The rapid expansion of computation-intensive applications renders current mobile edge computing (MEC) frameworks inadequate for delivering high-quality computing services in environments with sparse network infrastructure. Air-based stations, represented by low-altitude unmanned aerial vehicles (UAVs) and high-altitude platforms (HAPs), are considered a promising solution to this problem due to their flexible deployment, unconstrained by geographical conditions, and relatively low cost. However, UAV-based communication systems are highly sensitive to energy consumption, and HAPs face challenges in establishing stable connections with power-constrained devices while meeting their requirements for computation. To solve these limitations, we design a multi-UAV and HAP collaborative offloading framework innovatively, which takes both system energy cost and task processing delay into consideration, with their weighted consumption defined as the optimization objective. Since it is a mixed integer nonlinear programming (MINLP) problem, which is complicated to address by mathematical approaches, we reformulate it as a Markov decision process (MDP) and use a joint approach based on double deep Q network (DDQN) - proximal policy optimization (PPO) to assign task offloading methods and ratios, respectively. According to simulation results, the suggested approach ensures the timeliness of task processing while efficiently reducing the weighted consumption under varying number of users, task arrival densities, and task complexities.
Meng Li, Hao-Yu Wan, Su-Yu Lv et al.· IEEE Transactions on Mobile...· 1 citation
Amid the explosive growth of latency-aware and computation-sensitive services, mobile edge computing (MEC) assisted by aerial networks, such as high-altitude platforms (HAPs) and low-altitude unmanned aerial vehicles (UAVs), has emerged as an effective solution for providing computational capabilities to regions with sparse terrestrial infrastructure. Nevertheless, aerial networks are highly sensitive to energy cost and inherently constrained in hosting dense computing resources, while the exposed wireless environment renders them particularly vulnerable to attacks from malicious nodes. Consequently, it is imperative to develop effective task scheduling and computing resources management mechanisms that satisfy users quality-of-service (QoS) requirements while minimizing system cost and ensuring network reliability. In this paper, we develop a multi-cell MEC network composed of multiple UAVs and multiple HAPs, and further propose a dual-layer blockchain-enabled, federated election (FE)-based (DBFE) group relative policy optimization (GRPO) algorithm to jointly reduce the task offloading latency and system energy expenditure. In particular, blockchain techniques enhance system resilience against non-Byzantine failures, whereas the FE mechanism suppresses the influence of Byzantine behaviors. Simulation results demonstrate that, compared with existing approaches, the proposed method reduces the overall system cost by 19% and 31% under scenarios without malicious nodes and with malicious nodes, respectively.
Haoyu Wan, Meng Li, Qi Li et al.· IEEE Transactions on Cogniti...· 0 citations
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