A Density-Based Clustering Paradigm to Detect Faults in Wireless Sensor Network

  • Sourav Kumar Bhoi
  • Sanjaya Kumar Panda
  • Pabitra Mohan Khilar
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 174)

Abstract

Event detection using wireless sensor network is an emerging area of research nowadays in distributed environment. In geographical regions, it is a great area of research to set the sensors for event (volcanic eruptions) detection by taking local decisions. But due to failure of nodes in these regions, it is difficult to detect the event. In this paper, we have proposed an approach of detecting the fault by using Density-Based Clustering method. Our main idea is to form a density-based cluster in which the nodes within the cluster have same behavior (faulty or active). The cluster is formed by using ε-Neighborhood, in which the Density-Reachability and Density-Connectivity concepts are used to get the Density-Based Cluster. By this method, the faults are detected as the nodes which are not in the cluster. Our observation shows better results in modeling a Fault-Detection Paradigm to detect the faults in the network.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Applegate, D., Breslau, L., Cohen, E.: Coping with network failures: routing strate-gies for optimal demand oblivious restoration. In: Proc. ACM SIGMETRICS, pp. 270–281 (2004)Google Scholar
  2. 2.
    Brooks, R., Griffin, C., Friedlander, D.: Self-organized distributed sensor network entity tracking. International Journal of High Performance Computing Applications 16(3) (2002)Google Scholar
  3. 3.
    Chamberland, J., Veeravalli, V.: Distributed detection in sensor networks. IEEE on Signal Processing 51(2) (2003)Google Scholar
  4. 4.
    Chen, D., Kintala, D., Garg, S., Trivedi, K.S.: Dependability enhancement for IEEE 802.11 wirelessLAN with redundancy techniques. In: Proceedings of the International Conference on Dependable Systems and Networks, pp. 521–528 (June 2003)Google Scholar
  5. 5.
    Chen, Q., Lam, K., Fan, P.: Comments on distributed Bayesian algorithms for fault-tolerant event region detection in wireless sensor networks. IEEE Transactions on Computers 54(9) (September 2005)Google Scholar
  6. 6.
    Feyessa, T., Bikdash, M.: Geographically-sensitive network centrality and survivability assessment. In: 2011 IEEE 43rd Southeastern Symposium on System Theory (SSST), pp. 18–23 (March 2011)Google Scholar
  7. 7.
    Forouzan, B.A.: Data Communication and Networking, 4th edn. Tata Mcgraw-Hill (2006)Google Scholar
  8. 8.
    Han, J., Kamber, M.: Data mining, 2nd edn. Elsevier (2006)Google Scholar
  9. 9.
    Krishnamachari, B., Iyengar, S.: Distributed bayesian algorithms for fault-tolerant event region detection in wireless sensor networks. IEEE Transactions on Computers 53(3) (March 2004)Google Scholar
  10. 10.
    Li, R., Wang, X., Jiang, X.: Network survivability against region failure. In: 2011 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), pp. 1–6 (September 2011)Google Scholar
  11. 11.
    Luo, X., Dong, M., Huang, Y.: On distributed fault-tolerant detection in wireless sensor networks. IEEE Transactions on Neural Networks (2005) (to appear)Google Scholar
  12. 12.
    Neumayer, S., Modiano, E.: Network reliability with geographically correlated failures. In: IEEE INFOCOM 2010 (March 2010)Google Scholar
  13. 13.
    Neumayer, S., Zussman, G., Cohen, R., Modiano, E.: Assessing the vulnerability of the fiber infrastructure to disasters. In: IEEE INFOCOM 2009, pp. 1566–1574 (April 2009)Google Scholar
  14. 14.
    Ould-Ahmed-Vall, E., Riley, G.F., Heck, B.S.: A geometric-based approach to fault-tolerance in distributed detection using wireless sensor networks. School of Electrical and Computer Engineering, Georgia Institute of Technology, AtlantaGoogle Scholar
  15. 15.
    Ould-Ahmed-Vall, E., Riley, G.F., Heck, B.S.: A distributed fault tolerant algorithm for event detection using heterogeneous wireless sensor networks. In: Proceedings of the 45th IEEE Conference on Decision and Control, CDC 2006 (2006) (under review)Google Scholar
  16. 16.
    Sahoo, M., Khilar, P., Majhi, B.: A redundant neighbourhood approach to tolerate access point failure in IEEE 802.11 WLAN. In: Fourth International Conference on Industrial and Information Systems, ICIIS 2009, pp. 28–31 (December 2009)Google Scholar
  17. 17.
    Sahoo, M.N., Khilar, P.M.: Survivability of IEEE 802.11 wireless LAN against AP failure. International Journal of Computer Applications in Engineering, Technology and Sciences (IJCA- ETS), 424–428 (April 2009)Google Scholar
  18. 18.
    Sen, A., Murthy, S., Banerjee, S.: Region-based connectivity - a new paradigm for design of fault-tolerant networks. In: IEEE International Conference on High Performance Switching and Routing (HPSR), Paris, France, pp. 1–7 (June 2009)Google Scholar
  19. 19.
    Snow, A.P., Varshney, U., Malloy, A.D.: Reliability and survivability of wireless and mobile networks. IEEE Computer, 49–55 (July 2000)Google Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  • Sourav Kumar Bhoi
    • 1
  • Sanjaya Kumar Panda
    • 1
  • Pabitra Mohan Khilar
    • 1
  1. 1.Department of Computer Science and EngineeringNational Institute of TechnologyRourkelaIndia

Personalised recommendations