Joint Offloading Decision and Trajectory Optimization for Multi-UAV-Assisted Mobile Edge Computing System

Abstract

Multi-unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has recently emerged as a cost-effective solution to provide computational services to Internet of Thing (IOT) terminal devices (TDs) in the limited or no available infrastructures. However, irrational trajectory of UAVs and offloading decision by TDs contribute to increased delay and energy consumption in the UAV-MEC system. In this paper, we present a two-stage optimization algorithm that jointly considers computation-offloading decision and trajectory to minimize the weighted sum of delay and energy consumption in the multi-UAV-assisted MEC system. Specifically, a Stackelberg Game-based computation offloading (SG-CO) algorithm is designed to obtain the optimal offloading decision for TDs. Furthermore, under the given TDs offloading decision, we develop the Simulated Annealing-Beetle Antennae Search (SA-BAS) algorithm to achieve the optimal trajectory for UAVs, in which SA-BAS algorithm accelerates convergence and enhances global optimization performance. Extensive simulation analysis demonstrates that the proposed scheme outperforms the benchmark schemes, showcasing its effectiveness in minimizing the weight sum of delay and energy consumption.