Energy-Efficient Joint Task Offloading and 3D Trajectory Optimization for UAV-Assisted MEC Systems Over Uneven Terrain
With the rapid advancement and deep integration of the Internet of Things (IoT) and 5G technologies, mobile edge computing (MEC) has undertaken an increasingly important role in enhancing service quality. Leveraging their high mobility and flexible deployment, unmannedaerial vehicles (UAVs) extend MEC services to challenging environments such as mountainous areas. Nevertheless, UAVs have inherent limitations, including restricted onboard resources (e.g., energy and computing capacity) and the need for obstacle avoidance flight. In this work, which investigates a UAV-assisted MEC system with uneven terrain and dynamic service scenarios, these limitations bring additional challenges to system optimization. The incorporation of terrain information in high-dimensional state space, the continuous action space required for fine control, and the variable network demands under dynamic service scenarios complicate the non-convex optimization problem. By jointly designing UAV’s trajectory and user equipments’ (UEs) task allocation, we address the task offloading problem under safe flight conditions, aiming to maximize both service coverage ratio and UAV’s propulsion energy efficiency. Then, we propose a phased hierarchical deep reinforcement learning (PH-DRL) algorithm, in which the network training is designed in phases and the network structure is organized hierarchically. Specifically, the phased method overcomes insufficient network experience in complex environments, while the hierarchical method decomposes the optimization variables, enabling independent solution. Experimental results demonstrate that the PH-DRL algorithm substantially improves service coverage ratio and propulsion energy efficiency, achieving system utility that significantly outperforms other comparative strategies.