Research on Joint Optimization of Task Offloading and UAV Trajectory in Mobile Edge Computing Considering Communication Cost Based on Safe Reinforcement Learning

Abstract

Due to CPU and memory limitations, mobile IoT devices face challenges in handling delay-sensitive and computationally intensive tasks. Mobile edge computing addresses this issue by offloading tasks to the wireless network edge, reducing latency and energy consumption. UAVs serve as auxiliary edge clouds, providing flexible deployment and reliable wireless communication. To minimize latency and energy consumption, considering the limited resources and computing capabilities of UAVs, a multi-UAV and multi-edge cloud system was deployed for task offloading and UAV trajectory optimization. A joint optimization model for computing task offloading and UAV trajectory was proposed. During model training, a UAV communication mechanism was introduced to address potential coverage issues for mobile user devices through multiple UAVs or complete coverage. Considering the fact that decisions made by UAVs during trajectory planning may lead to collisions, a MADDPG algorithm with an integrated safety layer was adopted to obtain the safest actions closest to the joint UAV actions under safety constraints, thereby avoiding collisions between UAVs. Numerical simulation results demonstrate that the optimization method based on safety reinforcement learning considering communication cost outperforms other optimization methods. Communication between UAVs effectively addresses the issue of redundant or incomplete coverage for mobile user devices, reducing computation latency and energy consumption for task offloading. Additionally, the introduction of safety reinforcement learning effectively avoids collisions between UAVs.