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Unsupervised Outlier Detection in Sensor Networks Using Aggregation Tree

  • Kejia Zhang
  • Shengfei Shi
  • Hong Gao
  • Jianzhong Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4632)

Abstract

In the applications of sensor networks, outlier detection has attracted more and more attention. The identification of outliers can be used to filter false data, find faulty nodes and discover interesting events. A few papers have been published for this issue. However some of them consume too much communication, some of them need user to pre-set correct thresholds, some of them generate approximate results rather than exact ones. In this paper, a new unsupervised approach is proposed to detect global top n outliers in the network. This approach can be used to answer both snapshot queries and continuous queries. Two novel concepts, modifier set and candidate set for the global outliers, are defined in the paper. Also a commit-disseminate-verify mechanism for outlier detection in aggregation tree is provided. Using this mechanism and the these two concepts, the global top n outliers can be detected through exchanging short messages in the whole tree. Theoretically, we prove that the results generated by our approach are exact. The experimental results show that our approach is the most communication-efficient one compared with other existing methods. Moreover, our approach does not need any pre-specified threshold. It can be easily extended to multi-dimensional data, and is suitable for detecting outliers of various definitions.

Keywords

Sensor Network Sensor Node Wireless Sensor Network Outlier Detection Local Outlier 
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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References

  1. 1.
    Shnayder, V., Hempstead, M., Chen, B.R., Allen, G.W., Welsh, M.: Simulating the Power Consumption of Large-scale Sensor Network Applications. In: SenSys (2004)Google Scholar
  2. 2.
    Gupta, P., Kumar, P.R.: The Capacity of Wireless Networks. IEEE Trans. Information Theory 46(2), 388–404 (2000)CrossRefzbMATHMathSciNetGoogle Scholar
  3. 3.
    Warneke, B., Last, M., Liebowitz, B., Pister, K.: Smart Dust: Communicating with A Cubic-millimeter Computer. IEEE Computer Magazine, pp. 44–51 (January 2001)Google Scholar
  4. 4.
    Gunopulos, D., Kollios, G., Tsotras, J., Domeniconi, C.: Approximating Multi-Dimensional Aggregate Range Queries over Real Attributes. In: SIGMOD (2000)Google Scholar
  5. 5.
    Madden, S., Franklin, M.J., Hellerstein, J.M., Hong, W.: The Design of An Acquisitional Query Processor for Sensor Networks. In: SIGMOD (2003)Google Scholar
  6. 6.
    Ramaswamy, S., Rastogi, R., Shim, K.: Efficient Algorithms for Mining Outliers from Large Datasets. In: SIGMOD (2000)Google Scholar
  7. 7.
    Knorr, E., Ng, R.: Algorithms for Mining Distance-Based Outliers in Large Datasets. In: VLDB, 24–27 (1998)Google Scholar
  8. 8.
    Branch, J., Szymanski, B., Giannella, C., Wolff, R.: In-Network Outlier Detection in Wireless Sensor Networks. In: ICDCS (2006)Google Scholar
  9. 9.
    Subramaniam, S., Palpanas, T., Papadopoulos, D., Kalogeraki, V., Gunopulos, D.: Online Outlier Detection in Sensor Data Using Non-Parametric Models. In: VLDB (2006)Google Scholar
  10. 10.
    Palpanas, T., Papadopoulos, D., Kalogeraki, V., Gunopulos, D.: Distributed Deviation Detection in Sensor Networks. ACM SIGMOD 32(4), 77–82 (2003)CrossRefGoogle Scholar
  11. 11.
    Zhuang, Y., Chen, L.: In-network Outlier Cleaning for Data Collection in Sensor Networks. In: CleanDB (2006)Google Scholar
  12. 12.
    Intel Berkeley Research Lab. http://db.lcs.mit.edu/labdata/labdata.html
  13. 13.
    Crossbow Technology Inc. http://www.xbow.com/
  14. 14.
    Ash, J.N., Moses, R.L.: Outlier Compensation in Sensor Network Self-localization via the EM Algorithm. In: ICASSP (2005)Google Scholar
  15. 15.
    Jun, M.C., Jeong, H., Kuo, C.J.: Distributed Spatio-temporal Outlier Detection in Sensor Networks. In: SPIE (2005)Google Scholar
  16. 16.
    Janakiram, D., Reddy, A.M., Kumar, A.P.: Outlier Detection in Wireless Sensor Networks using Bayesian Belief Networks. In: Comsware (2006)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Kejia Zhang
    • 1
  • Shengfei Shi
    • 1
  • Hong Gao
    • 1
  • Jianzhong Li
    • 1
  1. 1.Database Research Center, School of Computer Science & Technology, Harbin Institute of Technology, HarbinChina

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