Distributed Inference for Network Localization Using Radio Interferometric Ranging

  • Dennis Lucarelli
  • Anshu Saksena
  • Ryan Farrell
  • I-Jeng Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4913)

Abstract

A localization algorithm using radio interferometric measurements is presented. A probabilistic model is constructed that accounts for general noise models and lends itself to distributed computation. A message passing algorithm is derived that exploits the geometry of radio interferometric measurements and can support sparse network topologies and noisy measurements. Simulations on real and simulated data show promising performance for 2D and 3D deployments.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Priyantha, N., Chakraborty, A., Balakrishnan, H.: The cricket location-support system. In: Proceedings of the 6th ACM MOBICOM Conference (2000)Google Scholar
  2. 2.
    Girod, L., Estrin, D.: Robust range estimation using acoustic and multimodal sensing. In: IEEE International Conference on Intelligent Robots and Systems (2001)Google Scholar
  3. 3.
    Bahl, P., Padmanabhan, V.N.: RADAR: An in-building RF-based user location and tracking system. In: Proceedings of INFOCOM 2000, March 2000, pp. 775–784 (2000)Google Scholar
  4. 4.
    Barton-Sweeney, A., Lymberopoulos, D., Savvides, A.: Sensor Localization and Camera Calibration in Distributed Camera Sensor Networks. In: Proceedings of IEEE BaseNets (October 2006)Google Scholar
  5. 5.
    Stoleru, R., He, T., Stankovic, J.A., Luebke, D.: A high-accuracy, low-cost localization system for wireless sensor networks. In: SenSys 2005. Proceedings of the 3rd International Conference on Embedded Networked Sensor Systems, pp. 13–26. ACM Press, New York (2005)CrossRefGoogle Scholar
  6. 6.
    Farrell, R., Garcia, R., Lucarelli, D., Terzis, A., Wang, I.-J.: Localization in multi-modal sensor networks. In: Third International Conference on Intelligent Sensors, Sensor Networks, and Information Processing, (to appear, December 2007)Google Scholar
  7. 7.
    Maróti, M., Völgyesi, P., Dóra, S., Kusý, B., Nádas, A., Lédeczi, Á., Balogh, G., Molnár, K.: Radio interferometric geolocation. In: SenSys 2005. Proceedings of the 3rd International Conference on Embedded Networked Sensor Systems, pp. 1–12. ACM Press, New York (2005)CrossRefGoogle Scholar
  8. 8.
    Kusý, B., Maróti, Á.L.M., Meertens, L.: Node density independent localization. In: IPSN 2006. Proceedings of the Fifth International Conference on Information Processing in Sensor Networks, pp. 441–448. ACM Press, New York (2006)CrossRefGoogle Scholar
  9. 9.
    Ihler, A.T., Moses, R.L., Fischer, I.J.W., Willsky, A.S.: Nonparametric belief propagation for self-localization of sensor networks. IEEE Journal on Selected Areas in Communications 23(4), 809–819 (2005)CrossRefGoogle Scholar
  10. 10.
    Fang, B.: Simple solutions for hyperbolic and related position fixes. IEEE Transactions on Aerospace and Electronic Systems 26(5), 748–753 (1990)CrossRefGoogle Scholar
  11. 11.
    Yedidia, J.S., Freeman, W.T., Weiss, Y.: Understanding Belief Propagation and its Generalizations. In: International Joint Conference on Artificial Intelligence (August 2001)Google Scholar
  12. 12.
    Paskin, M.A., Guestrin, C., McFadden, J.: A robust architecture for distributed inference in sensor networks. In: IPSN. Proceedings of the Fourth International Conference on Information Processing in Sensor Networks, pp. 55–62 (2005)Google Scholar
  13. 13.
    Zhang, D.-Q., Chang, S.-F.: Learning to Detect Scene Text Using Higher-order MRF with Belief Propagation. In: IEEE Workshop on Learning in Computer Vision and Pattern Recognition (June 2004)Google Scholar
  14. 14.
    Koller, D., Lerner, U., Angelov, D.: A general algorithm for approximate inference and its application to hybrid bayes nets. In: Proceedings of the Conference on Uncertainty in Artifical Intelligence (1999)Google Scholar
  15. 15.
    Bickson, D., Dolev, D., Weiss, Y.: Modified belief propagation algorithm for energy saving in wireless sensor networks. Technical Report TR-2005-85, The Hebrew University (2005)Google Scholar
  16. 16.
    Sudderth, E., Ihler, A., Freeman, W., Willsky, A.: Nonparametric belief propagation. In: CVPR (2003)Google Scholar
  17. 17.
    Isard, M.: Pampas: Real-valued graphical models for computer vision. In: Proceedings of CVPR (2003)Google Scholar
  18. 18.
    Ihler, A.T., Sudderth, E.B., Freeman, W.T., Willsky, A.S.: Efficient multiscale sampling from products of Gaussian mixtures. In: Thrun, S., Saul, L., Schölkopf, B. (eds.) Neural Information Processing Systems 16, MIT Press, Cambridge (2004)Google Scholar
  19. 19.
  20. 20.
    Eren, T., Aspnes, J., Whiteley, W., Yang, Y.R.: A theory of network localization. IEEE Transactions on Mobile Computing 5(12), 1663–1678 (2006)CrossRefGoogle Scholar
  21. 21.
  22. 22.
    Ihler, A.T., Fisher, I.J.W., Willsky, A.S.: Communication-constrained inference. Technical Report 2601, MIT, Laboratory for Information and Decision Systems (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Dennis Lucarelli
    • 1
  • Anshu Saksena
    • 1
  • Ryan Farrell
    • 1
    • 2
  • I-Jeng Wang
    • 1
  1. 1.Applied Physics LaboratoryJohns Hopkins UniversityLaurel 
  2. 2.Computer Science DepartmentUniversity of Maryland 

Personalised recommendations