Geo Referenced Dynamic Bayesian Networks for User Positioning on Mobile Systems

  • Boris Brandherm
  • Tim Schwartz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3479)


The knowledge of the position of a user is valuable for a broad range of applications in the field of pervasive computing. Different techniques have been developed to cope with the problem of uncertainty, noisy sensors, and sensor fusion.

In this paper we present a method, which is efficient in time- and space-complexity, and that provides a high scalability for in- and outdoor-positioning. The so-called geo referenced dynamic Bayesian networks enable the calculation of a user’s position on his own small hand-held device (e.g., Pocket PC) without a connection to an external server. Thus, privacy issues are considered and completely in the hand of the user.


Bayesian Network Time Slice Pervasive Computing Sensor Fusion Dynamic Bayesian Network 
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.
    Russell, S.J., Norvig, P.: Artificial Intelligence, A Modern Approach, 2nd edn., pp. 492–580. Pearson Education, London (2003)Google Scholar
  2. 2.
    Fox, D., Hightower, J., Liao, L., Schulz, D., Borriello, G.: Bayesian filtering for location estimation. IEEE Pervasive Computing 2, 24–33 (2002)CrossRefGoogle Scholar
  3. 3.
    Want, R., Hopper, A., Falcao, V., Gibbons, J.: The Active Badge Location System. ACM Transanctions on Information Systems 10, 91–102 (1992)CrossRefGoogle Scholar
  4. 4.
    Harter, A., Hopper, A., Steggles, P., Wart, A., Webster, P.: The anatomy of a context-aware application. In: 5th Annual ACM/IEEE International Conference on Mobile Computing and Networking (Mobicom 1999) (1999)Google Scholar
  5. 5.
    Ubisense Unlimited: Ubisense (2004),
  6. 6.
    Ni, L.M., Liu, Y., Lau, Y.C., Patil, A.P.: Landmarc: Indoor location sensing using active RFID. In: IEEE International Conference in Pervasive Computing and Communications 2003 (Percom 2003) (2003)Google Scholar
  7. 7.
    Amemiya, T., Yamashita, J., Hirota, K., Hirose, M.: Virtual Leading Blocks for the Deaf-Blind: A Real-Time Way-Finder by Verbal-Nonverbal Hybrid Interface and High-Density RFID Tag Space. In: Proceedings of the 2004 Virtual Reality (VR 2004), IEEE Virtual Reality, pp. 165–172 (2004)Google Scholar
  8. 8.
    Darwiche, A.: A differential approach to inference in Bayesian networks. Journal of the Association for Computing Machinery 50, 280–305 (2003)MathSciNetGoogle Scholar
  9. 9.
    Brandherm, B., Jameson, A.: An extension of the differential approach for Bayesian network inference to dynamic Bayesian networks. International Journal of Intelligent Systems 19, 727–748 (2004)zbMATHCrossRefGoogle Scholar
  10. 10.
    Krüger, A., Butz, A., Müller, C., Stahl, C., Wasinger, R., Steinberg, K.E., Dirschl, A.: The Connected User Interface: Realizing a Personal Situated Navigation Service. In: IUI - 2004 International Conference on Intelligent User Interfaces. ACM Press, New York (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Boris Brandherm
    • 1
  • Tim Schwartz
    • 1
  1. 1.Department of Computer ScienceSaarland UniversitySaarbrückenGermany

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