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DHC: Distributed, Hierarchical Clustering in Sensor Networks

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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.

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References

  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.

  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.

  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.

  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.

    Article  Google Scholar 

  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.

  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.

  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.

  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.

  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.

  10. Han J, Kamber M. Data Mining: Concepts and Techniques, Second Edition. Morgan Kaufmann Publishers, 2006.

  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.

  12. Johnson D B, aMaltz D A. Dynamic Source Routing in AdHoc Wireless Networks. Mobile Computing, Kluwer Academic Publishers, 1996, pp.153–181.

  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.

  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.

  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.

  16. CRU data. http://www.cru.uea.ac.uk/cru/data, Jul. 2009.

  17. Intel Lab data. http://berkeley.intel-research.net/labdata/, Sept. 2008.

  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.

  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.

  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.

  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.

  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.

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  25. Kotidis Y. Snapshot queries: Towards data-centric sensor networks. In Proc. ICDE 2005, Tokyo, Japan, Apr. 5-8, 2005, pp.131–142.

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Authors and Affiliations

  1. School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, China

    Xiu-Li Ma, Hai-Feng Hu, Hong-Mei Xiao, Dong-Qing Yang (Member, CCF) & Shi-Wei Tang (Senior Member, CCF)

  2. Key Laboratory of Machine Perception (Peking University), Ministry of Education, Beijing, 100871, China

    Xiu-Li Ma, Hai-Feng Hu, Hong-Mei Xiao & Shi-Wei Tang (Senior Member, CCF)

  3. Key Laboratory of High Confidence Software Technologies (Peking University), Ministry of Education, Beijing, 100871, China

    Dong-Qing Yang (Member, CCF)

  4. Department of Computer Science and Engineering, Hong Kong University of Science and Technology Clear Water Bay, Kowloon, Hong Kong, China

    Qiong Luo

  5. Google China, Beijing, 100871, China

    Shuang-Feng Li

Authors
  1. Xiu-Li Ma
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  2. Hai-Feng Hu
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  3. Shuang-Feng Li
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  4. Hong-Mei Xiao
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  5. Qiong Luo
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  6. Dong-Qing Yang
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  7. Shi-Wei Tang
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Correspondence to Xiu-Li Ma.

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Ma, XL., Hu, HF., Li, SF. et al. DHC: Distributed, Hierarchical Clustering in Sensor Networks. J. Comput. Sci. Technol. 26, 643–662 (2011). https://doi.org/10.1007/s11390-011-1165-0

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  • DOI: https://doi.org/10.1007/s11390-011-1165-0

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