Low energy clustering in BAN based on fuzzy simulated evolutionary computation

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

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

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 languageEnglish
Title of host publicationBodyNets 2015
Subtitle of host publicationProceedings of the 10th EAI International Conference on Body Area Networks
Place of PublicationNew York
PublisherAssociation for Computing Machinery
Pages221-227
Number of pages7
ISBN (Print)9781631900846
DOIs
Publication statusPublished - 2015
EventInternational Conference on Body Area Networks (10th : 2015) - Sydney
Duration: 28 Sep 201530 Sep 2015

Conference

ConferenceInternational Conference on Body Area Networks (10th : 2015)
CitySydney
Period28/09/1530/09/15

Keywords

  • wireless sensor networks
  • simulated evolutionary computation
  • fuzzy controller

Fingerprint Dive into the research topics of 'Low energy clustering in BAN based on fuzzy simulated evolutionary computation'. Together they form a unique fingerprint.

Cite this