Computer Science ›› 2022, Vol. 49 ›› Issue (6A): 619-627.doi: 10.11896/jsjkx.210600165

• Computer Network • Previous Articles     Next Articles

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

LIU Zhang-hui1,2, ZHENG Hong-qiang1,2, ZHANG Jian-shan1,2, CHEN Zhe-yi3   

  1. 1 College of Mathematics and Computer Science,Fuzhou University,Fuzhou 350116,China
    2 Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing,Fuzhou 350116,China
    3 Department of Computer Science,University of Exeter,Exeter EX4 4QF,United Kingdom
  • Online:2022-06-10 Published:2022-06-08
  • About author:LIU Zhang-hui,born in 1971,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include big data technology and intelligence computation.
    CHEN Zhe-yi,born in 1991,Ph.D.His main research interests include cloud/edge computing,resource optimization,deep lear-ning,and reinforcement lear-ning.
  • Supported by:
    National Natural Science Foundation of China(62072108) and Natural Science Foundation of Fujian Province for Distinguished Young Scholars(2020J06014).

Abstract: 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.

Key words: Computation offloading, Discrete particle swarm optimization algorithm, Greedy algorithm, Mobile edge computing, Unmanned aerial vehicle deployment

CLC Number: 

  • TP393
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