate that our proposed scheme outperforms comparative algorithms in comprehensive performance, providing an effective solution for optimizing AR-enabled vehicular Metaverse systems.

In the dynamic and demanding 6 G IoT environments, UAV-enabled Mobile Edge Computing (MEC) systems face significant challenges such as varying communication conditions, energy constraints, and high task demands. To address these challenges effectively, we propose a novel optimization framework that integrates Reconfigurable Intelligent Surfaces (RIS) and employs the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. The primary goals of this framework are to minimize latency, maximize throughput, and ensure energy efficiency by dynamically coordinating UAV trajectories, task offloading decisions, and RIS phase shifts. Through extensive simulations, we demonstrate that the proposed framework significantly reduces delay and improves throughput compared to traditional methods, highlighting the substantial benefits of combining RIS with UAV-MEC systems in dynamic IoT scenarios. These findings suggest that the integration of RIS can effectively enhance system performance and adaptability in next-generation wireless networks, providing a promising approach to tackle the complex challenges of 6G IoT environments.

In this paper, we address the problem of the operator’s economic profit maximization in a multi-access edge computing (MEC)-enabled time division multiple access (TDMA)-based air-ground integrated networking (AGIN) network. We consider to optimize task placement and replacement, unmanned aerial vehicle (UAV) placement, UAV flight time, access control, and task offloading ratios in user devices (UDs) and the UAV. The optimization is constrained by storage capacity, task processing quality of service (QoS) requirements, and TDMA requirements, etc. Our optimization is conducted in two time scales. Task placement and replacement are performed in a coarse-grained time scale (frame), while other optimizations are conducted in a fine-grained time scale (time slot). Due to the high dynamics of the environment, finding a solution is challenging. To address this problem, we present a hierarchical deep reinforcement learning (DRL) algorithm. The high-level component is a deep Q network (DQN) agent responsible for obtaining task placement and replacement solutions within a frame. The low-level component is an improved deep deterministic policy gradient (IDDPG) agent, which is used to address task processing-related issues within a time slot. Our simulations illustrate that the proposed algorithm has good performance in economic profit maximization compared with other algorithms.

To address the challenge of constructing short girth-8 quasi-cyclic (QC) low-density parity-check (LDPC) codes, a novel construction framework based on vertical symmetry (VS) is proposed. Basic properties of the VS structure are presented. With the aid of these properties, existing explicit constructions for column weights from three to five which can be transformed into the VS structure are sorted out. Then two novel explicit constructions with the VS structure which guarantee short codes are presented for column weights of three and six. Moreover, an efficient search-based method is also proposed to find short codes with the VS structure. Compared with the state-of-the-art benchmarks, both the explicit constructions and the search-based method presented in this paper can provide shorter codes for most cases. Simulation results show that the new shorter codes can perform almost the same as or better than the longer existing counterparts. Thus, the new shorter codes can fit better with the low-latency requirement for modern communication systems.

Multiaccess edge computing (MEC) empowered air–ground integrated networks (AGINs) hold great promise in delivering accessible computing services for users and Internet of Things (IoT) applications, such as forest fire monitoring, emergency rescue operations, etc. In this article, we present a comprehensive air–ground integrated MEC framework, where edge servers carried by unmanned aerial vehicles (UAVs) will provide efficient computation services to IoT devices and user equipment (UE) (which are collectively referred to as UEs). We aim to minimize the long-term average weighted sum of task completion delay and economic expenditure for all the UEs. This objective is achieved through various strategies, including preinstalling new service instances into UAVs, removing idle service instances from UAVs, task offloading decision making, access control, selecting appropriate service instances for each offloaded service request, and resource allocation optimization. Considering the complexity of the problem and the dynamics of the system, we reformulate the problem as a Markov decision process (MDP) and present a multiagent deep deterministic policy gradient (MADDPG)-based algorithm to enable low-complexity and real-time adaptive decision-making. Since our problem contains integer, binary and continuous variables, it is not straightforward to apply the MADDPG algorithm. Specifically, we first normalize the continuous variables, and then convert the continuous output generated by MADDPG into discrete variables, while ensuring the coupling constraints between different variables are preserved. The simulation results demonstrate the fast convergence of our proposed algorithm and its superior performance in minimizing costs compared with the baseline algorithms.

In the 6G era, space–air–ground integrated networks (SAGINs) can provide ubiquitous coverage for Internet of Things (IoT) devices. Multiaccess edge computing (MEC) and blockchain are two enabling technologies, which can further enhance the services capabilities of SAGINs, where MEC demonstrates a notable capability in efficiently minimizing both the task execution delays and system energy consumption, and blockchain can provide trust guarantee for task offloading and wireless data transmission among the entities operated by different operators in SAGIN. In this article, we present an MEC and blockchain enabled SAGIN architecture, which consists of two subsystems. In the MEC subsystem, a satellite and multiple unmanned aerial vehicles (UAVs) act as the edge nodes to provide IoT devices with computing power. Moreover, the satellite serves as the block generator and the client, and the UAVs serve as the consensus nodes of the blockchain subsystem. We intend to minimize the energy consumption within the network, which is achieved through the IoT devices’ task segmentation, the UAVs, and satellite’s bandwidth allocation among their served IoT devices. And moreover, the computing power of UAVs and the satellite also allocated in task processing and blockchain consensus. Considering the high dynamics of the network, it is impossible to obtain real-time and accurate channel information, so we remodel this problem as a Markov decision process, and propose a low-complexity adaptive optimization algorithm based on the deep deterministic policy gradient (DDPG). Our simulation results indicate that the proposed algorithm exhibits commendable performance in minimizing the network energy consumption and DDPG agent’s accumulated reward maximization.

Federated learning (FL) in Internet of Things (IoT) applications facilitates the collaborative training of a global model across distributed devices with a server. Despite its potential, the distributed nature and vulnerability of IoT devices render FL susceptible to Byzantine attacks. Existing approaches to counter these attacks are often impractical in real-world IoT scenarios, mainly due to the challenges posed by nonindependent identically distributed (non-IID) data and the high-dimensional model common in IoT devices. To address these challenges, we propose Guard-FL, an efficient and robust aggregation mechanism assisted by uniform manifold approximation and projection (UMAP) for FL. Guard-FL is designed to enhance the performance of the global model in non-IID data environments without compromising defense capabilities. Specifically, it utilizes UMAP to capture non-linear features among high-dimensional local models. Based on these features, robust regression and unsupervised clustering techniques are applied to effectively detect and remove attackers from local model updates. Subsequently, the server employs information stored in weights to evaluate and aggregate the remaining divergent model updates, thus significantly improving the global models performance. To validate the efficacy of Guard-FL, we provide a theoretical analysis of its convergence properties. Our experiments demonstrate that Guard-FL surpasses existing stateof-the-art solutions, achieving up to 96% accuracy in detecting malicious clients on non-IID CIFAR-10 datasets under various Byzantine attack scenarios. The implementation code is provided at https://github.com/XidianNSS/Guard-FL.git

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.