Advertisement

Improving Clustering Techniques in Wireless Sensor Networks Using Thinning Process

  • Monique Becker
  • Ashish Gupta
  • Michel Marot
  • Harmeet Singh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6821)

Abstract

We propose a rapid cluster formation algorithm using a thinning technique : rC-MHP(rapid Clustering inspired from Matérn Hard-Core Process). In order to prove its performance, it is compared with a well known cluster formation heuristic: Max-Min. Experimental results show that rC-MHP outperforms Max-Min in terms of messages needed to choose the cluster head, cluster head maintenance and memory requirement, comprehensively in sparse as well as in dense networks. We show that rC-MHP has a scalable behavior and it is very easy to implement. rC-MHP can be used as an efficient clustering technique.

Keywords

Sensor Node Wireless Sensor Network Cluster Head Node Density Channel Quality Indicator 
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.

References

  1. 1.
    Becker, M., Beylot, A.-L., Dhaou, R., Gupta, A., Kacimi, R., Marot, M.: Experimental Study: Link Quality and Deployment Issues in Wireless Sensor Networks. In: Fratta, L., Schulzrinne, H., Takahashi, Y., Spaniol, O. (eds.) NETWORKING 2009. LNCS, vol. 5550, pp. 14–25. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  2. 2.
    Gupta, A., Diallo, C., Marot, M., Becker, M.: Understanding topology challenges in the implementation of wireless sensor network for cold chain. In: IEEE Radio and Wireless Symposium, RWS 2010, New Orleans, USA (January 2010)Google Scholar
  3. 3.
    Woo, A., Tong, T., Culler, D.: Taming the underlying challenges of reliable multihop routing in sensor networks. In: SenSys (2003)Google Scholar
  4. 4.
    Stoyan, D., Kendall, W.S., Mecke, J.: Stochastic Geometry and Its Applications, 2nd edn. (September 1995)Google Scholar
  5. 5.
    Amis, A., Prakash, R., Vuong, T., Huynh, D.: Max-min d-cluster formation in wireless ad hoc networks. In: Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (2000)Google Scholar
  6. 6.
    De Clauzade De Mazieux, A.D., Marot, M., Becker, M.: Correction, Generalisation and Validation of the “Max-Min d-Cluster Formation Heuristic”. In: Akyildiz, I.F., Sivakumar, R., Ekici, E., Oliveira, J.C.d., McNair, J. (eds.) NETWORKING 2007. LNCS, vol. 4479, pp. 1149–1152. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Baccelli, F., Błaszczyszyn, B., Mühlethaler, P.: An aloha protocol for multihop mobile wireless networks. IEEE Transactions on Information Theory 52, 421–436 (2006)CrossRefGoogle Scholar
  8. 8.
    Hoydis, J., Petrova, M., Mahonen, P.: Effects of topology on local throughput-capacity of ad hoc networks. In: IEEE 19th International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2008 (2008)Google Scholar
  9. 9.
    Ieee std. 802.15.4 - 2003: Wireless medium access control (mac) and physical layer (phy) specifications for low rate wireless personal area networks (lr-wpans)Google Scholar
  10. 10.
    Srinivasan, K., Levis, P.: Rssi is under appreciated. In: Third Workshop on Embedded Networked Sensors, EmNets (2006)Google Scholar
  11. 11.
    Gupta, A., Sharma, M., Marot, M., Becker, M.: Hybridlqi: Hybrid multihoplqi for improving asymmetric links in wireless sensor networks. In: The Sixth Advanced International Conference on Telecommunications, Barcelona, Spain, May 9-15 (2010)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2011

Authors and Affiliations

  • Monique Becker
    • 1
  • Ashish Gupta
    • 1
  • Michel Marot
    • 1
  • Harmeet Singh
    • 1
  1. 1.CNRS–SAMOVAR–UMR 5157 – TELECOM SudParisEvryFrance

Personalised recommendations