Fault Detection in Sensor Network Using DBSCAN and Statistical Models

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 247)


In any sensor network one of the major challenges is to distinguish between the expected data and unexpected or faulty data. In this paper we have proposed a fault detection technique using DBSCAN and statistical model. DBSCAN is used to cluster the similar data and detect the outliers whereas statistical model is used to build a model to represent the expected behaviour of the sensor nodes. Using the expected behaviour model we have detected the faults in the data. Our experimental results on Intel Berkeley research lab dataset shows that faults have been successfully detected.


Fault detection DBSCAN Statistical model Sensor Network 


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  1. 1.
    Intel Berkeley Research lab dataset,
  2. 2.
    Chen, J., Kher, S., Somani, A.: Distributed fault detection of wireless sensor networks. In: Proceedings of the 2006 Workshop on Dependability Issues in Wireless Ad Hoc Networks and Sensor Networks, pp. 65–72. ACM (2006)Google Scholar
  3. 3.
    Ding, M., Chen, D., Xing, K., Cheng, X.: Localized fault-tolerant event boundary detection in sensor networks. In: Proceedings of the 24th Annual Joint Conference of the IEEE Computer and Communications Societies, INFOCOM 2005, vol. 2, pp. 902–913. IEEE (2005)Google Scholar
  4. 4.
    Gaber, M.: Data stream processing in sensor networks. Learning from Data Streams, p. 41 (2007)Google Scholar
  5. 5.
    Koushanfar, F., Potkonjak, M., Sangiovanni-Vincentelli, A.: On-line fault detection of sensor measurements. In: Proceedings of IEEE Sensors, vol. 2, pp. 974–979. IEEE (2003)Google Scholar
  6. 6.
    Krishnamachari, B., Iyengar, S.: Distributed bayesian algorithms for fault-tolerant event region detection in wireless sensor networks. IEEE Transactions on Computers 53(3), 241–250 (2004)CrossRefGoogle Scholar
  7. 7.
    Lee, M., Choi, Y.: Fault detection of wireless sensor networks. Computer Communications 31(14), 3469–3475 (2008)CrossRefGoogle Scholar
  8. 8.
    Lemos, A., Caminhas, W., Gomide, F.: Adaptive fault detection and diagnosis using an evolving fuzzy classifier. Information Sciences (2011)Google Scholar
  9. 9.
    Luo, X., Dong, M., Huang, Y.: On distributed fault-tolerant detection in wireless sensor networks. IEEE Transactions on Computers 55(1), 58–70 (2006)CrossRefGoogle Scholar
  10. 10.
    Ma, X., Yang, D., Tang, S., Luo, Q., Zhang, D., Li, S.: Online mining in sensor networks. In: Jin, H., Gao, G.R., Xu, Z., Chen, H. (eds.) NPC 2004. LNCS, vol. 3222, pp. 544–550. Springer, Heidelberg (2004), CrossRefGoogle Scholar
  11. 11.
    Ni, K., Ramanathan, N., Chehade, M., Balzano, L., Nair, S., Zahedi, S., Kohler, E., Pottie, G., Hansen, M., Srivastava, M.: Sensor network data fault types. ACM Transactions on Sensor Networks (TOSN) 5(3), 25 (2009)CrossRefGoogle Scholar
  12. 12.
    Ruiz, L., Siqueira, I., Wong, H., Nogueira, J., Loureiro, A., et al.: Fault management in event-driven wireless sensor networks. In: Proceedings of the 7th ACM International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems, pp. 149–156. ACM (2004)Google Scholar
  13. 13.
    Shell, J., Coupland, S., Goodyer, E.: Fuzzy data fusion for fault detection in wireless sensor networks. In: 2010 UK Workshop on Computational Intelligence (UKCI), pp. 1–6. IEEE (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department Of P.G Studies and Research in Computer ScienceMangalore UniversityMangaloreIndia

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