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A tuned fuzzy logic relocation model in WSNs using particle swarm optimization

Conference Paper


Abstract


  • In harsh and hostile environments, swift relocation of currently deployed nodes in the absence of centralized paradigm is a challenging issue in WSNs. Reducing the burden of centralized relocation paradigms by the distributed movement models comes at the price of unpleasant oscillations and excessive movements due to nodes' local and limited interactions. If the nodes' careless movements in the distributed relocation models are not properly addressed, their power will be exhausted. Therefore, in order to exert proper amount of virtual radial/angular push/pull forces among the nodes, a fuzzy logic relocation model is proposed and by considering linear combination of the presented performance metric(s)(i.e. coverage, uniformity, and average movement), its parameters are locally and globally tuned by particle swarm optimization(PSO). In order to tune fuzzy parameters locally and globally, PSO benefits respectively from nodes' neighbours within different ranges and all the given deployed area. Performance of locally and globally tuned fuzzy relocation models is compared with one another in addition to the distributed self-spreading algorithm (DSSA). It is shown that by applying PSO to the linear combinations of desired metric(s) to obtain tuned fuzzy parameters, the relocation model outperforms and/or is comparable to DSSA in one or more performance metric(s).

Authors


  •   Rafiei, Ali (external author)
  •   Maali, Yashar (external author)
  •   Abolhasan, Mehran (external author)
  •   Franklin, Daniel R. (external author)
  •   Safaei, Farzad
  •   Smith, Stephen (external author)

Publication Date


  • 2013

Citation


  • Rafiei, A., Maali, Y., Abolhasan, M., Franklin, D. R., Safaei, F. & Smith, S. (2013). A tuned fuzzy logic relocation model in WSNs using particle swarm optimization. IEEE 78th Vehicular Technology Conference (pp. 1-5). United States: IEEE.

Scopus Eid


  • 2-s2.0-84893274093

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/2020

Has Global Citation Frequency


Start Page


  • 1

End Page


  • 5

Place Of Publication


  • United States

Abstract


  • In harsh and hostile environments, swift relocation of currently deployed nodes in the absence of centralized paradigm is a challenging issue in WSNs. Reducing the burden of centralized relocation paradigms by the distributed movement models comes at the price of unpleasant oscillations and excessive movements due to nodes' local and limited interactions. If the nodes' careless movements in the distributed relocation models are not properly addressed, their power will be exhausted. Therefore, in order to exert proper amount of virtual radial/angular push/pull forces among the nodes, a fuzzy logic relocation model is proposed and by considering linear combination of the presented performance metric(s)(i.e. coverage, uniformity, and average movement), its parameters are locally and globally tuned by particle swarm optimization(PSO). In order to tune fuzzy parameters locally and globally, PSO benefits respectively from nodes' neighbours within different ranges and all the given deployed area. Performance of locally and globally tuned fuzzy relocation models is compared with one another in addition to the distributed self-spreading algorithm (DSSA). It is shown that by applying PSO to the linear combinations of desired metric(s) to obtain tuned fuzzy parameters, the relocation model outperforms and/or is comparable to DSSA in one or more performance metric(s).

Authors


  •   Rafiei, Ali (external author)
  •   Maali, Yashar (external author)
  •   Abolhasan, Mehran (external author)
  •   Franklin, Daniel R. (external author)
  •   Safaei, Farzad
  •   Smith, Stephen (external author)

Publication Date


  • 2013

Citation


  • Rafiei, A., Maali, Y., Abolhasan, M., Franklin, D. R., Safaei, F. & Smith, S. (2013). A tuned fuzzy logic relocation model in WSNs using particle swarm optimization. IEEE 78th Vehicular Technology Conference (pp. 1-5). United States: IEEE.

Scopus Eid


  • 2-s2.0-84893274093

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/2020

Has Global Citation Frequency


Start Page


  • 1

End Page


  • 5

Place Of Publication


  • United States