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Learning from Data Streams pp 87–105Cite as

Aggregation and Summarization in Sensor Networks

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Abstract

Sensor networks generate enormous quantities of data which need to be processed in a distributed fashion to extract interesting information. We outline how ideas and algorithms from data stream query processing are revolutionizing data processing in sensor networks. We also discuss how sensor networks pose some particular problems of their own and how these are being overcome.

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References

  1. N. Alon, Y. Matias, M. Szegedy, The space complexity of approximating the frequency moments. In: STOC ’96: Proceedings of the Twenty-Eighth Annual ACM Symposium on Theory of Computing, pp. 20–29, New York, NY, USA. ACM Press, 1996.

    Google Scholar 

  2. B. Babcock, C. Olston, Distributed top-k monitoring. In: SIGMOD ’03: Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, pp. 28–39, New York, NY, USA. ACM Press, 2003.

    Google Scholar 

  3. B. Babcock, S. Babu, M. Datar, R. Motwani, J. Widom, Models and issues in data stream systems. In: PODS ’02: Proceedings of the Twenty-First ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 1–16, New York, NY, USA. ACM Press, 2002.

    Google Scholar 

  4. J. Considine, F. Li, G. Kollios, J. Byers, Approximate aggregation techniques for sensor databases. In: Proc. of the 20th Intl. Conf. on Data Engineering (ICDE), 2004.

    Google Scholar 

  5. G. Cormode, S. Muthukrishnan, An improved data stream summary: the count-min sketch and its applications. In: Proc. of LATIN 2004, 2004.

    Google Scholar 

  6. G. Cormode, M. Garofalakis, S. Muthukrishnan, R. Rastogi, Holistic aggregates in a networked world: distributed tracking of approximate quantiles. In: Proc. of SIGMOD’05, 2005.

    Google Scholar 

  7. A. Deshpande, C. Guestrin, W. Hong, S. Madden, Exploiting correlated attributes in acquisitional query processing. In: Proc. of International Conference on Data Engineering (ICDE 2005), 2005.

    Google Scholar 

  8. A. Deshpande, C. Guestrin, S. Madden, J. Hellerstein, W. Hong, Model-driven data acquisition in sensor networks. In: Proc. of the 30th International Conference on Very Large Data Bases (VLDB 2004), 2004.

    Google Scholar 

  9. M. Durand, P. Flajolet, Loglog counting of large cardinalities. In: European Symposium on Algorithms (ESA03), 2003.

    Google Scholar 

  10. P. Flajolet, Counting by coin tossings. In: Lecture Notes in Computer Science, vol. 3321, pp. 1–12, 2004.

    Google Scholar 

  11. P. Flajolet, G.N. Martin, Probabilistic counting algorithms for data base applications. Journal of Computer and System Sciences, pp. 182–209, 1985.

    Google Scholar 

  12. S. Gandhi, J. Hershberger, S. Suri, Approximate isocontours and spatial summaries in sensor networks. In: International Conference on Information Processing in Sensor Networks (IPSN’07), 2007.

    Google Scholar 

  13. J.M. Greenwald, S. Khanna, Space-efficient online computation of quantile summaries. In: Proc. the 20th ACM SIGMOD Intl. Conf. on Management of Data (SIGMOD), 2001.

    Google Scholar 

  14. J.M. Greenwald, S. Khanna, Power-conserving computation of order-statistics over sensor networks. In: Proc. of 23rd ACM Symposium on Principles of Database Systems (PODS), 2004.

    Google Scholar 

  15. J.M. Hellerstein, W. Hong, S. Madden, K. Stanek, Beyond average: toward sophisticated sensing with queries. In: F. Zhao, L. Guibas (Eds.), Information Processing in Sensor Networks. Springer, 2003.

    Google Scholar 

  16. J. Hershberger, N. Shrivastava, S. Suri, C.D. Toth, Adaptive spatial partitioning for multidimensional data streams. In: Proc. of the 15th Annual International Symposium on Algorithms and Computation (ISAAC), 2004.

    Google Scholar 

  17. P. Indyk, D. Woodruff, Tight lower bounds for the distinct elements problem. In: Proc. of the 44th IEEE Symposium on Foundations of Computer Science (FOCS), 2004.

    Google Scholar 

  18. James reserve microclimate and video remote sensing, http://www.cens.ucla.edu.

  19. S. Madden, M.J. Franklin, J. Hellerstein, W. Hong, Tag: a tiny aggregation service for ad-hoc sensor networks. In: Proc. of OSDI ’02, 2002.

