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

Rafiei, A; Maali, Y; Abolhasan, M; Franklin, D; Safaei, F; Smith, S

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

Rafiei, A Maali, Y Abolhasan, M

Franklin, D

Safaei, F Smith, S

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Publication Type:: Conference Proceeding
Citation:: IEEE Vehicular Technology Conference, 2013
Issue Date:: 2013-12-01

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Field	Value	Language
dc.contributor.author	Rafiei, A	en_US
dc.contributor.author	Maali, Y	en_US
dc.contributor.author	Abolhasan, M https://orcid.org/0000-0002-4282-6666	en_US
dc.contributor.author	Franklin, D https://orcid.org/0000-0002-9563-5943	en_US
dc.contributor.author	Safaei, F	en_US
dc.contributor.author	Smith, S	en_US
dc.date.issued	2013-12-01	en_US
dc.identifier.citation	IEEE Vehicular Technology Conference, 2013	en_US
dc.identifier.isbn	9781467361873	en_US
dc.identifier.issn	1550-2252	en_US
dc.identifier.uri	http://hdl.handle.net/10453/27613
dc.description.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). Copyright © 2013 by the Institute of Electrical and Electronic Engineers, Inc.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0879507
dc.relation.ispartof	IEEE Vehicular Technology Conference	en_US
dc.relation.isbasedon	10.1109/VTCFall.2013.6692070	en_US
dc.title	A tuned fuzzy logic relocation model in WSNs using particle swarm optimization	en_US
dc.type	Conference Proceeding
utslib.for	100510 Wireless Communications	en_US
dc.location.activity	Las Vegas, USA	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

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). Copyright © 2013 by the Institute of Electrical and Electronic Engineers, Inc.

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http://hdl.handle.net/10453/27613