多无人机使能移动边缘计算系统中的计算卸载与部署优化  

Computation Offloading and Deployment Optimization in Multi-UAV-Enabled Mobile Edge Computing Systems

作　　者：刘漳辉[1,2] 郑鸿强 张建山 陈哲毅 LIU Zhang-hui;ZHENG Hong-qiang;ZHANG Jian-shan;CHEN Zhe-yi(College of Mathematics and Computer Science,Fuzhou University,Fuzhou 350116,China;Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing,Fuzhou 350116,China;Department of Computer Science,University of Exeter,Exeter EX44QF,United Kingdom)

机构地区：[1]福州大学数学与计算机科学学院,福州350116 [2]福建省网络计算与智能信息处理重点实验室,福州350116 [3]英国埃克塞特大学计算机科学系,埃克塞特EX44QF

出　　处：《计算机科学》2022年第S01期619-627,共9页Computer Science

基　　金：国家自然科学基金(62072108);福建省自然科学基金杰青项目(2020J06014)。

摘　　要：无人机与移动边缘计算技术的结合突破了传统地面通信的局限性。无人机所提供的有效视距信道可大大改善边缘服务器与移动设备之间的通信质量。为了进一步提升移动边缘计算系统的服务质量,设计了一种多无人机使能的移动边缘计算系统模型。在该系统中,无人机作为边缘服务器为移动设备提供计算服务,通过联合优化无人机部署与计算卸载策略实现平均任务响应时间的最小化。基于问题定义,提出了一种PSO-GA-G双层嵌套联合优化方法,该方法的外层采用了结合遗传算法算子的离散粒子群优化算法(Discrete Particle Swarm Optimization Algorithm Combined with Genetic Algorithm Operators,PSO-GA),实现了对无人机部署位置的优化;而该方法的内层则是采用了贪心算法(Greedy Algorithm),实现了对计算卸载策略的优化。大量仿真实验验证了所提方法的可行性和有效性。实验结果表明,相比其他基准方法,所提出方法可以实现更短的平均任务响应时间。The combination of unmanned aerial vehicles(UAVs)and mobile edge computing(MEC)technology breaks the limitations of traditional terrestrial communications.The effective line-of-sight channel provided by UAVs can greatly improve the communication quality between edge servers and mobile devices(MDs).To further enhance the quality-of-service(QoS)of MEC systems,a multi-UAV-enabled MEC system model is designed.In the proposed model,UAVs are regarded as edge servers to offer computing services for MDs,aiming to minimize the average task response time by jointly optimizing UAV deployment and computation offloading.Based on the problem definition,a two-layer joint optimization method(PSO-GA-G)is proposed.On one hand,the outer layer of the proposed method utilizes a discrete particle swarm optimization algorithm combined with genetic algorithm operators(PSO-GA)to optimize the UAV deployment.On the other hand,the inner layer of the proposed method adopts a greedy algorithm to optimize the computation offloading.Extensive simulation experiments verify the feasibility and effectiveness of the proposed method.The results show that the proposed method can achieve shorter average task response time,compared to other baseline methods.

关 键 词：移动边缘计算 无人机部署 计算卸载 离散粒子群优化算法 贪心算法 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]