    Google Scholar 

  20. S. Madden, S. Szewczyk, M.J. Franklin, D. Culler, Supporting aggregate queries over ad-hoc sensor networks. In: Workshop on Mobile Computing and Systems Application, 2002.

    Google Scholar 

  21. A. Manjhi, S. Nath, P.B. Gibbons, Tributaries and deltas: efficient and robust aggregation in sensor network streams. In: Proc. of SIGMOD’05, 2005.

    Google Scholar 

  22. G. Manku, R. Motwani, Approximate frequency counts over data streams. In: Proc. 28th Conf. on Very Large Data Bases (VLDB), 2002.

    Google Scholar 

  23. M. Muralikrishna, D.J. DeWitt, Equi-depth histograms for estimating selectivity factors for multi-dimensional queries. In: SIGMOD Conference, pp. 28–36, 1988.

    Google Scholar 

  24. S. Muthukrishnan, Data streams: algorithms and applications, 2003.

    Google Scholar 

  25. S. Nath, P.B. Gibbons, S. Seshan, Z.R. Anderson, Synopsis diffusion for robust aggregation in sensor networks. In: Proc. of SenSys’04, 2004.

    Google Scholar 

  26. N. Shrivastava, C. Buragohain, D. Agrawal, S. Suri, Medians and beyond: new aggregation techniques for sensor networks. In: Proc. of SenSys’04, 2004.

    Google Scholar 

  27. N. Shrivastava, C. Buragohain, D. Agrawal, S. Suri, Continuous quantile queries in sensor networks, 2007.

    Google Scholar 

  28. A. Silberstein, R. Braynard, J. Yang, Constraint chaining: on energy-efficient continuous monitoring in sensor networks. In: SIGMOD ’06: Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data, pp. 157–168, New York, NY, USA. ACM Press, 2006.

    Google Scholar 

  29. A. Silberstein, R. Braynard, C. Ellis, K. Munagala, J. Yang, A sampling-based approach to optimizing top-k queries in sensor networks. In: ICDE ’06: Proceedings of the 22nd International Conference on Data Engineering (ICDE’06), p. 68, Washington, DC, USA. IEEE Computer Society, 2006.

    Google Scholar 

  30. I. Solis, K. Obraczka, Efficient continuous mapping in sensor networks using isolines. In: MOBIQUITOUS ’05: Proceedings of the Second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, pp. 325–332, Washington, DC, USA. IEEE Computer Society, 2005.

    Google Scholar 

  31. R. Szewczyk, A. Mainwaring, J. Polastre, D. Culler, An analysis of a large scale habitat monitoring application. In: Proc. of SenSys ’04, 2004.

    Google Scholar 

  32. N. Xu, S. Rangwala, K. Chintalapudi, D. Ganesan, A. Broad, R. Govindan, D. Estrin, A wireless sensor network for structural monitoring. In: Proc. of SenSys ’04, 2004.

    Google Scholar 

  33. Y. Yao, J. Gehrke, The cougar approach to in-network query processing. ACM SIGMOD Record, p. 9, 2002.

    Google Scholar 

  34. J. Zhao, R. Govindan, D. Estrin, Computing aggregates for monitoring wireless sensor networks. In: The First IEEE Intl. Workshop on Sensor Network Protocols and Applications (SNPA), 2003.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Bell Labs Research, Alcatel-Lucent India, Bangalore, India

    Nisheeth Shrivastava

  2. Amazon.com, Seattle, WA, 98104, USA

    Chiranjeeb Buragohain

Authors
  1. Nisheeth Shrivastava
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  2. Chiranjeeb Buragohain
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Corresponding author

Correspondence to Nisheeth Shrivastava .

Editor information

Editors and Affiliations

  1. Laboratory of Artificial Intelligence and Decision Support, INESC-Porto LA and Faculty of Economics, University of Porto, Rua de Ceuta, 118, 6º, 4050-190, Porto, Portugal

    João Gama

  2. Tasmanian ICT Centre, GPO Box 1538, Hobart, TAS 7001, Australia

    Mohamed Medhat Gaber

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© 2007 Springer-Verlag Berlin Heidelberg

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Shrivastava, N., Buragohain, C. (2007). Aggregation and Summarization in Sensor Networks. In: Gama, J., Gaber, M.M. (eds) Learning from Data Streams. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-73679-4_7

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  • DOI: https://doi.org/10.1007/3-540-73679-4_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73678-3

  • Online ISBN: 978-3-540-73679-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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