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LEAPS: A Location Estimation and Action Prediction System in a Wireless LAN Environment

  • Qiang Yang
  • Yiqiang Chen
  • Jie Yin
  • Xiaoyong Chai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3222)

Abstract

Location estimation and user behavior recognition are research issues that go hand in hand. In the past, these two issues have been investigated separately. In this paper, we present an integrated framework called LEAPS (location estimation and action prediction), jointly developed by Hong Kong University of Science and Technology, and the Institute of Computing, Shanghai, of the Chinese Academy of Sciences that combines two areas of interest, namely, location estimation and plan recognition, in a coherent whole. Under this framework, we have been carrying out several investigations, including action and plan recognition from low-level signals and location estimation by intelligently selecting access points (AP). Our two-layered model, including a sensor-level model and an action and goal prediction model, allows for future extensions in more advanced features and services.

Keywords

Access Point Location Estimation Dynamic Bayesian Network Plan Recognition Dynamic Bayesian Network Model 
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.

References

  1. 1.
    Albrecht, D., Zukerman, I., Nicholson, A.: Bayesian models for keyhole plan recognition in an adventure game. User Modelling and User-adapted Interaction 8(1-2), 5–47 (1998)CrossRefGoogle Scholar
  2. 2.
    Bahl, P., Padmanabhan, V.N.: RADAR: An in-building RF-based user location and tracking system. In: Proceedings of IEEE INFOCOM 2000, pp. 775–784 (2000)Google Scholar
  3. 3.
    Blaylock, N., Allen, J.: Corpus-based statitical goal recognition. In: Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, Mexico, August 2003, pp. 1303–1308 (2003)Google Scholar
  4. 4.
    Charniak, E., Goldman, R.: A bayesian model of plan recognition. Artificial Intelligence Journal 64, 53–79 (1993)CrossRefGoogle Scholar
  5. 5.
    Kautz, H., Allen, J.F.: Generalized plan recognition. In: Proceedings of AAAI 1986, pp. 32–38 (1986)Google Scholar
  6. 6.
    Ladd, A., Bekris, K., Marceau, G., Rudys, A., Kavraki, L., Wallach, D.: Robotics-based location sensing using wireless ethernet. In: Proceedings of MOBICOM 2002, Atlanta, Georgia, USA (September 2002)Google Scholar
  7. 7.
    Lesh, N., Etzioni, O.: A sound and fast goal recognizer. In: Proceedings of IJCAI 1995, Montreal, Canada, pp. 1704–1710 (1995)Google Scholar
  8. 8.
    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), Dallas, TX, USA (March 2003)Google Scholar
  9. 9.
    Patterson, D.J., Liao, L., Fox, L., Kautz, H.: Inferring high-level behavior from low-level sensors. In: Dey, A.K., Schmidt, A., McCarthy, J.F. (eds.) UbiComp 2003. LNCS, vol. 2864, pp. 73–89. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  10. 10.
    Pynadath, D.V., Wellman, M.P.: Probabilistic state-dependent grammars for plan recognition. In: Proceedings of the Sixteenth Conference on UAI, San Francisco, CA, pp. 507–514 (2000)Google Scholar
  11. 11.
    Roos, T., Myllymaki, P., Tirri, H., Misikangas, P., Sievanen, J.: A probabilistic approach to WLAN user location estimation. International Journal of Wireless Information Networks 9(3), 155–164 (2002)CrossRefGoogle Scholar
  12. 12.
    Yin, J., Chai, X., Yang, Q.: High-level goal recognition in a wireless lan. In: Proceedings of the Nineteenth National Conference on Artificial Intelligence (AAAI 2004), San Jose, CA, USA (2004)Google Scholar
  13. 13.
    Youssef, M., Agrawala, A.: Handling samples correlation in the horus system. In: IEEE InfoCom 2003, Hong Kong (March 2004)Google Scholar
  14. 14.
    Youssef, M., Agrawala, A., Shankar, U.: WLAN location determination via clustering and probability distributions. In: Proceedings of IEEE PerCom 2003 (March 2003)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2004

Authors and Affiliations

  • Qiang Yang
    • 1
  • Yiqiang Chen
    • 2
  • Jie Yin
    • 1
  • Xiaoyong Chai
    • 1
  1. 1.Department of Computer ScienceHong Kong University of Science and TechnologyKowloon, Hong KongChina
  2. 2.Shanghai Division, Institute of Computing technologyChinese Academy of SciencesShanghaiChina

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