• DocumentCode
    2044974
  • Title

    Percolation-theory based density derivations of wireless sensor network nodes for preventing exposure paths

  • Author

    Liang Liu ; Xi Zhang ; Huadong Ma

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Texas A&M Univ., College Station, TX
  • fYear
    2008
  • fDate
    19-21 March 2008
  • Firstpage
    836
  • Lastpage
    841
  • Abstract
    Deriving the critical density to achieve region coverage for random sensor-nodes deployments is a fundamentally important problem in the area of wireless sensor networks. Most existing works on region coverage is based on the omnidirectional sensing model, and focus on full coverage model which ensures that every point in the deployment region is covered. In this paper, we consider coverage problem from a different point of view. We propose a percolation theory based model to solve the exposure path problem for omnidirectional and directional sensor networks, respectively. We start with converting the exposure path problem into a typical site percolation model, and then derive the critical densities according to the percolation threshold under random sensor deployment where sensors are deployed according to a 2-dimensional Poisson process. We evaluate our proposed model by simulations.
  • Keywords
    stochastic processes; wireless sensor networks; 2D Poisson process; density derivations; directional sensor networks; exposure path prevention; omnidirectional sensing model; percolation theory; percolation threshold; random sensor nodes deployments; wireless sensor network; Information systems; Intelligent networks; Intelligent sensors; Intrusion detection; Laboratories; Object detection; Predictive models; Sensor phenomena and characterization; Telecommunications; Wireless sensor networks; Coverage; directional sensing model; exposure path; percolation theory; wireless sensor networks;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Sciences and Systems, 2008. CISS 2008. 42nd Annual Conference on
  • Conference_Location
    Princeton, NJ
  • Print_ISBN
    978-1-4244-2246-3
  • Electronic_ISBN
    978-1-4244-2247-0
  • Type

    conf

  • DOI
    10.1109/CISS.2008.4558636
  • Filename
    4558636