A Particle Swarm Optimization and Mutation Operator Based Node Deployment Strategy for WSNs

被引:1
|
作者
Wang, Jin [1 ]
Ju, Chunwei [1 ]
Ji, Huan [1 ]
Youn, Geumran [2 ]
Kim, Jeong-Uk [2 ]
机构
[1] Yangzhou Univ, Sch Informat Engn, Yangzhou, Jiangsu, Peoples R China
[2] Sangmyung Univ, Dept Elect Engn, Seoul, South Korea
来源
关键词
Wireless sensor network; Coverage; Particle swarm optimization; Mutation operator; SENSOR NETWORK; COVERAGE; ALGORITHM;
D O I
10.1007/978-3-319-68505-2_37
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Coverage control is one of the most critical issues for wireless sensor networks (WSNs), which is closely related to the sensor network performance. Generally, sensor nodes are randomly and massively deployed in targeted area, this densely deployment will give rise to communication overhead. In order to fully utilize sensor nodes in target area, we consider the problem of maximizing the lifetime of network with fewer nodes. In this paper, we propose a novel algorithm based on particle swarm optimization and mutation operator. We first give a mathematic model to calculate network coverage rate. Then, premature phenomenon judgment is given and a mutation operator is introduced. Finally, we utilize mutation operator to improve particle swarm optimization in particle search process. Simulation results show that compared with traditional particle swarm algorithm, our algorithm can effectively increase the coverage rate.
引用
收藏
页数:8
相关论文
共 50 条
  • [11] A New Particle Swarm Optimization Algorithm with Adaptive Mutation Operator
    Gao, Yuelin
    Duan, Yuhong
    [J]. ICIC 2009: SECOND INTERNATIONAL CONFERENCE ON INFORMATION AND COMPUTING SCIENCE, VOL 1, PROCEEDINGS: COMPUTING SCIENCE AND ITS APPLICATION, 2009, : 58 - +
  • [12] Quantum-behaved Particle Swarm Optimization with mutation operator
    Liu, J
    Xu, WB
    Sun, J
    [J]. ICTAI 2005: 17TH IEEE INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE, PROCEEDINGS, 2005, : 237 - 240
  • [13] Subpopulation Particle Swarm Optimization with a Hybrid Mutation Strategy
    Xie, Zixuan
    Huang, Xueyu
    Liu, Wenwen
    [J]. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2022, 2022
  • [14] Node Deployment Using Virtual Force with Particle Swarm Optimization in WSN
    Umadevi, K. S.
    Shah, Virti
    Desai, Unnati
    [J]. ADVANCED SCIENCE LETTERS, 2018, 24 (08) : 6017 - 6019
  • [15] Particle swarm optimization with an enhanced learning strategy and crossover operator
    Molaei, Sajjad
    Moazen, Hadi
    Najjar-Ghabel, Samad
    Farzinvash, Leili
    [J]. KNOWLEDGE-BASED SYSTEMS, 2021, 215 (215)
  • [16] An adaptive mutation strategy for differential evolution algorithm based on particle swarm optimization
    Abhishek Dixit
    Ashish Mani
    Rohit Bansal
    [J]. Evolutionary Intelligence, 2022, 15 : 1571 - 1585
  • [17] An adaptive mutation strategy for differential evolution algorithm based on particle swarm optimization
    Dixit, Abhishek
    Mani, Ashish
    Bansal, Rohit
    [J]. EVOLUTIONARY INTELLIGENCE, 2022, 15 (03) : 1571 - 1585
  • [18] Parallel particle swarm optimization based mobile sensor node deployment in wireless sensor networks
    Wang, Xue
    Wang, Sheng
    Ma, Jun-Jie
    [J]. Jisuanji Xuebao/Chinese Journal of Computers, 2007, 30 (04): : 563 - 568
  • [19] Metropolis Particle Swarm Optimization Algorithm with Mutation Operator For Global Optimization Problems
    Idoumghar, L.
    Aouad, M. Idrissi
    Melkemi, M.
    Schott, R.
    [J]. 22ND INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2010), PROCEEDINGS, VOL 1, 2010,
  • [20] Particle Swarm Optimization Based on Power Mutation
    Wu, Xiaoling
    Zhong, Min
    [J]. 2009 ISECS INTERNATIONAL COLLOQUIUM ON COMPUTING, COMMUNICATION, CONTROL, AND MANAGEMENT, VOL IV, 2009, : 464 - 467