WASP: An Enhanced Indoor Locationing Algorithm for a Congested Wi-Fi Environment

  • Hsiuping Lin
  • Ying Zhang
  • Martin Griss
  • Ilya Landa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5801)


Accurate and reliable location information is important to many context-aware mobile applications. While the Global Positioning System (GPS) works quite well outside, it is quite problematic for indoor locationing. In this paper, we introduce WASP, an enhanced indoor locationing algorithm. WASP is based on the Redpin algorithm which matches the received Wi-Fi signal with the signals in the training data and uses the position of the closest training data as the user’s current location. However, in a congested Wi-Fi environment the Redpin algorithm gets confused because of the unstable radio signals received from too many APs. WASP addresses this issue by voting the right location from more neighboring training examples, weighting Access Points (AP) based on their correlation with a certain location, and automatic filtering of noisy APs. WASP significantly outperform the-state-of-the-art Redpin algorithm. In addition, this paper also reports our findings on how the size of the training data, the physical size of the room and the number of APs affect the accuracy of indoor locationing.


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  1. 1.
    Bahl, P., Padmanabhan, V.: Radar: an in-building rf-based user location and tracking system. In: IEEE INFOCOM 2000, vol. 2, pp. 775–784 (2000)Google Scholar
  2. 2.
    Barcelo, F., Evennou, F., de Nardis, L., Tome, P.: Advances in indoor location. In: LIAISON - ISHTAR Workshop (Septemper 2006)Google Scholar
  3. 3.
    Bolliger, P.: Redpin - adaptive, zero-configuration indoor localization through user collaboration. In: ACM International Workshop, March 2008, pp. 55–60 (2008)Google Scholar
  4. 4.
    Brunato, M., Battiti, R.: Statistical learning theory for location fingerprinting in wireless lans. Computer Networks and ISDN Systems 47, 825–845 (2005)CrossRefMATHGoogle Scholar
  5. 5.
    Carlotto, A., Parodi, M., Bonamico, C., Lavagetto, F., Valla, M.: Proximity classification for mobile devices using wi-fi environment similarity. In: ACM International Workshop, pp. 43–48 (2008)Google Scholar
  6. 6.
    Correa, J., Katz, E., Collins, P., Griss, M.: Room-level wi-fi location tracking. CyLab Mobility Research Center technical report MRC-TR-2008-02 (November 2008)Google Scholar
  7. 7.
    Fan, R., Chen, P., Lin, C.: Working set selection using second order information for training support vector machines. Journal of Machine Learning Research 6, 1889–1918 (2005)MathSciNetMATHGoogle Scholar
  8. 8.
    Hightower, J., Borriello, G.: Location systems for ubiquitous computing. IEEE Computer 34, 57–66 (2001)CrossRefGoogle Scholar
  9. 9.
    Ho, W., Smailagic, A., Siewiorek, D., Faloutsos, C.: An adaptive two-phase approach to wifi location sensing. IEEE International Conference 5, 456 (2006)Google Scholar
  10. 10.
    Li, B., Salter, J., Dempster, A., Rizos, C.: Indoor positioning techniques based on wireless lan. In: IEEE International Conference, p. 113 (March 2006)Google Scholar
  11. 11.
    Paschalidis, I., Lai, W., Ray, S.: Statistical Location Detection. In: Mao, G., Fidan, B. (eds.) Localization Algorithms and Strategies for Wireless Sensor Networks: Monitoring and Surveillance Techniques for Target Tracking, IGI Global (2009)Google Scholar
  12. 12.
    Seshadri, V., Zaruba, G., Huber, M.: A bayesian sampling approach to in-door localization of wireless devices using received signal strength indication. In: IEEE International Conference, March 2005, pp. 75–84 (2005)Google Scholar
  13. 13.
    Wu, C., Fu, L., Lian, F.: Wlan location determination in e-home via support vector classification. In: IEEE International Conference, vol. 2, pp. 1026–1031 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Hsiuping Lin
    • 1
  • Ying Zhang
    • 1
  • Martin Griss
    • 1
  • Ilya Landa
    • 1
  1. 1.Carnegie Mellon Silicon ValleyMoffett FieldUSA

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