Optimization of Task Scheduling and Dynamic Service Strategy for Multi-UAV-enabled Mobile Edge Computing System

In this study, we introduce a multi-unmanned aerial vehicle (multi-UAV) enabled mobile edge computing (MEC) system, with UAVs as the computing server for the task offloading of ground users. The energy consumption for ground users is minimized by jointly optimizing the UAV task scheduling, bit allocation, and UAV trajectory in a unified framework. To accomplish such goal, we propose a two-layer optimization strategy, where the upper layer optimizes the UAV task scheduling based on a dynamic programming-based bidding optimization method, while the lower one solves the bit allocation and UAV trajectory. In particular, the lower layer is decoupled into several subproblems to reduce the computational complexity, which can be easily solved using an alternating direction method of multipliers. However, the UAV trajectories optimized by solving the decoupled subproblems may lead to path conflicts. As such, we further propose a re-optimization strategy to eliminate such conflicts. Experimental results demonstrate that the proposed strategy achieves a favorable performance than those of greedy and random strategies in terms of total user energy consumption, the trajectory conflicts can be eliminated effectively, and the UAV trajectory can satisfy the safety constraints.