Fairness-Based 3-D Multi-UAV Trajectory Optimization in Multi-UAV-Assisted MEC System

Unmanned aerial vehicles (UAVs)-assisted mobile-edge computing (MEC) communication system has recently gained increasing attention. In this article, we investigate a 3-D multi-UAV trajectory optimization based on ground devices (GDs) selecting the target UAV for task computing. Specifically, we first design a 3-D dynamic multi-UAV-assisted MEC system in which GDs have real-time mobility and task update. Next, we formulate the system communication, computation, and flight energy consumption as objective functions based on fairness among UAVs. Then, to pursue fairness among UAVs, we theoretically deduce and mathematically prove the optimal GDs' selectivity and offloading strategy, that is, how GDs select the optimal UAV for task offloading and how much to offload. While ensuring the optimal offloading strategy and GDs' selectivity between UAVs and GDs at each step, we model UAV trajectories as a sequence of location updates of all UAVs and apply a multiagent deep deterministic policy gradient (MADDPG) algorithm to find the optimal solution. Simulation results demonstrate that we achieve the minimum energy consumption under the premise of fairness and the efficiency of model processing tasks.

Automated summary

In this article, a 3-D multi-UAV trajectory optimization based on ground devices (GDs) selecting the target UAV for task computing is investigated. A dynamic multi-UAV-assisted MEC system is designed, and the communication, computation, and flight energy consumption are formulated as objective functions based on fairness among UAVs. Through mathematical deduction, the optimal GDs' selectivity and offloading strategy are proven, and a multiagent deep deterministic policy gradient (MADDPG) algorithm is applied to find the optimal solution by modeling UAV trajectories as a sequence of location updates.