EAI Endorsed Transactions on Energy Web 16(8): e5

Research Article

Low Energy Clustering in BAN Based on Fuzzy Simulated Evolutionary Computation

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  • @ARTICLE{10.4108/eai.28-9-2015.2261426,
        author={Jie Zhou and Eryk Dutkiewicz and Ren Ping Liu and Gengfa Fang and Yuanan Liu},
        title={Low Energy Clustering in BAN Based on Fuzzy Simulated Evolutionary Computation},
        journal={EAI Endorsed Transactions on Energy Web},
        volume={16},
        number={8},
        publisher={ACM},
        journal_a={EW},
        year={2015},
        month={12},
        keywords={wireless sensor networks, simulated evolutionary computation, fuzzy controller},
        doi={10.4108/eai.28-9-2015.2261426}
    }
    
  • Jie Zhou
    Eryk Dutkiewicz
    Ren Ping Liu
    Gengfa Fang
    Yuanan Liu
    Year: 2015
    Low Energy Clustering in BAN Based on Fuzzy Simulated Evolutionary Computation
    EW
    EAI
    DOI: 10.4108/eai.28-9-2015.2261426
Jie Zhou1, Eryk Dutkiewicz1, Ren Ping Liu2,*, Gengfa Fang1, Yuanan Liu3
  • 1: Macquarie University
  • 2: CSIRO
  • 3: Beijing University of Posts and Telecommunications
*Contact email: ren.liu@csiro.au

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 compare with the other two methods, which means the proposed method significantly improves the energy efficiency.