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. Multi-access 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 task execution delays and system energy consumption, and blockchain can provide trust guarantee for task offloading and wireless data transmission among entities operated by different operators in SAGIN. In this paper, we present an MEC and blockchain enabled SAGIN architecture, which consists of two sub-systems. In the MEC sub-system, a satellite and multiple Unmanned Aerial Vehicles (UAVs) act as 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 consensus nodes of the blockchain sub-system. We intend to minimize the energy consumption within the network, which is achieved through the IoT devices’ task segmentation, the UAVs and satellite’s band-width 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 re-model this problem as a Markov decision process, and propose a low-complexity adaptive optimization algorithm based on Deep Deterministic Policy Gradient (DDPG). Our simulation results indicate that the proposed algorithm exhibits commendable performance in minimizing network energy consumption and DDPG agent’s accumulated reward maximization.

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.

In this paper, we investigate the secure task offloading and computation resource allocation issues in a consortium blockchain-enabled multi-access edge computing (MEC) system. Specifically, edge servers and a cloud center provides user equipments (UEs) with augmented computing power for task processing, while consortium blockchain can provide trust and secure guarantee to UEs in task offloading. Within the MEC system, we intend to minimize the task processing cost of all UEs by jointly optimizing the binary task offloading decision and the computation resource block allocation. Meanwhile, in the blockchain system, we first enhance the consensus procedure by proposing an improved practical Byzantine fault tolerance (IPBFT) consensus algorithm, and then conduct consensus committee selection, thus to minimize consensus delay and fail ratio. The two systems are jointly optimized, subjecting to the computation power of edge nodes, the node number limitation of IPBFT, the task processing and blockchain consensus delay, etc. To address the problem effectively, we reform it into a Markov decision process (MDP) and use proximal policy optimization (PPO) to dynamically learn the optimal joint solution. Simulation results demonstrate that our proposed algorithm converges fast, and performs well in total reward maximization, and UEs’ cost, consensus delay and fail ratio minimization.

The present study aims to investigate the problem of minimizing energy consumption in Unmanned Aerial Vehicle (UAV)-assisted Wireless Sensor Networks (WSNs). In the current context of scarce energy resources, efficient management of energy in UAVs and sensor nodes is crucial for enhancing overall network performance and prolonging network lifespan. To address this, a comprehensive approach is proposed, integrating cluster analysis and path planning to optimize energy utilization and path selection. Initially, Honey Badger Algorithm (HBA) optimized Probabilistic Fuzzy C-Means (PFCM) algorithm is using to select more suitable clusters, mitigating energy wastage near the data center and reducing energy consumption in nodes far from the cluster center. This optimization process also simplifies UAV path planning complexity. Subsequently, the clustering results are input into the Proximal Policy Optimization 2 (PPO2) algorithm for UAV path planning, aiming to determine the optimal trajectory. PPO2 algorithm employs reinforcement learning to balance energy consumption and path optimization, thus minimizing energy consumption across the entire network. To further enhance the path, the 2-opt algorithm is employed to improve the trajectory of the strategic network. Through a local search algorithm, the path of the strategic network is optimized, thereby improving energy utilization efficiency and network performance. Extensive simulation experiments are conducted to validate the effectiveness of the proposed algorithm. Comparative analysis with other algorithms indicates significant improvements in energy consumption, effectively prolonging the network’s lifespan. This method allows for substantial energy savings in UAVs and sensor nodes, consequently enhancing the overall energy efficiency of the network.

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