Joint task offloading and trajectory optimization for multi-UAV assisted mobile edge computing

Jiequ JI; Kun ZHU; Changyan YI; Ran WANG

doi:10.11959/j.issn.2096-3750.2021.00190

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Joint task offloading and trajectory optimization for multi-UAV assisted mobile edge computing

Topic: Edge Intelligence and Fog Computing in IoT | 更新时间：2024-06-05

- Joint task offloading and trajectory optimization for multi-UAV assisted mobile edge computing
- Chinese Journal on Internet of Things Vol. 5, Issue 1, Pages: 27-35(2021)
- 作者机构：
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- DOI：10.11959/j.issn.2096-3750.2021.00190
  CLC： TP39
- Published：30 March 2021，
  
  Published Online：2021-03，
- 稿件说明：
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JIEQU JI, KUN ZHU, CHANGYAN YI, et al. Joint task offloading and trajectory optimization for multi-UAV assisted mobile edge computing. [J]. Chinese journal on internet of things, 2021, 5(1): 27-35.
DOI：

JIEQU JI, KUN ZHU, CHANGYAN YI, et al. Joint task offloading and trajectory optimization for multi-UAV assisted mobile edge computing. [J]. Chinese journal on internet of things, 2021, 5(1): 27-35. DOI： 10.11959/j.issn.2096-3750.2021.00190.

摘要

提出了一个多无人机辅助移动边缘计算系统，系统部署了多个配备计算资源的无人机为本地资源有限的移动用户提供计算卸载机会。每个用户的计算任务分为两部分：一部分卸载到关联的无人机进行计算，剩余部分在本地进行计算。旨在联合优化一个有限周期内的无人机轨迹和用户调度来最小化所有用户间最大的总时延。所提出的问题是一个混合整数非凸优化问题，为了有效求解该问题，通过引入一些辅助变量将其等价地转化为一种更易处理的形式，然后提出了一个惩罚凹凸过程的算法来求解转化后的问题。数值结果表明，与其他基准方案相比，提出的联合优化方案能获得显著的性能增益。

Abstract

An unmanned aerial vehicle (UAV)-assisted mobile edge computing system was proposed in which multiple UAVs equipped with computing resources were employed to provide computation offloading opportunities for mobile users with limited local resources.The computing tasks of each user can be divided into two parts.One portion was offloaded to its associated UAV for computing and the remaining portion was processed locally.It was aimed at minimizing the sum of the maximum delay among all user devices by jointly optimizing the user scheduling and the UAV trajectory in a finite period.The proposed problem was a mixed-integer non-convex optimization problem.To facilitate solving this problem

it was equivalently converted into a more tractable problem by introducing some auxiliary variables

and then a penalty concave-convex procedure algorithm was proposed to solve the converted problem.Simulation results show that the proposed joint optimization scheme achieves significantly better performance than other benchmark schemes.

关键词

无人机移动边缘计算轨迹设计用户调度

Keywords

unmanned aerial vehiclemobile edge computingtrajectory designuser scheduling

references

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HU Q Y, CAI Y L, YU G D ,et al. Joint offloading and trajectory design for UAV-enabled mobile edge computing systems[J]. IEEE Internet of Things Journal, 2019,6(2): 1879-1892.

ZHOU F H, WU Y P, SUN H J ,et al. UAV-enabled mobile edge computing:offloading optimization and trajectory design[C]// Proceedings of 2018 IEEE International Conference on Communications (ICC). Piscataway:IEEE Press, 2018: 1-6.

ZHOU F H, WU Y P, HU R Q ,et al. Computation rate maximization in UAV-enabled wireless-powered mobile-edge computing systems[J]. IEEE Journal on Selected Areas in Communications, 2018,36(9): 1927-1941.

JI J Q, ZHU K, YI C Y ,et al. A detailed proof for transforming the original problem into a more tractable form:penalty concave-convex algorithm[EB]. 2020.

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