Joint Optimization of UAV Trajectory, User Scheduling, and Resource Allocation for Secure MEC Systems in IoT Using Deep Reinforcement Learning

Abstract

As the network society progresses, Mobile Edge Computing (MEC) has emerged as a transformative technology, enabling low-latency and high-efficiency data processing, which is reshaping the evolution of the Internet of Things (IoT). By offloading computational tasks to the network edge and delivering services closer to the data source, MEC effectively mitigates data transmission latency and enhances system responsiveness, positioning itself as a pivotal enabler of future intelligent societies. This study addresses the critical issue of communication security in urban environments, where eavesdropping poses significant threats, by proposing a novel joint optimization framework that integrates UAV flight trajectory planning, user scheduling and computational resource allocation. To navigate the complexities of high-dimensional state and continuous action spaces, we leverage the Deep Deterministic Policy Gradient (DDPG) algorithm for iterative policy optimization. The objective is to minimize task processing delays while ensuring secure communication. Experimental results substantiate the efficacy of the proposed algorithm, demonstrating its ability to achieve a balanced trade-off between communication security and task processing efficiency, across varying weight coefficients, thereby enhancing the overall performance of MEC systems in IoT contexts.