Event Correlations in Sensor Networks

  • Ping Ni
  • Li Wan
  • Yang Cai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5545)

Abstract

In this paper we present a novel method to mine the correlations of events in sensor networks to extract correlation patterns of sensors’ behaviors by using an unsupervised algorithm based on a hash table. The goal is to discover anomalous events in a large sensor network where its structure is unknown. Our algorithm enables users to select the correlation confidence level and only display the significant event correlations. Our experiment results show that it can discover significant event correlations in both continuous and discrete signals from heterogeneous sensor networks. The applications include smart building design and large network data mining.

Keywords

Correlation Visualization Sensor Network 
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References

  1. 1.
    Mahoney, M.V., Chan, K.: Trajectory Boundary Modeling of Time Series for Anomaly Detection. SIGKDD Explorations Newsletter 7(2), 132–136 (2005)CrossRefGoogle Scholar
  2. 2.
    Fabian, M.: Unsupervised Pattern Mining from Symbolic Temporal Data. SIGKDD Explorations Newsletter 9(1), 41–45 (2007)CrossRefGoogle Scholar
  3. 3.
    Zhang, T., Yue, D., Gu, Y., Yu, G.: Boolean Representation Based Data-Adaptive Correlation Analysis over Time Series Streams. In: CIKM 2007 Information and knowledge management, pp. 203–212. ACM, New York (2007)Google Scholar
  4. 4.
    Ke, Y., Cheng, J.: Correlated Pattern Minging in Quantitative Databases. ACM Transactions on Database Systems 3(33), Article 14 (August 2008)Google Scholar
  5. 5.
    Ke, Y., Cheng, J.: Correlation search in graph databases. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 390–399. ACM, New York (2007)CrossRefGoogle Scholar
  6. 6.
    Cohen, E., Datar, M.: Finding Interesting Associations without Support Pruning. IEEE Transaction on Knowledge and Data Engineering 1(13) (February 2001)Google Scholar
  7. 7.
    Tsai, K.-C., Sung, J.-T.: An Environment Sensor Fusion Application on Smart Building Skins. In: IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, June 2008, pp. 291–295 (2008)Google Scholar
  8. 8.
    Sharples, S., Callaghan, V., Clarke, G.: A Multi-agent Architecture for Intelligent Building Sensing and Control. International Sensor Review Journal, 1–8 ( May 1999)Google Scholar
  9. 9.
    Hagras, H., Callaghan, V., Colley, M., Clarke, G.: A Hierarchical Fuzzy–genetic Multiagent Architecture for Intelligent Buildings Online Learning, Adaptation and Control. Information Sciences 150, 33–57 (2003)CrossRefGoogle Scholar
  10. 10.
    Kay, R.: Discovery of Frequent Distributed Event patterns in Sensor Networks. In: Verdone, R. (ed.) EWSN 2008. LNCS, vol. 4913, pp. 106–124. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Boukerche, A., Samarah, S.: A Novel Algorithm for Mining Association Rules in Wireless Ad Hoc Sensor Networks. IEEE Transactions on Parallel and Distributed Systems 19(7) (July 2008)Google Scholar
  12. 12.
    Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rule. In: Proc. 20th Int’l Conf. Very Large Data Bases (VLDB 1994), pp. 487–499. Morgan Kaufrmann, San Francisco (1994)Google Scholar
  13. 13.
    Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of the 11th international Conference on Data Engineering, pp. 3–14. IEEE Press, Los Alamitos (1995)Google Scholar
  14. 14.
    Cong, S., Han, J.: Parallel mining of closed sequential patterns. In: KDD 2005: Eleventh ACM SIGKDD international conference on knowledge discovery in data mining, Chicago, Illinois, pp. 562–567 (2005)Google Scholar
  15. 15.
  16. 16.
    Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach. In: Data Mining and Knowledge Discovery, 2000 ACM SIGMOD international conference on Management of data, vol. 8(1), pp. 1–12 (2000)Google Scholar
  17. 17.
    Zaki, M., Hsiao, C.: Charm: An Efficient Algorithm for Closed Itemset Mining. In: SDM 2002 (April 2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ping Ni
    • 1
  • Li Wan
    • 1
  • Yang Cai
    • 1
  1. 1.Carnegie Mellon UniversityPittsburghUSA

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