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Distributed, Hierarchical Clustering and Summarization in Sensor Networks

  • Xiuli Ma
  • Shuangfeng Li
  • Qiong Luo
  • Dongqing Yang
  • Shiwei Tang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4505)

Abstract

We propose DHCS, a method of distributed, hierarchical clustering and summarization for online data analysis and mining in sensor networks. Different from the acquisition and aggregation of raw sensory data, our method clusters sensor nodes based on their current data values as well as their geographical proximity, and computes a summary for each cluster. Furthermore, these clusters, together with their summaries, are produced in a distributed, bottom-up manner. The resulting hierarchy of clusters and their summaries facilitates interactive data exploration at multiple resolutions. It can also be used to improve the efficiency of data-centric routing and query processing in sensor networks. Our simulation results on real world data sets as well as synthetic data sets show the effectiveness and efficiency of our approach.

Keywords

Sensor networks clustering summarization 

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References

  1. 1.
    Bandyopadhyay, S., Coyle, E.J.: An Energy Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks. In: INFOCOM (2003)Google Scholar
  2. 2.
    Breunig, M.M., Kriegel, H., Kroger, P., Sander, J.: Data Bubbles: Quality Preserving Performance Boosting for Hierarchical Clustering. In: SIGMOD (2001)Google Scholar
  3. 3.
    Guestrin, C., Bodik, P., Thibaux, R., Paskin, M., Madden, S.: Distributed Regression: An Efficient Framework for Modeling Sensor Network Data. In: IPSN (2004)Google Scholar
  4. 4.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. China Machine Press, Beijing (2001)Google Scholar
  5. 5.
    Jindal, A., Psounis, K.: Modeling Spatially-Correlated Sensor Network data. In: SECON (2004)Google Scholar
  6. 6.
    Johnson, D.B., Maltz, D.A.: Dynamic Source Routing in Ad-hoc Wireless Networks. In: Mobile Computing, pp. 153–181. Kluwer Academic Publishers, Dordrecht (1996)CrossRefGoogle Scholar
  7. 7.
    Kotidis, Y.: Snapshot Queries: Towards Data-Centric Sensor Networks. In: ICDE (2005)Google Scholar
  8. 8.
    Madden, S., Franklin, M.J., Hellerstein, J.M., Hong, W.: Tag: A Tiny Aggregation Service for ad hoc Sensor Networks. In: OSDI (2002)Google Scholar
  9. 9.
    Younis, O., Fahmy, S.: Distributed Clustering in Ad-hoc Sensor Networks: A Hybrid, Energy-efficient Approach. In: INFOCOM (2004)Google Scholar
  10. 10.
    Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: An efficient data clustering method for very large databases. In: SIGMOD (1996)Google Scholar
  11. 11.
    Zhou, T., Ramakrishnan, R., Livny, M.: Data Bubbles for Non-Vector Data: Speeding-up Hierarchical Clustering in Arbitrary Metric Spaces. In: VLDB (2003)Google Scholar
  12. 12.

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Xiuli Ma
    • 1
  • Shuangfeng Li
    • 1
  • Qiong Luo
    • 2
  • Dongqing Yang
    • 1
  • Shiwei Tang
    • 1
  1. 1.School of Electronics Engineering and Computer Science, State Key Laboratory on, Machine Perception, Peking University, Beijing,100871China
  2. 2.Department of Computer Science, The Hong Kong University of Science and Technology, Clear Water Bay, KowloonHong Kong

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