Computer Science ›› 2022, Vol. 49 ›› Issue (11A): 220200089-7.doi: 10.11896/jsjkx.220200089

• Computer Networ • Previous Articles     Next Articles

UAV Base Station Deployment Method for Mobile Edge Computing

LIU Fang-zheng, MA Bo-wen, LYU Bo-feng, HUANG Ji-wei   

  1. Beijing Key Laboratory Petroleum Data Mining,China University of Petroleum-Beijing,Beijing 102249,China
  • Online:2022-11-10 Published:2022-11-21
  • About author:LIU Fang-zheng,born in 1991,Ph.D,is a student member of China Computer Federation.Her main research interests include services computing and edge computing.
    HUANG Ji-wei,born in 1987,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include services computing,Internet of Things,and edge computing.
  • Supported by:
    Beijing Nova Program of Science and Technology(Z201100006820082),National Natural Science Foundation of China(61972414),Beijing Natural Science Foundation(4202066).

Abstract: In mobile edge computing (MEC),local devices can offload tasks to edge servers for execution to improve the quality of service (QoS).However,in disaster-stricken areas or in emergencies,ground-fixed base stations may be paralyzed on a large scale.For emergency communications,mobile edge computing systems supported by unmanned aerial vehicles (UAV) have emerged.As an emerging means of emergency communication,drones can carry edge servers,and ground user equipment can offload their computing tasks to the drones for execution.However,it is challenging to deploy multiple UAV base stations in a multi-user network.To this end,this paper focuses on the strategic deployment of UAV base stations,modeling the problem as a multi-objective optimization problem,which aims to balance the workload among UAV base stations and minimize the access delay between ground users and UAV base stations.Compared with single-objective optimization problems,multi-objectives interact with each other and the solutions are not unique,which brings certain difficulties to model solving.For this reason,this paper proposes a Pareto boundary search algorithm based on K-medoids to solve the problem,and then further proposes to use the principal component analysis algorithm (PCA) to find the most suitable solution from the Pareto boundary as the final deployment strategy for UAV base stations.The experiment in this paper uses real data sets and compares the performance with several other baseline methods to verify the effectiveness of the proposed solution.

Key words: UAV base station deployment, Multi-objective optimization, K-medoids, Pareto boundary, Principal component analysis

CLC Number: 

  • TP393
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