Skip to main content
SpringerLink
Log in

Model-based approximate querying in sensor networks

  • Regular Paper
  • Published:
The VLDB Journal Aims and scope Submit manuscript

Abstract

Declarative queries are proving to be an attractive paradigm for interacting with networks of wireless sensors. The metaphor that “the sensornet is a database” is problematic, however, because sensors do not exhaustively represent the data in the real world. In order to map the raw sensor readings onto physical reality, a model of that reality is required to complement the readings. In this article, we enrich interactive sensor querying with statistical modeling techniques. We demonstrate that such models can help provide answers that are both more meaningful, and, by introducing approximations with probabilistic confidences, significantly more efficient to compute in both time and energy. Utilizing the combination of a model and live data acquisition raises the challenging optimization problem of selecting the best sensor readings to acquire, balancing the increase in the confidence of our answer against the communication and data acquisition costs in the network. We describe an exponential time algorithm for finding the optimal solution to this optimization problem, and a polynomial-time heuristic for identifying solutions that perform well in practice. We evaluate our approach on several real-world sensor-network datasets, taking into account the real measured data and communication quality, demonstrating that our model-based approach provides a high-fidelity representation of the real phenomena and leads to significant performance gains versus traditional data acquisition techniques.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. SenSys: Call for Papers. http://www.cis.ohio-state.edu/sensys04/ (2004)

  2. IPSN: Call for Papers. http://ipsn04.cs.uiuc.edu/call_for_papers.html (2004)

  3. Madden, S., Franklin, M.J., Hellerstein, J.M., Hong, W.: The design of an acquisitional query processor for sensor networks. In: ACM SIGMOD (2003)

  4. Yao, Y., Gehrke, J.: Query processing in sensor networks. In: Conference on Innovative Data Systems Research (CIDR) (2003)

  5. Kollios, G., Considine, J., Li, F., Byers, J.: Approximate aggregation techniques for sensor databases. In: ICDE (2004)

  6. Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)

    MATH  Google Scholar 

  7. Crossbow, Inc.: Wireless sensor networks. http://www.xbow.com/Products/Wireless_Sensor_Networks.htm

  8. Madden, S., Hong, W., Hellerstein, J.M., Franklin, M.: TinyDB web page. http://telegraph.cs.berkeley.edu/tinydb

  9. Madden, S.: The design and evaluation of a query processing architecture for sensor networks. Master’s thesis, UC Berkeley (2003)

  10. TAOS, Inc.: Tsl2550 ambient light sensor. Technical report. http://www.taosinc.com/pdf/tsl2550-E39.pdf (2002)

  11. Intersema: Ms5534a barometer module. Technical report. http://www.intersema.com/pro/module/file/da5534.pdf (2002)

  12. Sensirion: Sht11/15 relative humidity sensor. Technical report. http://www.sensirion.com/en/pdf/Datasheet_SHT1x_SHT7x_0206.pdf (2002)

  13. Intanagonwiwat, C., Govindan, R., Estrin, D.: Directed diffusion: A scalable and robust communication paradigm for sensor networks. In: MobiCOM, Boston, MA (2000)

  14. Pottie, G., Kaiser, W.: Wireless integrated network sensors. Commun. ACM 43(5), 51–58 (2000)

    Article  Google Scholar 

  15. Polastre, J.: Design and implementation of wireless sensor networks for habitat monitoring. Master’s thesis, UC Berkeley (2003)

  16. Lin, S., Kernighan, B.: An effective heuristic algorithm for the tsp. Oper. Res. 21, 498–516 (1971)

    Article  MathSciNet  Google Scholar 

  17. Bahar, R., Frohm, E., Gaona, C., Hachtel, G., Macii, E., Pardo, A., Somenzi, F.: Algebraic decision diagrams and their applications. In: IEEE Internation Conference on Computer-Aided Design, pp. 188–191 (1993)

  18. Deshpande, A., Guestrin, C., Madden, S., Hong, W.: Exploiting correlated attributes in acquisitional query processing. In: ICDE (2005)

