• DocumentCode
    2328413
  • Title

    Swarm intelligence-based sensor network deployment strategy

  • Author

    Park, Hyungmin ; Han, Ji-Hyeong ; Kim, Jong-Hwan

  • Author_Institution
    Dept. of Electr. Eng., KAIST, Daejeon, South Korea
  • fYear
    2010
  • fDate
    18-23 July 2010
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    The wireless sensor network is a decentralized and self-organized system. Each sensor node in the sensor network should be intelligent enough to carry out its task of monitoring the environment. There would be numerous ways for deploying the sensor nodes in the environment. In this paper, swarm intelligence-based sensor network deployment strategy is proposed. To make a reference point for each sensor node, fuzzy integral is utilized as a multi-criteria decision making process. Three criteria, such as sensor value, crowdedness and confidence, are used for partial evaluation and the degree of consideration for each criterion is represented by fuzzy measure. Global evaluation by fuzzy integral determines the best position for each sensor node independently. To show the effectiveness of the proposed strategy, it is compared with the SPSO07-based deployment strategy through computer simulations in a simulation environment. The results show that the proposed strategy covers much wider area with sensor nodes than the SPSO07-based one.
  • Keywords
    decision making; environmental science computing; fuzzy set theory; intelligent sensors; multivariable systems; particle swarm optimisation; self-adjusting systems; sensor placement; wireless sensor networks; SPSO07 based deployment strategy; computer simulation; decentralized system; fuzzy integral; intelligent sensor; multicriteria decision making process; self organized system; sensor network deployment strategy; swarm intelligence; wireless sensor network; Decision making; Mobile communication; Monitoring; Particle swarm optimization; Pixel; Pollution measurement; Robot sensing systems;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation (CEC), 2010 IEEE Congress on
  • Conference_Location
    Barcelona
  • Print_ISBN
    978-1-4244-6909-3
  • Type

    conf

  • DOI
    10.1109/CEC.2010.5586182
  • Filename
    5586182