Joint Dynamic Role Switching Scheme and Cooperative Task Offloading Optimization for UAV Swarm-Enabled Edge Computing

Due to the high flexibility and wide coverage, unmanned aerial vehicle (UAV) has always been a popular issue in the area of mobile edge computing. UAV swarms can be deployed on-site to serve user equipment (UE) and handle offloading tasks. However, it’s inefficient that the uneven distribution of tasks cause some UAVs to be assigned heavy tasks while others are idle within the swarm. To address above issue, this paper investigates a cooperative offloading problem of minimizing the total system latency and the energy consumption, subject to constraints on battery capacity and execution latency. The problem is confirmed to be a challenging mixed-integer nonconvex programming problem with Non-deterministic Polynomial feature. Therefore we propose a joint UAV dynamic role switch scheme and cooperative offloading (MARSCO) algorithm to solve it efficiently, where two sub-problems are optimized iteratively. Specifically, both of them are optimized utilizing multi-agent deep reinforcement learning (MADRL) algorithm to interact with the environment for optimization. Finally, numerical results illustrate that the proposed algorithm utilizes the system resources to significantly reduce the total system latency and energy consumption compared with the benchmark algorithms.