  19. Olston, C., Widom, J.: Best effort cache sychronization with source cooperation. In: SIGMOD (2002)

  20. Sharaf, A., Beaver, J., Labrinidis, A., Chrysanthis, K.: Balancing energy efficiency and quality of aggregate data in sensor networks. VLDB J. 13(4), 384–403 (2004)

    Article  Google Scholar 

  21. Deshpande, A., Garofalakis, M., Rastogi, R.: Independence is good: Dependency-based histogram synopses for high-dimensional data. In: SIGMOD (2001)

  22. Friedman, N.: Learning belief networks in the presence of missing values and hidden variables. In: Proceedings of the 14th International Conference on Machine Learning, pp. 125–133 (1997)

  23. Ganguly, S.: Design and analysis of parametric query optimization algorithms. In: VLDB’98, Proceedings of 24rd International Conference on Very Large Data Bases (1998)

  24. Getoor, L., Taskar, B., Koller, D.: Selectivity estimation using probabilistic models. In: SIGMOD (2001)

  25. Gibbons, P.B.: Distinct sampling for highly-accurate answers to distinct values queries and event reports. In: Proceedings of Conference on Very Large Data Bases (2001)

  26. Desphande, A., Guestrin, C., Madden, S., Hellerstein, J.M., Hong, W.: Model-driven data acquisition in sensor networks. In: Proceedings of Conference on Very Large Data Bases (2004)

  27. Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, San Mateo, CA (1988)

    Google Scholar 

  28. Cowell, R., Dawid, P., Lauritzen, S., Spiegelhalter, D.: Probabilistic Networks and Expert Systems. Springer, New York (1999)

    MATH  Google Scholar 

  29. Paskin, M.A., Guestrin, C.E.: Robust probabilistic inference in distributed systems. In: UAI, the 20th International Conference on Uncertainty in Artificial Intelligence (2004)

  30. Heckerman, D.: A tutorial on learning with bayesian networks, Microsoft, MSR-TR-95-06, March (1995)

  31. Lerner, U., Moses, B., Scott, M., McIlraith, S., Koller, D.: Monitoring a complex physical system using a hybrid dynamic bayes net. In: Proceedings of UAI (2002)

  32. Boyen, X., Koller, D.: Tractable inference for complex stochastic processes. In: Proceedings of UAI (1998)

  33. Dean, T., Kanazawa, K.: A model for reasoning about persistence and causation. Comput. Intell. 5(3), 142–150 (1989)

    Google Scholar 

  34. Pearl, J.: Causality : Models, Reasoning, and Inference. Cambridge University Press, Cambridge (2000)

    MATH  Google Scholar 

  35. Bernardo, J., Smith, A.: Bayesian Theory. Wiley, New York (1994)

    MATH  Google Scholar 

  36. Bellman, R.E.: Dynamic Programming. Princeton, Princeton, NJ (1957)

    Google Scholar 

  37. Puterman, M.L.: Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York (1994)

  38. Boutilier, C., Dearden, R., Goldszmidt, M.: Exploiting structure in policy construction. In: Proceedings of IJCAI, pp. 1104–1111 (1995)

  39. Guestrin, C.E., Koller, D., Parr, R.: Multiagent planning with factored MDPs. In: 14th Neural Information Processing Systems (NIPS-14), Vancouver, Canada, pp. 1523–1530 (2001)

  40. Hellerstein, J., Hong, W., Madden, S., Stanek, K.: Beyond average: Towards sophisticated sensing with queries. In: Proceedings of the First Workshop on Information Processing in Sensor Networks (IPSN) (2003)

  41. Guestrin, C., Bodik, P., Thibaux, R., Paskin, M., Madden, S.: Distributed regression: An efficient framework for modeling sensor network data. In: Proceedings of Information Processing in Sensor Networks (IPSN) (2004)

  42. Gibbons, P.B., Matias, Y.: New sampling-based summary statistics for improving approximate query answers. In: SIGMOD (1998)

