多无人机协同陆地设施辅助移动边缘计算的系统能耗最小化方法

陈阳; 皮德常; 代成龙; 李本田; 王碧; 薛乔

doi:10.12263/DZXB.20210433

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多无人机协同陆地设施辅助移动边缘计算的系统能耗最小化方法

学术论文 | 更新时间：2025-12-08

- 多无人机协同陆地设施辅助移动边缘计算的系统能耗最小化方法
- Energy Minimization for Multi-UAVs Cooperative Ground Access Points Assisted Mobile Edge Computing
- 电子学报 2023年51卷第4期页码：984-992
- 作者机构：
  
  1.南京航空航天大学计算机科学与技术学院，江苏南京 211100
  2.江南大学人工智能与计算机学院，江苏无锡 214028
  3.东南大学计算机科学与工程学院，江苏南京211189
  4.香港理工大学电子与资讯工程学院，香港 999077
- 作者简介：
  
  [ "陈阳男，1993年生. 现为南京航空航天大学计算机科学与技术学院博士生. 主要研究方向为无人机辅助边缘计算、自然启发算法等..E-mail: shawn.cy@nuaa.edu.cn" ]
  [ "皮德常（通讯作者）男，1971年生.现为南京航空航天大学计算机科学与技术学院教授.主要研究方向为数据挖掘、智能应用等.E-mail: di.pi@nuaa.edu.cn" ]
  [ "代成龙男，1991年生.现为江南大学人工智能与计算机学院讲师.主要研究方向为包括数据挖掘、机器学习等.E-mail: chenglongdai@jiangnan.edu.cn" ]
  [ "李本田男，1988年生.南京航空航天大学在读博士生.主要研究方向为图机器学习、图神经网络及其在通信、物联网领域中的应用.E-mail: lbt@nuaa.edu.cn" ]
  [ "王碧男，1988年生，现为东南大学计算机科学与工程学院博士生.主要研究方向为强化学习等.E-mail: wangbi@seu.edu.cn" ]
  [ "薛乔女，1992年生.现为香港理工大学电子与资讯工程学院博士后研究员.主要研究方向为隐私保护、机器学习等.E-mail: qiaoxue1992@gmail.com" ]
- 基金信息：
  
  国家科技创新2030“新一代人工智能”重大项目(2021ZD0113103)
- DOI：10.12263/DZXB.20210433
  中图分类号： TP18;
- 收稿：2021-04-01，
  
  修回：2021-06-08，
  
  纸质出版：2023-04-25
- 稿件说明：
移动端阅览
陈阳,皮德常,代成龙等.多无人机协同陆地设施辅助移动边缘计算的系统能耗最小化方法[J].电子学报,2023,51(04):984-992.

CHEN Yang,PI De-chang,DAI Cheng-long,et al.Energy Minimization for Multi-UAVs Cooperative Ground Access Points Assisted Mobile Edge Computing[J].ACTA ELECTRONICA SINICA,2023,51(04):984-992.
陈阳,皮德常,代成龙等.多无人机协同陆地设施辅助移动边缘计算的系统能耗最小化方法[J].电子学报,2023,51(04):984-992. DOI： 10.12263/DZXB.20210433.

CHEN Yang,PI De-chang,DAI Cheng-long,et al.Energy Minimization for Multi-UAVs Cooperative Ground Access Points Assisted Mobile Edge Computing[J].ACTA ELECTRONICA SINICA,2023,51(04):984-992. DOI： 10.12263/DZXB.20210433.

摘要

无人机作为移动基站辅助边缘计算可为用户设备提供广泛的服务范围和额外计算能力，本文提出一种多无人机协同陆地设施辅助边缘计算的系统.该系统将多架无人机作为移动基站，来协同多个陆地设施对移动用户提供计算卸载服务.系统分为局部计算模型、无人机计算模型、陆地设施计算模型以及无人机盘旋能耗模型.目的是优化多个无人机的位置和用户的卸载决策使得系统总体能耗最小.为求解该问题，提出一种多子群驱动的均衡优化算法.该方法基于两个子种群演化交互，集成了变异和种群重启机制，具有良好的优化能力.仿真实验表明，提出的算法能更好降低系统能耗.

Abstract

UAV (Unmanned Aerial Vehicle/Drones)-assisted mobile edge computing can provide extensive coverage and additional computing power to user devices. In this paper

we study a system of multi-UAVs collaborative ground facilities-assisted mobile edge computing. The system provides offloading computing for user equipment through multiple UAVs in collaboration with multiple ground facilities. The system is divided into the local computing model

UAVs computing model

Ground computing model

and UAVs energy consumption model. The objective is to optimize the UAVs' locations and user offloading decisions to minimize the system energy consumption. The system energy minimization is a large-scale mixed integer optimization problem. To solve the problem

we propose a multi-subgroup driven equilibrium optimizer. The algorithm incorporates two subgroup evolutionary interactions

mutation and population restart mechanisms. Experiments show that the proposed algorithm can better reduce the system energy consumption compared with several other swarm intelligence algorithms.

关键词

Keywords

references

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