Predictive Modeling-Based Data Collection in Wireless Sensor Networks

  • Lidan Wang
  • Amol Deshpande
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4913)


We address the problem of designing practical, energy-efficient protocols for data collection in wireless sensor networks using predictive modeling. Prior work has suggested several approaches to capture and exploit the rich spatio-temporal correlations prevalent in WSNs during data collection. Although shown to be effective in reducing the data collection cost, those approaches use simplistic corelation models and further, ignore many idiosyncrasies of WSNs, in particular the broadcast nature of communication. Our proposed approach is based on approximating the joint probability distribution over the sensors using undirected graphical models, ideally suited to exploit both the spatial correlations and the broadcast nature of communication. We present algorithms for optimally using such a model for data collection under different communication models, and for identifying an appropriate model to use for a given sensor network. Experiments over synthetic and real-world datasets show that our approach significantly reduces the data collection cost.


Sensor Network Sensor Node Wireless Sensor Network Communication Cost Junction Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: Wireless sensor networks: a survey. Computer Networks 38 (2002)Google Scholar
  2. 2.
    Arici, T., Gedik, B., Altunbasak, Y., Liu, L.: PINCO: A pipelined in-network compression scheme for data collection in wireless sensor networks. In: IEEE Intl. Conf. on Computer Communications and Networks (2003)Google Scholar
  3. 3.
    Blair, J.R.S., Peyton, B.: An Introduction to Chordal Graphs and Clique Trees. In: Graph Theory and Sparse Matrix Computation, pp. 1–29. Springer, New York (1993)Google Scholar
  4. 4.
    Chu, D., Deshpande, A., Hellerstein, J., Hong, W.: Approximate data collection in sensor networks using probabilistic models. In: Proceedings of the International Conference on Data Engineering (ICDE) (2006)Google Scholar
  5. 5.
    Cormode, G., Garofalakis, M., Muthukrishnan, S., Rastogi, R.: Holistic aggregates in a networked world: Distributed tracking of approximate quantiles. In: SIGMOD (2005)Google Scholar
  6. 6.
    Cristescu, R., Beferull-Lozano, B., Vetterli, M., Wattenhofer, R.: Network correlated data gathering with explicit communication: Np-completeness and algorithms. IEEE/ACM Transactions on Networking 14(1), 41–54 (2006)CrossRefGoogle Scholar
  7. 7.
    Cristescu, R., Beferull-Lozano, B., Vetterli, M.: Networked slepian-wolf: Theory and algorithms. In: Karl, H., Wolisz, A., Willig, A. (eds.) Wireless Sensor Networks. LNCS, vol. 2920, Springer, Heidelberg (2004)Google Scholar
  8. 8.
    Deshpande, A., Garofalakis, M., Jordan, M.: Efficient stepwise selection in decomposable models. In: UAI (2001)Google Scholar
  9. 9.
    Deshpande, A., Guestrin, C., Madden, S., Hellerstein, J., Hong, W.: Model-driven data acquisition in sensor networks. In: VLDB (2004)Google Scholar
  10. 10.
    Edwards, D.: Introduction to Graphical Modeling. Springer, New York (1995)Google Scholar
  11. 11.
    Guha, S., Khuller, S.: Approximation algorithms for connected dominating sets. Algorithmica 20(4), 374–387 (1998)zbMATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Gupta, H., Navda, V., Das, S., Chowdhary, V.: Efficient gathering of correlated data in sensor networks. In: MobiHoc. (2005)Google Scholar
  13. 13.
    Gupta, P., Kumar, P.R.: The capacity of wireless networks. IEEE Transactions on Information Theory 46, 388–404 (2000)zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Heinzelman, W.R., Chandrakasan, A., Balakrishnan, H.: Energy-efficient communication protocol for wireless microsensor networks. In: HICSS 2000. Proceedings of the 33rd Hawaii International Conference on System Sciences, vol. 8, p. 8020 (2000)Google Scholar
  15. 15.
    Intanagonwiwat, C., Govindan, R., Estrin, D.: Directed diffusion: A scalable and robust communication paradigm for sensor networks. In: ACM MobiCOM (2000)Google Scholar
  16. 16.
    Jensen, F.V., Jensen, F.: Optimal Junction Trees. In: Proceedings of the Tenth Annual Conference on Uncertainty in Artificial Intelligence, Seattle, Washington (July 1994)Google Scholar
  17. 17.
    Kotidis, Y.: Snapshot queries: Towards data-centric sensor networks. In: ICDE (2005)Google Scholar
  18. 18.
    Madden, S.: Intel lab data (2003),
  19. 19.
    Madden, S., Franklin, M.J., Hellerstein, J.M., Hong, W.: TAG: A tiny aggregation service for ad-hoc sensor networks. In: OSDI (2002)Google Scholar
  20. 20.
    Madden, S., Hong, W., Hellerstein, J., Franklin, M.: TinyDB web page,
  21. 21.
    Olston, C., Loo, B., Widom, J.: Adaptive precision setting for cached approximate values. In: SIGMOD (2001)Google Scholar
  22. 22.
    Pattem, S., Krishnamachari, B., Govindan, R.: The impact of spatial correlation on routing with compression in wireless sensor networks. In: IPSN (2004)Google Scholar
  23. 23.
    Pradhan, S., Ramchandran, K.: Distributed source coding using syndromes (DISCUS): Design and construction. IEEE Trans. Information Theory (2003)Google Scholar
  24. 24.
    Scaglione, A., Servetto, S.: On the interdependence of routing and data compression in multi-hop sensor networks. In: Mobicom (2002)Google Scholar
  25. 25.
    Silberstein, A., Braynard, R., Yang, J.: Constraint-chaining: On energy-efficient continuous monitoring in sensor networks. In: SIGMOD (2006)Google Scholar
  26. 26.
    Slepian, D., Wolf, J.: Noiseless coding of correlated information sources. IEEE Transactions on Information Theory 19(4) (1973)Google Scholar
  27. 27.
    Su, X.: A combinatorial algorithmic approach to energy efficient information collection in wireless sensor networks. ACM Trans. Sen. Netw. 3(1), 6 (2007)CrossRefGoogle Scholar
  28. 28.
    Whittaker, J.: Graphical Models in Applied Multivariate Statistics. Wiley Series in Probability and Mathematical Statistics. John Wiley, Chichester (1990)zbMATHGoogle Scholar
  29. 29.
    Widmann, M., Bretherton, C.: 50 km resolution daily precipitation for the pacific northwest (2003),
  30. 30.
    Wyner, A.D., Ziv, J.: The rate-distortion function for source coding with side information at the decoder. IEEE Transactions on Information Theory (1976)Google Scholar
  31. 31.
    Xiong, Z., Liveris, A.D., Cheng, S.: Distributed source coding for sensor networks. IEEE Signal Processing Magazine 21, 80–94 (2004)CrossRefGoogle Scholar
  32. 32.
    Yao, Y., Gehrke, J.: Query processing in sensor networks. In: CIDR (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Lidan Wang
    • 1
  • Amol Deshpande
    • 1
  1. 1.Computer Science DepartmentUniversity of MarylandUSA

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