Correlation, Coding, and Cooperation in Wireless Sensor Networks

  • Samar Agnihotri
  • Pavan Nuggehalli
  • H. S. Jamadagni
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4837)

Abstract

We consider a single-hop data-gathering sensor network, consisting of a set of sensor nodes that transmit data periodically to a base-station. We are interested in maximizing the lifetime of this network. With our definition of network lifetime and the assumption that the radio transmission energy consumption forms the most significant portion of the total energy consumption at a sensor node, we attempt to enhance the network lifetime by reducing the transmission energy budget of sensor nodes by exploiting three system-level opportunities.

We pose the problem of maximizing lifetime as a max-min optimization problem subject to the constraint of successful data collection and limited energy supply at each node. This turns out to be an extremely difficult optimization to solve. To reduce the complexity of this problem, we allow the sensor nodes and the base-station to interactively communicate with each other and employ instantaneous decoding at the base-station. The chief contribution of the paper is to show that the computational complexity of our problem is determined by the complex interplay of various system-level opportunities and challenges.

Keywords

Sensor networks Lifetime maximization Multi-access networks Joint source-channel coding Data correlation Slepian-Wolf coding Scheduling 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chang, J., Tassiulas, L.: Energy conserving routing in wireless ad-hoc networks. In: Proc. IEEE INFOCOM 2000, Tel-Aviv, Israel (March 2000)Google Scholar
  2. 2.
    Kang, I., Poovendran, R.: Maximizing static network lifetime of wireless broadcast adhoc networks. In: Proc. IEEE ICC 2003, Anchorage, AK (May 2003)Google Scholar
  3. 3.
    Heinzelman, W.R., Chandrakasan, A., Balakrishnan, H.: Energy-efficient communication protocol for wireless microsensor networks. In: Proc. HICSS 2000, Maui, HI (2000)Google Scholar
  4. 4.
    Slepian, D., Wolf, J.K.: Noiseless coding of correlated information sources. IEEE Trans. Inform. Theory IT-19(4), 471–480 (1973)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Xiong, Z., Liveris, A.D., Cheng, S.: Distributed source coding for sensor networks. IEEE Signal Processing Mag. 21 (September 2004)Google Scholar
  6. 6.
    Agnihotri, S., Nuggehalli, P., Rao, R.: Enhancing sensor network lifetime using interactive communication. In: Proc. IEEE ISIT 2007, Nice, France (June 2007)Google Scholar
  7. 7.
    Uysal-Biyikoglu, E., Prabhakar, B., El Gamal, A.: Energy-efficient packet transmission over a wireless link. IEEE Trans. Networking 10(4), 487–499 (2002)CrossRefGoogle Scholar
  8. 8.
    Yei, W., Heidemann, J., Estrin, D.: An energy-efficient MAC protocol for wireless sensor networks. In: Proc. 21st Intl. Ann. Joint Conf. of the IEEE Computer and Communication Societies (2002)Google Scholar
  9. 9.
    Bhardwaj, M., Chandrakasan, A.P.: Bounding the lifetime of sensor networks via optimal role assignments. In: Proc. IEEE INFOCOM 2002, New York City (June 2002)Google Scholar
  10. 10.
    Singh, S., Woo, M., Raghavendra, C.S.: Power-aware routing in mobile ad hoc networks. In: Proc. ACM MOBICOM 1998, Dallas, TX (October 1998)Google Scholar
  11. 11.
    Rodoplu, V., Meng, T.: Minimum energy mobile wireless networks. IEEE JSAC 17(8), 1333–1344 (1999)Google Scholar
  12. 12.
    Sadagopan, N., Krishnamachari, B.: Maximizing data extraction in energy-limited sensor networks. In: Proc. IEEE INFOCOM 2004, Hong Kong (March 2004)Google Scholar
  13. 13.
    Li, Q., Aslam, J., Rus, D.: Online power-aware routing in wireless ad-hoc networks. In: Proc. ACM MOBICOM 2001, Rome, Italy (June 2001)Google Scholar
  14. 14.
    Baek, S.J., de Veciana, G., Su, X.: Minimizing energy consumption in large-scale sensor networks through distributed data compression and hierarchical aggregation. IEEE JSAC 22(6), 1130–1140 (2004)Google Scholar
  15. 15.
    Cristescu, R., Lozano, B.B., Vetterli, M.: On network correlated data gathering. In: Proc. IEEE INFOCOM 2004, Hong Kong (March 2004)Google Scholar
  16. 16.
    Agnihotri, S., Nuggehalli, P., Jamadagni, H.S.: On maximizing lifetime of a sensor cluster. In: Proc. WoWMoM 2005, Taormina, Italy (June 2005)Google Scholar
  17. 17.
    Cover, T.M., El Gamal, A., Salehi, M.: Multiple access channels with arbitrarily correlated sources. IEEE Trans. Inform. Theory IT-26(6), 648–657 (1980)CrossRefGoogle Scholar
  18. 18.
    Cover, T.M., Thomas, J.: Elements of Information Theory. John Wiley & Sons, New York (1991)MATHGoogle Scholar
  19. 19.
    Barros, J., Servetto, S.D.: Network information flow with correlated sources. IEEE Trans. Inform. Theory IT-52(1), 155–170 (2006)CrossRefMathSciNetGoogle Scholar
  20. 20.
    Kawadia, V., Kumar, P.R.: A cautionary perspective on cross-layer design. IEEE Wireless Comm. Mag. 12(1), 3–11 (2005)CrossRefGoogle Scholar
  21. 21.
    Agnihotri, S.: New models for the correlation in sensor data. pre-print available at arXiv: cs.IT/0702035Google Scholar
  22. 22.
    Rubin, F.: A search procedure for Hamilton paths and circuits. Journal of ACM 21(4), 576–580 (1974)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Samar Agnihotri
    • 1
  • Pavan Nuggehalli
    • 1
  • H. S. Jamadagni
    • 1
  1. 1.CEDTIndian Institute of ScienceBangaloreIndia

Personalised recommendations