Joint Task Scheduling, Routing, and Charging for Multi-UAV Based Mobile Edge Computing
Joint Task Scheduling, Routing, and Charging for Multi-UAV Based Mobile Edge Computing
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DOI：
10.1109/icc45855.2022.9839040
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
Jun Chen;Junfei Xie
中科院分区：
文献类型：
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作者：
Jun Chen;Junfei Xie
基于无人机的移动边缘计算（MEC）系统近年来受到越来越多的研究关注。它们可以为地面用户（GUs）提供按需计算服务，而不依赖于任何通信基础设施，与传统的地面MEC或基于云的系统相比，它们有潜力以更低的延迟提供更好的计算服务。考虑到无人机的电池容量有限，现有的基于无人机的MEC研究集中在使用无人机在小区域上为GUs服务，以便在一次飞行中完成所有任务。在本文中，我们的目标是通过将充电站集成到系统中来消除这一限制，并扩大基于无人机的MEC系统可以服务的用户范围。然后，以最小化总能耗、总服务时间和同时充电的总能量为目标，制定联合任务调度、路由和充电问题。为了解决这个问题，我们建立了一个混合整数规划（MIP）模型和一个等效的混合整数线性规划（MILP）模型。数值比较研究证明了所提出方法的最优解。
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.