Beyond Average: Toward Sophisticated Sensing with Queries

  • Joseph M. Hellerstein
  • Wei Hong
  • Samuel Madden
  • Kyle Stanek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2634)

Abstract

High-level query languages are an attractive interface for sensor networks, potentially relieving application programmers from the burdens of distributed, embedded programming. In research to date, however, the proposed applications of such interfaces have been limited to simple data collection and aggregation schemes. In this paper, we present initial results that extend the TinyDB sensornet query engine to support more sophisticated data analyses, focusing on three applications: topographic mapping, wavelet-based compression, and vehicle tracking. We use these examples to motivate the feasibility of implementing sophisticated sensing applications in a query-based system, and present some initial results and research questions raised by this agenda.

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References

  1. [1]
    R. Calderbank, I. Daubechies, W. Sweldens, and B.-L. Yeo. Wavelet transforms that map integers to integers. 5(3):332–369, 1998.MATHMathSciNetGoogle Scholar
  2. [2]
    Y. H. H. Dan Li, Kerry Wong and A. Sayeed. Detection, classification and tracking of targets in distributed sensor networks. IEEE Signal Processing Magazine, 19(2), Mar 2002.Google Scholar
  3. [3]
    J. Elson, L. Girod, and D. Estrin. Fine-grained network time synchronization using reference broadcasts. In OSDI, 2002.Google Scholar
  4. [4]
    D. Estrin. Embedded networked sensing for enviromental monitoring. Keynote, circuits and systems workshop. Slides available at http://lecs.cs.ucla.edu/estrin/talks/CAS-JPL-Sept02.ppt.
  5. [5]
    D. Ganesan, D. Estrin, and J. Heidemann. Dimensions:Why dowe need a new data handling architecture for sensor networks? In Proceedings of the First Workshop on Hot Topics In Networks (HotNets-I), Princeton, New Jersey, Oct. 2002.Google Scholar
  6. [6]
    M. Garofalakis and P. B. Gibbons. Wavelet synopses with error guarantees. In Proc. ACM SIGMOD 2002, pages 476–487, Madison, WI, June 2002.Google Scholar
  7. [7]
    J. M. Hellerstein, P. J. Haas, and H. Wang. Online aggregation. In Proceedings of the ACM SIGMOD, pages 171–182, Tucson, AZ, May 1997.Google Scholar
  8. [8]
    J. Hill, R. Szewczyk, A. Woo, S. Hollar, and D. C. K. Pister. System architecture directions for networked sensors. In ASPLOS, November 2000.Google Scholar
  9. [9]
    C. Intanagonwiwat, D. Estrin, R. Govindan, and J. Heidemann. Impact of network density on data aggregation in wireless sensor networks. ICDCS-22, November 2001.Google Scholar
  10. [10]
    C. Intanagonwiwat, R. Govindan, and D. Estrin. Directed diffusion: A scalable and robust communication paradigm for sensor networks. In MobiCOM, Boston, MA, August 2000.Google Scholar
  11. [11]
    J. Rabaey, et al. Picoradios for wireless sensor networks: The next challenge in ultra-lowpower design. In Proceedings of the International Solid-State Circuits Conference, San Francisco, CA, Feb. 2002.Google Scholar
  12. [12]
    D. Keim and M. Heczko. Wavelets and their applications in databases. In ICDE, Heidelberg, Germany, Apr. 2001.Google Scholar
  13. [13]
    M. V. Leonov and A. G. Nitikin. An efficient algorithm for a closed set of boolean operations on polygonal regions in the plane. Technical report, A.P. Ershov Institute of Informatics Systems, 1997. Preprint 46 (In Russian.) English Translation available at http://home.attbi.com/msleonov/pbpaper.html.
  14. [14]
    S. Madden, M. J. Franklin, J. M. Hellerstein, and W. Hong. TAG: A Tiny AGgregation Service for Ad-Hoc Sensor Networks. In OSDI, 2002.Google Scholar
  15. [15]
    S. Madden, W. Hong, J. M. Hellerstein, and M. Franklin. TinyDB web page. http://telegraph.cs.berkeley.edu/tinydb.
  16. [16]
    S. Madden, R. Szewczyk, M. Franklin, and D. Culler. Supporting aggregate queries over ad-hoc wireless sensor networks. In WMCSA, 2002.Google Scholar
  17. [17]
    J. Malik, S. Belognie, T. Leung, and J. Shi. Contour and texture analysis for image segmentation. International Journal of Computer Vision, 43(1):7–27, 2001.MATHCrossRefGoogle Scholar
  18. [18]
    Y. Matias, J. S. Vitter, and M. Wang. Wavelet-based histograms for selectivity estimation. In SIGMOD, pages 448–459, Seattle, Washington, June 1998.Google Scholar
  19. [19]
    P. Ramanathan, K. Saluja, K.-C. Wang, and T. Clouqueur. UW-API: A Network Routing Application Programmer’s Interface. Draft version 1.0, January 2001.Google Scholar
  20. [20]
    W. Sweldens. The lifting scheme: A construction of second generation wavelets. SIAM J. Math. Anal., 29(2):511–546, 1997.CrossRefMathSciNetGoogle Scholar
  21. [21]
    W. Sweldens and P. Schröder. Building your own wavelets at home. In Wavelets in Computer Graphics, pages 15–87. ACM SIGGRAPH Course notes, 1996. http://cm.bell-labs.com/who/wim/papers/athome.pdf.
  22. [22]
    Y. Yao and J. Gehrke. The cougar approach to in-network query processing in sensor networks. In SIGMOD Record, September 2002.Google Scholar
  23. [23]
    J. Zhao, R. Govindan, and D. Estrin. Computing aggregates for monitoring wireless sensor networks. Technical Report 02-773, USC, September 2003.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Joseph M. Hellerstein
    • 1
    • 2
  • Wei Hong
    • 2
  • Samuel Madden
    • 1
  • Kyle Stanek
    • 1
  1. 1.UC BerkeleyBerkeley
  2. 2.Intel ResearchBerkeley

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