Low energy clustering in BAN based on fuzzy simulated evolutionary computation

Jie Zhou; Eryk Dutkiewicz; Ren Ping Liu; Gengfa Fang; Yuanan Liu

doi:10.4108/eai.28-9-2015.2261426

Low energy clustering in BAN based on fuzzy simulated evolutionary computation

Jie Zhou, Eryk Dutkiewicz, Ren Ping Liu, Gengfa Fang, Yuanan Liu

School of Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	BodyNets 2015
Subtitle of host publication	Proceedings of the 10th EAI International Conference on Body Area Networks
Place of Publication	New York
Publisher	Association for Computing Machinery
Pages	221-227
Number of pages	7
ISBN (Print)	9781631900846
DOIs	https://doi.org/10.4108/eai.28-9-2015.2261426
Publication status	Published - 2015
Event	International Conference on Body Area Networks (10th : 2015) - Sydney Duration: 28 Sept 2015 → 30 Sept 2015

Conference

Conference	International Conference on Body Area Networks (10th : 2015)
City	Sydney
Period	28/09/15 → 30/09/15

Keywords

wireless sensor networks
simulated evolutionary computation
fuzzy controller

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10.4108/eai.28-9-2015.2261426

Cite this

@inproceedings{049e7dae41134ce1b13844610e1d97a5,

title = "Low energy clustering in BAN based on fuzzy simulated evolutionary computation",

abstract = "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.",

keywords = "wireless sensor networks, simulated evolutionary computation, fuzzy controller",

author = "Jie Zhou and Eryk Dutkiewicz and Liu, {Ren Ping} and Gengfa Fang and Yuanan Liu",

year = "2015",

doi = "10.4108/eai.28-9-2015.2261426",

language = "English",

isbn = "9781631900846",

pages = "221--227",

booktitle = "BodyNets 2015",

publisher = "Association for Computing Machinery",

note = "International Conference on Body Area Networks (10th : 2015) ; Conference date: 28-09-2015 Through 30-09-2015",

}

Zhou, J, Dutkiewicz, E, Liu, RP, Fang, G & Liu, Y 2015, Low energy clustering in BAN based on fuzzy simulated evolutionary computation. in BodyNets 2015: Proceedings of the 10th EAI International Conference on Body Area Networks. Association for Computing Machinery, New York, pp. 221-227, International Conference on Body Area Networks (10th : 2015), Sydney, 28/09/15. https://doi.org/10.4108/eai.28-9-2015.2261426

Low energy clustering in BAN based on fuzzy simulated evolutionary computation. / Zhou, Jie; Dutkiewicz, Eryk; Liu, Ren Ping et al.
BodyNets 2015: Proceedings of the 10th EAI International Conference on Body Area Networks. New York: Association for Computing Machinery, 2015. p. 221-227.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

T1 - Low energy clustering in BAN based on fuzzy simulated evolutionary computation

AU - Zhou, Jie

AU - Dutkiewicz, Eryk

AU - Liu, Ren Ping

AU - Fang, Gengfa

AU - Liu, Yuanan

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

KW - wireless sensor networks

KW - simulated evolutionary computation

KW - fuzzy controller

UR - http://www.scopus.com/inward/record.url?scp=85002044957&partnerID=8YFLogxK

U2 - 10.4108/eai.28-9-2015.2261426

DO - 10.4108/eai.28-9-2015.2261426

M3 - Conference proceeding contribution

AN - SCOPUS:85002044957

SN - 9781631900846

SP - 221

EP - 227

BT - BodyNets 2015

PB - Association for Computing Machinery

CY - New York

T2 - International Conference on Body Area Networks (10th : 2015)

Y2 - 28 September 2015 through 30 September 2015

ER -

Low energy clustering in BAN based on fuzzy simulated evolutionary computation

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Keywords

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