Task Scheduling and Trajectory Optimization Based on Fairness and Communication Security for Multi-UAV-MEC System

Unmanned aerial vehicles (UAVs) show significant potential in enhancing communication services within the mobile edge computing (MEC) system by taking their advantages on the flexible mobility and reliable line-of-sight links. However, in the scenarios with multiple UAV-MECs (UMs) operating concurrently, potential conflicts in their trajectories need to be mitigated. Thus, the 3D trajectory needs to be properly designed in a highly reliable manner. Besides, such an infrastructure-free communication paradigm also exposes a potential risk of misuse by malicious parties, which allows them to eavesdrop on private communications, posing a threat to the security and privacy. Therefore, we consider a multi-UAV-assisted MEC communication system, where a UAV maliciously eavesdrops on the data transmission from the user devices (UDs) while a jammer is deployed on the ground to interfere with the eavesdropping channel. In specific, our objective is to minimize the energy consumption and latency while incorporating fairness metrics by optimizing the 3D trajectories of UMs, transmission power of UDs, and the offloading strategies under the constraints of ensuring communication security and load fairness. Given the complexity of this mixed-integer nonconvex programming problem, we decompose the formulated problem into three subproblems. Specifically, at each time slot, we optimize the transmit power and offloading strategies using theoretical derivation and mathematical analysis, respectively. Additionally, a multi-agent deep deterministic policy gradient (MADDPG) algorithm is employed to optimize the trajectories of UMs. Simulation results demonstrate that our proposed joint optimization algorithm successfully minimizes the system energy consumption and delay as compared to benchmarking schemes.