NDLI: A Tuned Fuzzy Logic Relocation Model in WSNs Using Particle Swarm Optimization

Content Provider	IEEE Xplore Digital Library
Author	Rafiei, A. Maali, Y. Abolhasan, M. Franklin, D. Safaei, F. Smith, S.
Copyright Year	2013
Description	Author affiliation: Fac. of Inf., Univ. of Wollongong, Wollongong, NSW, Australia (Safaei, F.) \|\| Fac. of Eng. & Inf. Technol., Univ. of Technol., Sydney, Sydney, NSW, Australia (Rafiei, A.; Maali, Y.; Abolhasan, M.; Franklin, D.) \|\| Dept. of Comput., Macquarie Univ., Sydney, NSW, Australia (Smith, S.)
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).
Starting Page	1
Ending Page	5
File Size	379394
Page Count	5
File Format	PDF
ISBN	9781467361873
ISSN	10903038
DOI	10.1109/VTCFall.2013.6692070
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-02
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Force Boundary conditions Decision support systems Measurement Fuzzy logic Particle swarm optimization Mathematical model
Content Type	Text
Resource Type	Article

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