Low energy clustering in BAN based on fuzzy simulated evolutionary computation

Zhou, J; Dutkiewicz, E; Liu, RP; Fang, G; Liu, Y

Low energy clustering in BAN based on fuzzy simulated evolutionary computation

Zhou, J Dutkiewicz, E

Liu, RP

Fang, G Liu, Y

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Publication Type:: Journal Article
Citation:: BodyNets International Conference on Body Area Networks, 2015
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Zhou, J	en_US
dc.contributor.author	Dutkiewicz, E https://orcid.org/0000-0002-4268-9286	en_US
dc.contributor.author	Liu, RP https://orcid.org/0000-0001-7001-6305	en_US
dc.contributor.author	Fang, G	en_US
dc.contributor.author	Liu, Y	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	BodyNets International Conference on Body Area Networks, 2015	en_US
dc.identifier.uri	http://hdl.handle.net/10453/100142
dc.description.abstract	© 2015 ICST. A low energy clustering method of body area networks based on fuzzy simulated evolutionary computation is proposed in this paper. To reduce communication energy consumption, we also designed a fuzzy controller to dynamically adjust the crossover and mutation probability. Simulations are conducted by using the proposed method, the clustering methods based on the particle swarm optimization and the method based on the quantum evolutionary algorithm. Results show that the energy consumption of the proposed method decreased compared with the other two methods, which means that the proposed method significantly improves the energy efficiency.	en_US
dc.relation.ispartof	BodyNets International Conference on Body Area Networks	en_US
dc.relation.isbasedon	10.4108/eai.28-9-2015.2261426	en_US
dc.title	Low energy clustering in BAN based on fuzzy simulated evolutionary computation	en_US
dc.type	Journal Article
utslib.for	100510 Wireless Communications	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 - GBDTC - Global Big Data Technologies
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

© 2015 ICST. A low energy clustering method of body area networks based on fuzzy simulated evolutionary computation is proposed in this paper. To reduce communication energy consumption, we also designed a fuzzy controller to dynamically adjust the crossover and mutation probability. Simulations are conducted by using the proposed method, the clustering methods based on the particle swarm optimization and the method based on the quantum evolutionary algorithm. Results show that the energy consumption of the proposed method decreased compared with the other two methods, which means that the proposed method significantly improves the energy efficiency.

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http://hdl.handle.net/10453/100142