Wireless Personal Communications

, Volume 56, Issue 4, pp 715–731 | Cite as

LUCA: An Energy-efficient Unequal Clustering Algorithm Using Location Information for Wireless Sensor Networks

  • Sungryoul Lee
  • Han Choe
  • Byoungchang Park
  • Yukyoung Song
  • Chong-kwon Kim
Article

Abstract

Over the last several years, various clustering algorithms for wireless sensor networks have been proposed to prolong network lifetime. Most clustering algorithms provide an equal cluster size using node’s ID, degree and etc. However, many of these algorithms heuristically determine the cluster size, even though the cluster size significantly affects the energy consumption of the entire network. In this paper, we present a theoretical model and propose a simple clustering algorithm called Location-based Unequal Clustering Algorithm (LUCA), where each cluster has a different cluster size based on its location information which is the distance between a cluster head and a sink. In LUCA, in order to minimize the energy consumption of entire network, a cluster has a larger cluster size as increasing distance from the sink. Simulation results show that LUCA achieves better performance than conventional equal clustering algorithm for energy efficiency.

Keywords

Energy efficiency Location information Unequal clustering Wireless sensor networks 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ai, J., Turgut, D., & Boloni, L. (2005). A cluster-based energy balancing scheme in heterogeneous wireless sensor networks. In Proceedings of the 4th international conference on networking (ICN). April, 2005.Google Scholar
  2. 2.
    Amis, A. D., Prakash, R., Vuong, T. H. P., & Huynh, D. (2000). Max–min d-cluster formation in wireless ad hoc networks. In Proceedings of INFOCOM. March, 2000.Google Scholar
  3. 3.
    Akyildiz I. F., Su W., Sankarasubramnian Y., Cayirci E. (2002) A survey on sensor networks. IEEE Communications Magazine 40: 102–114CrossRefGoogle Scholar
  4. 4.
    Baker D. J., Ephremides A. (1981) The architectural organization of a mobile radio network via a distributed algorithm. IEEE Transactions on Communications 29(11): 1694–1701CrossRefGoogle Scholar
  5. 5.
    Bandyopadhyay, S., & Coyle, E. J. (2003). An energy efficient hierarchical clustering algorithm for wireless sensor networks. In Proceedings of IEEE INFOCOM. April, 2003.Google Scholar
  6. 6.
    Bettstetter, C. (2004). The cluster density of a distributed clustering algorithm in ad hoc networks. In Proceedings of IEEE, ICC. June, 2004.Google Scholar
  7. 7.
    Buettner, M., Yee, G. V., Anderson, E., & Han, R. (2006). X-MAC: A short preamble MAC protocol for duty-cycled wireless sensor networks. In Proceedings of ACM SenSys. November, 2006.Google Scholar
  8. 8.
    Cao, Y., He, C., & Wang, J. (2005). A backoff-based energy efficient clustering algorithm for wireless sensor networks. In Lecture notes in computer science (LNCS) (Vol. 3794). Berlin, Heidelberg: Springer.Google Scholar
  9. 9.
    Chan, H., & Perrig, A. (2004). ACE: An emergent algorithm for highly uniform cluster formation. In Proceedings of the first European workshop on sensor networks (EWSN). January 2004.Google Scholar
  10. 10.
    Culler D., Estrin D., Srivastava M. (2004) Overview of sensor networks. IEEE Computer 37(8): 41–49Google Scholar
  11. 11.
    Ephremides, A., Wieselthier, J. E., & Baker, D. J. (1987). A design concept for reliable mobile radio networks with frequency hopping signaling. In Proceedings of IEEE (no. 1, Vol. 75, pp. 56–73).Google Scholar
  12. 12.
    Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of 33rd annual Hawaii international conference on system sciences (HICSS ’00) (Vol. 2, pp. 3005–3014). January, 2000.Google Scholar
  13. 13.
