UAV-aided distribution line inspection using double-layer offloading mechanism

With the continuous growth of electricity demand, the safe and stable operation of distribution lines is crucial for power transportation. Unmanned aerial vehicle (UAV) inspection has been widely used for the maintenance and repair of distribution lines. Due to the limitations of computational power and endurance, it is difficult for UAVs to independently complete data processing. Combined with mobile edge computing (MEC), this paper proposes a computing offloading strategy based on multi-agent reinforcement learning and double-layer offloading mechanism, which can further utilize the computing power of non-task devices and edge servers. Firstly, three-layer system architecture, named MEC-U-NTDC (MEC-UAV-Non-task Device Cloud), is built. Secondly, double-layer offloading mechanism is designed to comprehensively utilize the computing power of edge servers and neighbouring non-task devices. Finally, a multi-agent algorithm DLMQMIX is proposed to minimize the total cost for UAV inspection. Simulation experiments show that the proposed algorithm can effectively solve the task offloading problem of UAV-aided distribution line inspection, and compared with algorithms such as PSO, GA, and QMIX, it performs better in terms of average delay, system cost, and load balancing, achieving a smaller total system cost.