Scalable Data Collection in Sensor Networks

  • Asad Awan
  • Suresh Jagannathan
  • Ananth Grama
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5374)

Abstract

Dense sensor deployments impose significant constraints on aggregate network data rate and resource utilization. Effective protocols for such data transfers rely on spatio-temporal correlations in sensor data for in-network data compression. The message complexity of these schemes is generally lower bounded by n, for a network with n sensors, since correlation is not collocated with sensing. Consequently, as the number of nodes and network density increase, these protocols become increasingly inefficient. We present here a novel protocol, called SNP, for fine-grained data collection, which requires approximately O(n − R) messages, where R, a measure of redundancy in sensed data generally increases with density. SNP uses spatio-temporal correlations to near-optimally compress data at the source, reducing network traffic and power consumption. We present a comprehensive information theoretic basis for SNP and establish its superior performance in comparison to existing approaches. We support our results with a comprehensive experimental evaluation of the performance of SNP in a real-world sensor network testbed.

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References

  1. 1.
    Awan, A., Jagannathan, S., Grama, A.: Macroprogramming heterogeneous sensor network systems using COSMOS. In: Proc. of EuroSys (March 2007)Google Scholar
  2. 2.
    Chu, D., Deshpande, A., Hellerstein, J.M., Hong, W.: Approximate data collection in sensor networks using probabilistic models. In: Proc. of ICDE 2006 (April 2006)Google Scholar
  3. 3.
    Levis, P., et al.: The Emergence of Networking Abstractions and Techniques in TinyOS. In: Proc. of NSDI 2004 (March 2004)Google Scholar
  4. 4.
    Gupta, P., Kumar, P.R.: The capacity of wireless networks. IEEE Transactions on Information Theory IT-46(2) (March 2000)Google Scholar
  5. 5.
    Heinzelman, W.R., Chandrakasan, A., Balakrishnan, H.: Energy-efficient communication protocols for wireless microsensor networks. In: Proc. of HICSS (January 2000)Google Scholar
  6. 6.
    Intanagonwiwat, C., Govindan, R., Estrin, D., Heidemann, J., Silva, F.: Directed diffusion for wireless sensor networking. ACM/IEEE Transactions on Networking 11(1), 2–16 (2002)CrossRefGoogle Scholar
  7. 7.
    Kulik, J., Rabiner, W., Balakrishnan, H.: Adaptive protocols for information dissemination in wireless sensor networks. In: Proc. of Mobicom 1999 (August 1999)Google Scholar
  8. 8.
    Pattem, S., Krishnamachari, B., Govindan, R.: The impact of spatial correlation on routing with compression in wireless sensor networks. In: Proc. of IPSN 2004 (April 2004)Google Scholar
  9. 9.
    Pradhan, S., Kusuma, J., Ramchandran, K.: Distributed compression in a dense microsensor network. IEEE Signal Processing Magazine 19(2) (March 2002)Google Scholar
  10. 10.
    Savvides, A., Han, C.-C., Strivastava, M.B.: Dynamic fine-grained localization in ad-hoc networks of sensors. In: Mobicom 2001 (July 2001)Google Scholar
  11. 11.
    Slepian, D., Wolf, J.: Noiseless coding of correlated information sources. IEEE Transactions on Information Theory 19(4)Google Scholar
  12. 12.
    Tolle, G.: Sonoma redwoods data (2005), www.cs.berkeley.edu/~get/sonoma

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Asad Awan
    • 1
  • Suresh Jagannathan
    • 1
  • Ananth Grama
    • 1
  1. 1.Purdue UniversityWest LafayetteUSA

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