Energy-Efficient Clustering Algorithm in Wireless Sensor Networks

  • DaeHwan Kim
  • SangHak Lee
  • We Duke Cho
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4096)


Wireless sensor networks is a key technology for new ways of interaction between computers and the physical environment. However, the energy constrained and limited computing resources of the sensor nodes present major challenges in gathering data. Since sensor nodes are densely deployed, redundant data may occur. While cluster-based data gathering is efficient at energy and bandwidth, it’s difficult to cluster network efficiently. In this work, a new distributed clustering algorithm for ubiquitous sensor network is presented. Clustering is based on the distance between nodes and the number in a cluster for wireless sensor networks. Simulation results show that the proposed algorithm balances the energy dissipation over the whole network thus increase the amount of data delivery to the sink.


Sensor Network Sensor Node Cluster Algorithm Wireless Sensor Network Network Lifetime 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Akyildiz, I.F., Weilian, S., Sankarasubramaniam, Y., Cayirci, E.: A Survey on Sensor Networks. IEEE Comm. Mag., 102–114 (2002)Google Scholar
  2. 2.
    National Research Council: Embedded, Everywhere: A Research Agenda for Networked Systems of Embedded Computers. National Academy Press (2001)Google Scholar
  3. 3.
    Ganesan, D., Cerpa, A., Ye, W., Yu, Y., Zhao, J., Estrin, D.: Networking Issues in Wireless Sensor Networks. Journal of Parallel and Distributed Computing (JPDC) Special issue on Frontier in Distributed Sensor Networks, 799–814 (2003)Google Scholar
  4. 4.
    Estrin, E., Govindan, R., Heidemann, J., Kumar, S.: Next Century Challenges: Scalable Coordination in Sensor Networks. In: Proc. of the 5th Annual International Conference on Mobile computing and Networks, pp. 263–270 (1999)Google Scholar
  5. 5.
    Heinzelman, W., Chandrakasan, A., Balakrishnan, H.: An Application-Specific Protocol Architecture for Wireless Microsensor Networks. IEEE Trans. on Wireless Comm., 660–669 (2002)Google Scholar
  6. 6.
    Xu, Y., Heidemann, J., Estrin, D.: Geography-Informed Energy Conservation for Ad Hoc Routing. In: Proc. of the ACM/IEEE International Conference on Mobile Computing and Networking, pp. 70–84 (2001)Google Scholar
  7. 7.
    Chen, B., Jamieson, K., Balakrishnan, H., Morris, R.: Span: an Energy-Efficient Coordination Algorithm for Topology Maintenance in Ad Hoc Wireless Networks. ACM Wireless Networks, 85–96 (2002)Google Scholar
  8. 8.
    Cerpa, A., Estrin, D.: ASCENT: Adaptive Self-Configuring Sensor Networks Topologies. In: Proc. of IEEE INFOCOM, pp. 1278–1287 (2002)Google Scholar
  9. 9.
    Kwon, T., Gerla, M., Varma, V., Barton, M., Hsing, T.: Efficient Flooding with Passive Clustering-An Overhead-Free Selective Forward Mechanism for Ad Hoc Sensor Networks. Proc. of the IEEE, 1210–1220 (2002)Google Scholar
  10. 10.
    Kawadia, V., Kumar, P.: Power Control and Clustering in Ad Hoc Networks. In: Proc. of IEEE INFOCOM, pp. 459–469 (2003)Google Scholar
  11. 11.
    Mhatre, V., Rosenberg, C.: Design guidelines for wireless sensor networks: communication, clustering and aggregation. Elsevier Ad Hoc Networks, 45–63 (2003)Google Scholar
  12. 12.
    Tomoyuki, O., Shinji, I., Yoshiaki, K., Kenji, I.: An Adaptive Multihop Clustering Scheme for Ad Hoc Networks with High Mobility. IEICE Trans. on Fundamentals, 1689–1697 (2003)Google Scholar
  13. 13.
    Chan, H., Perrig, A.: ACE: An Emergent Algorithm for Highly Uniform Cluster Formation. In: 2004 European Workshop on Sensor Networks, pp. 154–171 (2004)Google Scholar
  14. 14.
    Younis, O., Fahmy, S.: Distributed Clustering in Ad-hoc Sensor Networks: A Hybrid, Energy-Efficient Approach. In: IEEE INFOCOM, pp. 629–640 (2004)Google Scholar
  15. 15.
    Kirkpatrick, S., Gelatt, C., Vecchi, M.: Optimization by Simulated Annealing. Science, 13–22 (1983)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • DaeHwan Kim
    • 1
    • 2
  • SangHak Lee
    • 1
  • We Duke Cho
    • 2
  1. 1.Intelligent IT System CenterKorea Electronics Technology InstituteGyeonggi-doSouth Korea
  2. 2.Department of Electrical and Computer EngineeringAjou UniversitySuwonSouth Korea

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