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Journal of Computer Science and Technology

, Volume 26, Issue 4, pp 643–662 | Cite as

DHC: Distributed, Hierarchical Clustering in Sensor Networks

  • Xiu-Li Ma
  • Hai-Feng Hu
  • Shuang-Feng Li
  • Hong-Mei Xiao
  • Qiong Luo
  • Dong-Qing Yang
  • Shi-Wei Tang
Regular Paper

Abstract

In many sensor network applications, it is essential to get the data distribution of the attribute value over the network. Such data distribution can be got through clustering, which partitions the network into contiguous regions, each of which contains sensor nodes of a range of similar readings. This paper proposes a method named Distributed, Hierarchical Clustering (DHC) for online data analysis and mining in senior networks. Different from the acquisition and aggregation of raw sensory data, DHC 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. We also design and evaluate the maintenance mechanisms for DHC to make it be able to work on evolving data. Our simulation results on real world datasets as well as synthetic datasets show the effectiveness and efficiency of our approach.

Keywords

clustering data mining wireless sensor networks 

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References

  1. [1]
    Volgyesi P, Nadas A, Koutsoukos X, Ledeczi A. Air quality monitoring with Sensor Map. In Proc. IPSN 2008, St. Louis, USA, Apr. 22-24, 2008, pp.529–530.Google Scholar
  2. [2]
    Barrenetxea G, Ingelrest F, Schaefer G, Vetterli M. SensorScope: Out-of-the-box environmental monitoring. In Proc. IPSN 2008, St. Louis, USA, Apr. 22-24, 2008, pp.332–343.Google Scholar
  3. [3]
    Michel S, Salehi A, Luo L, Dawes N, Aberer K, Barrenetxea G, Bavay M, Kansal A, Kumar A, Nath S, Parlange M, Tansley S, Ingen C V, Zhao F, Zhou Y. Environmental monitoring 2.0. In Proc. ICDE 2009, Shanghai, China, Mar. 29-Apr. 2, 2009, pp.1507–1510.Google Scholar
  4. [4]
    Krause A, Leskovec J, Guestrin C, Van Briesen J, Faloutsos C. Efficient sensor placement optimization for securing large water distribution networks. Journal of Water Resources Planning and Management, 2008, 134(6): 516–526.CrossRefGoogle Scholar
  5. [5]
    Xue W, Luo Q, Chen L, Liu Y. Contour map matching for event detection in sensor networks. In Proc. SIGMOD 2006, Chicago, USA, Jun. 27-29, pp.145–156.Google Scholar
  6. [6]
    Meka A, Singh A K. Distributed spatial clustering in sensor networks. In Proc. EDBT 2006, Munich, Germany, Mar. 26-31, 2006, pp.980–1000.Google Scholar
  7. [7]
    Guestrin C, Bodik P, Thibaux R, Paskin M, Madden S. Distributed regression: An efficient framework for modeling sensor network data. In Proc. IPSN 2004, Berkeley, USA, Apr. 26-27, 2004, pp.1–10.Google Scholar
  8. [8]
    Yin J, Gaber M M. Clustering distributed time series in sensor networks. In Proc. ICDM 2008, Pisa, Italy, Dec. 15-19, 2008, pp.678–687.Google Scholar
  9. [9]
    Ma X, Li S, Luo Q, Yang D, Tang S. Distributed, hierarchical clustering and summarization in sensor networks. In Proc. APWeb 2007/WAIM 2007, Huangshan, China, Jun. 16-18, 2007, pp.168–175.Google Scholar
  10. [10]
    Han J, Kamber M. Data Mining: Concepts and Techniques, Second Edition. Morgan Kaufmann Publishers, 2006.Google Scholar
  11. [11]
    Zhang T, Ramakrishnan R, Livny M. BIRCH: An efficient data clustering method for very large databases. In Proc. SIGMOD 1996, Montreal, Canada, Jun. 4-6, 1996, pp.103–114.Google Scholar