    Johnson, D. B., & Maltz, D. A. (1996). Dynamic source routing in ad hoc wireless networks. In Mobile computing (Vol. 353, pp. 153–181). New York: Kluwer.Google Scholar
  14. 14.
    Lee, H. Y., Seah, W. K. G., & Sun, P. (2006). Energy implications of clustering in heterogeneous wireless sensor networks—An analytical view. In IEEE international symposium on personal, indoor and mobile radio communications (PIMRC). September, 2006.Google Scholar
  15. 15.
    Li, C., Ye, M., Chen, G., & Wu, J. (2005). An energy-efficient unequal clustering mechanism for wireless sensor networks. In Proceedings of the 2nd IEEE international conference on mobile ad-hoc and sensor systems (MASS’05). November 2005.Google Scholar
  16. 16.
    Lin C. R., Gerla M. (1997) Adaptive clustering for mobile wireless networks. IEEE Journal of Selected Areas in Communications (JSAC) 15(7): 1265–1275CrossRefGoogle Scholar
  17. 17.
    Ma M., Yang Y. (2007) Adaptive triangular deployment algorithm for unattended mobile sensor networks. IEEE Transactions on Computers 56: 946–947CrossRefMathSciNetGoogle Scholar
  18. 18.
    Manjeshwar, A., & Agrawal, D. (2001). TEEN: A routing protocol for enhanced efficiency in wireless sensor networks. In Proceedings of 15th international parallel and distributed processing symposium (pp. 2009–2015). April, 2001.Google Scholar
  19. 19.
    Park, B., Lee, S., Song, Y., & Kim, C. (2004). Energy-efficient optimal clustering size for wireless sensor networks. In 31th Korea information science society (KISS) fall conference. October, 2004.Google Scholar
  20. 20.
    Perkins, C. E., & Royer, E. M. (1999). Ad hoc on-demand distance vector routing. In Proceedings of the 2nd IEEE workshop on mobile computing systems and applications, February, 1999.Google Scholar
  21. 21.
    Qin, M., & Zimmermann, R. (2005). An energy-efficient voting-based clustering algorithm for sensor networks. In Proceedings of ACIS international workshop self-assembling wireless networks (SAWN). May, 2005.Google Scholar
  22. 22.
    Shu, T., Krunz, M., & Vrudhula, S. (2005). Power balanced coverage-time optimization for clustered wireless sensor networks. In Proceedings of ACM MobiHoc’05 (pp. 111–120). May 2005.Google Scholar
  23. 23.
    Soro, S., & Heinzelman, W. (2005). Prolonging the Lifetime of Wireless Sensor Networks via Unequal Clustering. In Proceedings of 5th international workshop on algorithms for wireless, mobile, ad hoc and sensor networks (WMAN ’05). April, 2005.Google Scholar
  24. 24.
    Tian, Q., & Coyle, E. J. (2006). Optimal distributed detection in clustered wireless sensor networks: The weighted median. In Proceedings of IEEE INFOCOM 2006. April, 2006.Google Scholar
  25. 25.
    Wen C. Y., Sethares W. A. (2005) Automatic decentralized clustering for wireless sensor networks. EURASIP Journal on Wireless Communications and Networking 5(5): 686–697CrossRefGoogle Scholar
  26. 26.
    Wen, C. Y., & Sethares, W. A. (2006). Adaptive decentralized re-clustering for wireless sensor networks. In Proceedings of IEEE international conference on systems, man, and cybernetics. October, 2006.Google Scholar
  27. 27.
    Ye, W., Heidenmann, J., & Estrin, D. (2002). An energy-efficient MAC protocol for wireless sensor networks. In Proceedings of IEEE INFOCOM. June, 2002.Google Scholar

Copyright information

© Springer Science+Business Media, LLC. 2009

Authors and Affiliations

  • Sungryoul Lee
    • 1
  • Han Choe
    • 1
  • Byoungchang Park
    • 2
  • Yukyoung Song
    • 3
  • Chong-kwon Kim
    • 1
  1. 1.Dept. of Computer Science and EngineeringSeoul National UniversitySeoulKorea
  2. 2.Future Technology Laboratory, KTSeoulKorea
  3. 3.Intellectual Property CenterLG ElectronicsSeoulKorea

Personalised recommendations