Computer Science ›› 2020, Vol. 47 ›› Issue (7): 243-249.doi: 10.11896/jsjkx.200200133

• Computer Network • Previous Articles     Next Articles

WSN Coverage Optimization Based on Adaptive Particle Swarm Optimization

QI Wei1, YU Hui-qun 1,2, FAN Gui-sheng1, CHEN Liang1   

  1. 1 Department of Computer Science and Engineering,East China University of Science and Technology,Shanghai 200237,China
    2 Shanghai Key Laboratory of Computer Software Evaluating and Testing,Shanghai 201112,China
  • Received:2020-02-29 Online:2020-07-15 Published:2020-07-16
  • About author:QI Wei,born in 1994,postgraduate.Her main research interests include software engineering and CPS.
    YU Hui-qun,born in 1967,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include software engineering,and formal method.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61702334,61772200),Project Supported by Shanghai Natural Science Foundation (17ZR1406900,17ZR1429700) and Planning Project of Shanghai Institute of Higher Education(GJEL18135)

Abstract: The wireless sensor network (WSN) coverage of data sensing layer has great significance on the quality of sensing services.In view of the problems of coverage redundancy,coverage void and premature convergence of particle swarm optimization caused by the randomness of initial deployment of wireless sensor network,an adaptive virtual force particle swarm optimization algorithm based on binomial perception coverage is proposed,which optimizes the effective coverage of the network.By adding mobile nodes to the network,the algorithm performs the redeployment distribution of position scheduling,adjusts the inertia weight by calculating the degree of population evolution and the degree of relative aggregation,and ueses the threshold of fitness variance to judge whether the intergerence of virtual force strategy is needed in the current state.This paper focuses on the analysis of the impact of the initial deployment category and mobile node proportion on the redeployment coverage performance,and gives the corresponding implementation algorithm.Simulation results show that compared with ACPSO,DACPSO and DVPSO,the improved PSO has 98.33% coverage and high mobile efficiency,which fully proves the effectiveness of the algorithm.

Key words: Hybrid sensor networks, Adaptive particle swarm optimization, Virtual force strategy, Coverage control, Redeployment

CLC Number: 

  • TP311
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