  12. [12]
    Johnson D B, aMaltz D A. Dynamic Source Routing in AdHoc Wireless Networks. Mobile Computing, Kluwer Academic Publishers, 1996, pp.153–181.Google Scholar
  13. [13]
    Madden S, Franklin M J, Hellerstein J M, Hong W. Tag: A tiny aggregation service for ad hoc sensor networks. In Proc. OSDI 2002, Boston, USA, Dec. 9-11, 2002.Google Scholar
  14. [14]
    Olston C, Jiang J, Widom J. Adaptive filters for continuous queries over distributed data streams. In Proc. SIGMOD 2003, San Diego, USA, Jun. 9-12, pp.563–574.Google Scholar
  15. [15]
    Deligiannakis A, Kotidis Y, Roussopoulos N. Hierarchical innetwork data aggregation with quality guarantees. In Proc. EDBT 2004, Crete, Greece, Mar. 14-18, 2004, pp.658–675.Google Scholar
  16. [16]
    CRU data. http://www.cru.uea.ac.uk/cru/data, Jul. 2009.
  17. [17]
    Intel Lab data. http://berkeley.intel-research.net/labdata/, Sept. 2008.
  18. [18]
    Jindal A, Psounis K. Modeling spatially-correlated sensor network data. In Proc. SECON 2004, Santa Clara, USA, Oct. 4-7, 2004, pp.162–171.Google Scholar
  19. [19]
    Madden S, Franklin M J, Hellerstein J M, Hong W. The design of an acquisitional query processor for sensor networks. In Proc. SIGMOD 2003, San Diego, USA, Jun. 9-12, pp.491–502.Google Scholar
  20. [20]
    Breunig M M, Kriegel H, Kroger P, Sander J. Data bubbles: Quality preserving performance boosting for hierarchical clustering. In Proc. SIGMOD 2001, Santa Barbara, USA, May 21-24, 2001, pp.79–90.Google Scholar
  21. [21]
    Bandyopadhyay S, Coyle E J. An energy efficient hierarchical clustering algorithm for wireless sensor networks. In Proc. INFOCOM 2003, San Francisco, USA, Mar. 30-Apr. 3, 2003, pp.1713–1723.Google Scholar
  22. [22]
    Zhang Q, Liu J, Wang W. Approximate clustering on distributed data streams. In Proc. ICDE 2008, Cancun, Mexico, Apr. 7-12, 2008, pp.1131–1139.Google Scholar
  23. [23]
    Hua M, Lau MK, Pei J, Wu K. Continuous K-means monitoring with low reporting cost in sensor networks. IEEE Transaction on Knowledge and Data Engineering, 2009, 21(12): 1679–1691.CrossRefGoogle Scholar
  24. [24]
    Liu C, Wu K, Pei J. An energy-efficient data collection framework for wireless sensor networks by exploiting spatiotemporal correlation. IEEE Transaction on Parallel and Distributed Systems, July 2007, 18(7): 1010–1023.CrossRefGoogle Scholar
  25. [25]
    Kotidis Y. Snapshot queries: Towards data-centric sensor networks. In Proc. ICDE 2005, Tokyo, Japan, Apr. 5-8, 2005, pp.131–142.Google Scholar

Copyright information

© Springer Science+Business Media, LLC & Science Press, China 2011

Authors and Affiliations

  • Xiu-Li Ma
    • 1
    • 2
  • Hai-Feng Hu
    • 1
    • 2
  • Shuang-Feng Li
    • 5
  • Hong-Mei Xiao
    • 1
    • 2
  • Qiong Luo
    • 4
  • Dong-Qing Yang
    • 1
    • 3
  • Shi-Wei Tang
    • 1
    • 2
  1. 1.School of Electronics Engineering and Computer SciencePeking UniversityBeijingChina
  2. 2.Key Laboratory of Machine Perception (Peking University), Ministry of EducationBeijingChina
  3. 3.Key Laboratory of High Confidence Software Technologies (Peking University), Ministry of EducationBeijingChina
  4. 4.Department of Computer Science and EngineeringHong Kong University of Science and Technology Clear Water BayKowloonHong Kong, China
  5. 5.Google ChinaBeijingChina

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