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Data Mining Via Minimal Spanning Tree Clustering For Prolonging Lifetime Of Wireless Sensor Networks

Author

Listed:
  • GUANGYAN HUANG

    (Advanced Test Technology Lab., Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, P. R. China)

  • XIAOWEI LI

    (Advanced Test Technology Lab., Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, P. R. China)

  • JING HE

    (Chinese Academy of Sciences Research Center, on Data Technology and Knowledge Economy, Beijing 100080, P. R. China)

  • XIN LI

    (School of Management, Royal Holloway, University of London, Egham, Surrey, TW20 0EX, UK)

Abstract

Clustering is applied in wireless sensor networks for increasing energy efficiency. Clustering methods in wireless sensor networks are different from those in traditional data mining systems. This paper proposes a novel clustering algorithm based on Minimal Spanning Tree (MST) and Maximum Energy resource on sensors named MSTME. Also, specified constrains of clustering in wireless sensor networks and several evaluation metrics are given. MSTME performs better than already known clustering methods of Low Energy Adaptive Clustering Hierarchy (LEACH) and Base Station Controlled Dynamic Clustering Protocol (BCDCP) in wireless sensor networks when they are evaluated by these evaluation metrics. Simulation results show MSTME increases energy efficiency and network lifetime compared with LEACH and BCDCP in two-hop and multi-hop networks, respectively.

Suggested Citation

  • Guangyan Huang & Xiaowei Li & Jing He & Xin Li, 2007. "Data Mining Via Minimal Spanning Tree Clustering For Prolonging Lifetime Of Wireless Sensor Networks," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 6(02), pages 235-251.
    • Handle: RePEc:wsi:ijitdm:v:06:y:2007:i:02:n:s0219622007002538
    DOI: 10.1142/S0219622007002538
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