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Organizing a Global Coordinate System from Local Information on an Ad Hoc Sensor Network

  • Radhika Nagpal
  • Howard Shrobe
  • Jonathan Bachrach
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2634)

Abstract

We demonstrate that it is possible to achieve accurate localization and tracking of a target in a randomly placed wireless sensor network composed of inexpensive components of limited accuracy. The crucial enabler for this is a reasonably accurate local coordinate system aligned with the global coordinates. We present an algorithm for creating such a coordinate system without the use of global control, globally accessible beacon signals, or accurate estimates of inter-sensor distances. The coordinate system is robust and automatically adapts to the failure or addition of sensors. Extensive theoretical analysis and simulation results are presented. Two key theoretical results are: there is a critical minimum average neighborhood size of 15 for good accuracy and there is a fundamental limit on the resolution of any coordinate system determined strictly from local communication. Our simulation results show that we can achieve position accuracy to within 20% of the radio range even when there is variation of up to 10% in the signal strength of the radios. The algorithm improves with finer quantizations of inter-sensor distance estimates: with 6 levels of quantization position errors better than 10% are achieved. Finally we show how the algorithm gracefully generalizes to target tracking tasks.

Keywords

Sensor Network Wireless Sensor Network Distance Estimate Global Coordinate System Radio Range 
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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References

  1. 1.
    Paramvir Bahl and Venkata N. Padmanabhan. Radar: An in-building rf-based user location and tracking system. In Proceedings of Infocom 2000, 2000.Google Scholar
  2. 2.
    Nirupama Bulusu, John Heidemann, Deborah Estrin, and Tommy Tran. Self-configuring localization systems: Design and experimental evaluation. Submmited to ACM TECS Special Issue on Netowork Embedded Computing, August 2002.Google Scholar
  3. 3.
  4. 4.
    L. Doherty, L. El Ghaoui, and K. S. J. Pister. Convex position estimation in wireless sensor networks. In Proceedings of Infocom 2001, April 2001.Google Scholar
  5. 5.
    J. Hill, R. Szewczyk, A. Woo, S. Hollar, D. Culler, and K. Pister. System architecture directions for networked sensors. In Proceedings of ASPLOS-IX, 2000.Google Scholar
  6. 6.
    B. Hofmann-Wellenhoff, H. Lichtennegger, and J. Collins. Global Positioning System: Theory and Practice, Fourth Edition. Springer Verlag, 1997.Google Scholar
  7. 7.
    L. Kleinrock and J. Silvester. Optimum tranmission radii for packet radio networks or why six is a magic number. Proc. Natnl. Telecomm. Conf., pages 4.3.1–4.3.5, 1978.Google Scholar
  8. 8.
    N. Lynch. Distributed Algorithms. Morgan Kaufmann Publishers, Wonderland, 1996.zbMATHGoogle Scholar
  9. 9.
    James D. McLurkin. Algorithms for distributed sensor networks. Master’s thesis, UCB, December 1999.Google Scholar
  10. 10.
    W. Mendenhall, D. Wackerly, and R. Scheaffer. Mathematical Statistics with Applications. PWS-Kent Publishing Company, Boston, 1989.Google Scholar
  11. 11.
    R. Nagpal. Organizing a global coordinate system from local information on an amorphous computer. AI Memo 1666, MIT, 1999.Google Scholar
  12. 12.
    R. Nagpal and D. Coore. An algorithm for group formation in an amorphous computer. In Proceedings of the 10th IASTED International Conference on Parallel and Distributed Computing and Systems (PDCS’98), October 1998.Google Scholar
  13. 13.
    Philips, Shivendra, Panwar, and Tatami. Connectivity properties of a packet radio network model. IEEE Transactions on Information Theory, 35(5), September 1998.Google Scholar
  14. 14.
    Nissanka B. Priyantha, Anit Chakraborty, and Hari Balakrishnan. The cricket location-support system. In Proceedings of MobiCom 2000, August 2000.Google Scholar
  15. 15.
    A. Savvides, C. Han, and M. Strivastava. Dynamic fine-grained localization in ad-hoc networks of sensors. In Proceedings of ACM SIGMOBILE, July 2001.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Radhika Nagpal
    • 1
  • Howard Shrobe
    • 1
  • Jonathan Bachrach
    • 1
  1. 1.Artificial Intelligence LaboratoryMassachusetts Institute of TechnologyCambridge

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