Joint Task Scheduling, Routing, and Charging for Multi-UAV Based Mobile Edge Computing

Abstract

Unmanned aerial vehicles (UAVs) based mobile edge computing (MEC) systems have attracted increasing research attention recently. They can provide on-demand computing services for ground users (GUs) without relying on any communication infrastructures and have the potential to provide better computing services with lower latency, compared with the conventional ground-based MEC or cloud-based systems. Considering the limited battery capacity of the UAVs, existing studies on UAV-based MEC have focused on using UAVs to serve GUs over small areas so that all tasks can be completed during a single flight. In this paper, we aim to remove this restriction and expand the range of users the UAV-based MEC system can serve, by integrating charge stations into the system. A joint task scheduling, routing, and charging problem is then formulated with the objective to minimize the total energy consumption, total service time, and total energy charged simultaneously. To solve this problem, we develop a mixed-integer programming (MIP) model and an equivalent mixed-integer linear programming (MILP) model. Comparative numerical studies demonstrate the optimal solutions found by the proposed approaches.