A High Energy Efficiency Approach Based on Fuzzy Clustering Topology for Long Lifetime in Wireless Sensor Networks

  • Quynh-Trang Lam
  • Mong-Fong Horng
  • Trong-The Nguyen
  • Jia-Nan Lin
  • Jang-Pong Hsu
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 457)


Fuzzy logic has been successfully applied in various fields of daily life. Fuzzy logic is based on non-crisp set. The characteristic function of non-crisp set is permitted to have to range value between 0 and 1. In a cluster each node is definitely not only belong a cluster but also belong more than a cluster like as the non-crisp set. Therefore, classification cluster in wireless sensor network (WSN) is a complex problem. Fuzzy c-mean algorithm (FCM) is a highly suitable for classification cluster. The paper proposes for integration of Fuzzy Logic Controller and FCM to give a solution to improve the energy efficiency of WSN. Moreover, through the simulation results the lifetime of cluster is increased by more than 55%. The paper shows that the proposed approach has been confirmed that is the better choice of high energy efficiency for longer lifetime in cluster of WSN.


Fuzzy logic Fuzzy c-mean algorithm wireless sensor network slave nodes master nodes Fuzzy Logic Controller network lifetime 


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  1. 1.
    Begonya, O., Luis, A., Christos, V.: Highly Reliable Energy-Saving MAC for Wireless Body Sensor Networks in Health care Systems. IEEE Journal on Selected Areas in Communications, 553–564 (2009)Google Scholar
  2. 2.
    Shu, H., Liang, Q., Gao, J.: Wireless Sensor Network Life time Analysis Using Interval Type-2 Fuzzy Logic Systems. IEEE Transactions on Fuzzy Systems, 416–427 (2008)Google Scholar
  3. 3.
    Tianying, W., Kun, Y., Weiyi, L., Jin, X.: An Energy-efficient Data Transfer Model of Wireless Sensor Networks Based on the Coalitional Game Theory. In: 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp. 1354–1358 (2011)Google Scholar
  4. 4.
    Jang, J.-S.R., Sun, C.T., Mizutani, E.: Neuro-Fuzzy and Soft Computing A Computational Approach to Learning and Machine Intelligence. Prentice-Hall (1997)Google Scholar
  5. 5.
    Rafik, A.A., Witold, P.: Fundamentals of a Fuzzy-Logic-Based Generalized Theory of Stability. IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics, 971–988 (2009)Google Scholar
  6. 6.
    Kaur, T., Baek, J.: A Strategic Deployment and Cluster-Header Selection for Wireless Sensor Networks. IEEE Transactions on Consumer Electronics, 1890–1897 (2009)Google Scholar
  7. 7.
    Horng, M.-F., Chen, Y.-T., Chu, S.-C., Pan, J.-S., Liao, B.-Y.: An Extensible Particles Swarm Optimization for Energy-Effective Cluster Management of Underwater Sensor Networks. In: Pan, J.-S., Chen, S.-M., Nguyen, N.T. (eds.) ICCCI 2010, Part I. LNCS (LNAI), vol. 6421, pp. 109–116. Springer, Heidelberg (2010)Google Scholar
  8. 8.
    Wang, Q.: Packet Traffic: A Good Data Source for Wireless Sensor Network Modeling and Anomaly Detection. IEEE Network, 15–21 (2011)Google Scholar
  9. 9.
    Verma, P., Yadava, R.D.S.: Fuzzy C-means Clustering Based Uncertainty Measure for Sample Weighting Boosts Pattern Classification Efficiency. In: Computational Intelligence and Signal Processing (CISP), pp. 31–35 (2012)Google Scholar
  10. 10.
    Moslem, N., Masoud, A.: Lifetime Analysis of Random Event-Driven Clustered Wireless Sensor Networks. IEEE Transactions on Mobile Computing, 1448–1458 (2011)Google Scholar
  11. 11.
    Chen, L., Gong, D.G., Wang, S.R.: Nearest Neighbor Classification by Partially Fuzzy Clustering. In: 26th International Conference on Advanced Information Networking and Applications Workshops, pp. 789–794 (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Quynh-Trang Lam
    • 1
  • Mong-Fong Horng
    • 2
  • Trong-The Nguyen
    • 2
  • Jia-Nan Lin
    • 3
  • Jang-Pong Hsu
    • 3
  1. 1.Department of Industrial ManagementNational Kaohsiung University of Applied SciencesKaohsiungTaiwan
  2. 2.Department of Electronics EngineeringNational Kaohsiung University of Applied SciencesKaohsiungTaiwan
  3. 3.Advance Multimedia Internet, Inc.TainanTaiwan

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