  43. Acharya, S., Gibbons, P., Poosala, V., Ramaswamy, S.: Join synopses for approximate query answering, SIGMOD’99. In: Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data, 275–286. ACM Press, New York, NY, USA, Philadelphia, Pennsylvania, United States (1999) ISBN: 1-58113-084-8, DOI: (http://doi.acm.org/10.1145/304182.304207)

  44. Nath, S., Gibbons, P., Seshan, S., Anderson, Z.: Synopsis diffusion for robust aggregation in sensor networks. In: Proceedings of SenSys (2004)

  45. Olston, C., Loo, B.T., Widom, J.: Adaptive precision setting for cached approximate values. In: ACM SIGMOD (2001)

  46. Cheng, R., Kalashnikov, D.V., Prabhakar, S.: Evaluating probabilistic queries over imprecise data. In: SIGMOD (2003)

  47. Chu, M., Haussecker, H., Zhao, F.: Scalable information-driven sensor querying and routing for ad hoc heterogeneous networks. Int. J. High Perform. Comput. Appl. 16(2), 293–313 (2002)

    Article  Google Scholar 

  48. Hellerstein, J.M., Haas, P.J., Wang, H.: Online aggregation. In: SIGMOD, Tucson, AZ, pp. 171–182 (1997)

  49. Hellerstein, J.M., Avnur, R., Chou, A., Hidber, C., Olston, C., Raman, V., Roth, T., Haas, P.J.: Interactive data analysis with CONTROL. IEEE Comput. 32(8) (1999)

  50. Ioannidis, Y.E., Ng, R.T., Shim, K., Sellis, T.K.: Parametric query optimization. In: Proceedings of the 18th International Conference on Very Large Data Bases (1992)

  51. Graefe, G., Ward, K.: Dynamic query evaluation plans. In: SIGMOD (1989)

  52. Cole, R., Graefe, G.: Optimization of dynamic query evaluation plans. In: SIGMOD (1994)

  53. Hellerstein, J.M., Franklin, M.J., Chandrasekaran, S., Deshpande, A., Hildrum, K., Madden, S., Raman, V., Shah, M.: Adaptive query processing: Technology in evolution. IEEE Data Eng. Bull. 23(2), 7–18 (2000)

    Google Scholar 

  54. Babu, S., Motwani, R., Munagala, K., Nishizawa, I., Widom, J.: Adaptive ordering of pipelined stream filters. In: SIGMOD (2004)

  55. Shivakumar, N., Garcia-Molina, H., Chekuri, C.: Filtering with approximate predicates. In: Proceedings of Conference on Very Large Data Bases (1998)

  56. Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice Hall, Englewood Cliffs, NJ (1994)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Maryland, College Park, MD, 20742, USA

    Amol Deshpande

  2. Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA, 15213, USA

    Carlos Guestrin

  3. Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA, 02139-4307, USA

    Samuel R. Madden

  4. Intel Research, Berkeley, CA, USA

    Joseph M. Hellerstein & Wei Hong

  5. University of California, Berkeley, CA, 94702, USA

    Joseph M. Hellerstein & Wei Hong

Authors
  1. Amol Deshpande
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Carlos Guestrin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Samuel R. Madden
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Joseph M. Hellerstein
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wei Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amol Deshpande.

Additional information

This article includes and extends results that were previously published in VLDB 2004 [Desphande, A., Guestrin, C., Madden, S., Hellerstein, J.M., Hong, W.: Model-driven data acquisition in sensor networks. In {VLDB} (2004)], and combines these techniques with the conditional planning approach published in ICDE 2005 [Deshpande, A., Guestrin, C., Madden, S., Hong, W.: Exploiting correlated attributes in acquisitional query processing. In {ICDE} (2005)].

Rights and permissions

Reprints and permissions

About this article

Cite this article

Deshpande, A., Guestrin, C., Madden, S.R. et al. Model-based approximate querying in sensor networks. The VLDB Journal 14, 417–443 (2005). https://doi.org/10.1007/s00778-005-0159-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00778-005-0159-3

Keywords

Search

Navigation