Journal of South China University of Technology (Natural Science Edition) ›› 2011, Vol. 39 ›› Issue (10): 13-18.doi: 10.3969/j.issn.1000-565X.2011.10.003

• Electronics, Communication & Automation Technology • Previous Articles     Next Articles

Dual Selection Mechanism-Based Clustering Algorithm for Wireless Sensor Networks

Sun Zhong-gao1,2  Zheng Zi-wei1,3  Xu Shao-juan4   

  1. 1. College of Information Science and Technology,Dalian Maritime University,Dalian 116026,Liaoning,China; 2. School of Physics and Electronic Technology,Liaoning Normal University,Dalian 116029,Liaoning,China; 3. College of Information Science and Engineering,Ningbo University,Ningbo 315211,Zhejiang,China; 4. City Institute,Dalian University of Technology,Dalian 116600,Liaoning,China
  • Received:2011-05-03 Revised:2011-07-30 Online:2011-10-25 Published:2011-09-01
  • Contact: 郑紫微(1975-)，男，教授，博士生导师，主要从事无线通信技术研究． E-mail: ziwei_zheng@yahoo．com．cn E-mail:sunzg98@ sina.com
  • About author:孙中皋(1978-) ，男，博士生，主要从事无线传感器网络研究．
  • Supported by:

    国家自然科学基金资助项目( 60772119，60972063) ; 国家科技重大专项( 2011ZX03002-004-02) ; 浙江省杰出青年科学基金资助项目( R1110416) ; 教育部新世纪优秀人才支持计划项目( NCET-08-0706) ; 辽宁省高等学校优秀人才支持计划项目( 2008RC56)

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Abstract:

In order to efficiently utilize the energy in wireless sensor networks,a dual selection mechanism-based clustering algorithm ( DSMCA) effectively combining the voting mechanism with the time-driven one is proposed. In the voting process,a node casts a vote for each neighbor node with higher residual energy. The poll depends on the comprehensive evaluation value of the multiple attributes of neighbor nodes,and the weight coefficient of the multiple attributes is determined by means of the entropy weighting coefficient method. After the voting,each node maps its poll into a certain length of waiting time to participate in cluster head competition by using a conversion function. Moreover,a node with a higher poll produces a shorter time,thus being chosen as a cluster head prior to other nodes. Simulation results show that DSMCA balances the energy consumption among sensor nodes and effectively prolongs the lifetime of the sensor network.

Key words: wireless sensor networks, clustering algorithms, energy efficiency, entropy weighting coefficient method

CLC Number: