Calibree: Calibration-Free Localization Using Relative Distance Estimations

  • Alex Varshavsky
  • Denis Pankratov
  • John Krumm
  • Eyal de Lara
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5013)


Existing localization algorithms, such as centroid or fingerprinting, compute the location of a mobile device based on measurements of signal strengths from radio base stations. Unfortunately, these algorithms require tedious and expensive off-line calibration in the target deployment area before they can be used for localization. In this paper, we present Calibree, a novel localization algorithm that does not require off-line calibration. The algorithm starts by computing relative distances between pairs of mobile phones based on signatures of their radio environment. It then combines these distances with the known locations of a small number of GPS-equipped phones to estimate absolute locations of all phones, effectively spreading location measurements from phones with GPS to those without. Our evaluation results show that Calibree performs better than the conventional centroid algorithm and only slightly worse than fingerprinting, without requiring off-line calibration. Moreover, when no phones report their absolute locations, Calibree can be used to estimate relative distances between phones.


Mobile Phone Localization Algorithm Absolute Location Cell Tower Centroid Algorithm 
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.
    Bahl, P., Padmanabhan, V.N.: RADAR: An in-building RF-based user location and tracking system. In: Proceedings of INFOCOM, pp. 775–784 (2000)Google Scholar
  2. 2.
    Capkun, S., Hamdi, M., Hubaux, J.-P.: GPS-free positioning in mobile ad-hoc networks. Cluster Computing Journal 5(2), 157–167 (2002)CrossRefGoogle Scholar
  3. 3.
    Chen, M.Y., Sohn, T., Chmelev, D., Hightower, D.H.J., Hughes, J., LaMarca, A., Potter, F., Smith, I., Varshavsky, A.: Practical metropolitan-scale positioning for gsm phones. In: Proceedings of the Eighth International Conference on Ubiquitous Computing, Irvine, California (September 2006)Google Scholar
  4. 4.
    Dabek, F., Cox, R., Kaashoek, F., Morris, R.: Vivaldi: a decentralized network coordinate system. In: Proceedings of SIGCOMM, pp. 15–26 (2004)Google Scholar
  5. 5.
    Hazas, M., Kray, C., Gellersen, H., Agbota, H., Kortuem, G., Krohn, A.: A relative positioning system for co-located mobile devices (2005)Google Scholar
  6. 6.
    Hightower, J., Want, R., Borriello, G.: SpotON: An indoor 3d location sensing technology based on RF signal strength. Technical Report 00-02-02, University of Washington, Department of Computer Science and Engineering, Seattle, WA (February 2000)Google Scholar
  7. 7.
    Hofmann-Wellenhof, B., Lichtenegger, H., Collins, J.: Global Positioning System: Theory and Practice, 3rd edn. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  8. 8.
    Krumm, J., Hinckley, K.: The nearme wireless proximity server. In: Davies, N., Mynatt, E.D., Siio, I. (eds.) UbiComp 2004. LNCS, vol. 3205, pp. 283–300. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    LaMarca, A., Hightower, J., Smith, I., Consolvo, S.: Self-mapping in 802.11 location systems. In: Beigl, M., Intille, S.S., Rekimoto, J., Tokuda, H. (eds.) UbiComp 2005. LNCS, vol. 3660, pp. 87–104. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Langendoen, K., Reijers, N.: Distributed localization in wireless sensor networks: a quantitative comparison. Computer Networks 43(4), 499–518 (2003)CrossRefzbMATHGoogle Scholar
  11. 11.
    Otsason, V., Varshavsky, A., LaMarca, A., de Lara, E.: Accurate gsm indoor localization. In: Beigl, M., Intille, S.S., Rekimoto, J., Tokuda, H. (eds.) UbiComp 2005. LNCS, vol. 3660, Springer, Heidelberg (2005)Google Scholar
  12. 12.
    Press, W.H., Flannery, B.P., Teukolsky, S.A., Vetterling, W.T.: Numerical Recipes in C: The Art of Scientific Computing, 2nd edn. Cambridge University Press, Cambridge (1992)zbMATHGoogle Scholar
  13. 13.
    Savarese, C., Rabay, J., Langendoen, K.: Robust positioning algorithms for distributed ad-hoc wireless sensor networks. In: Proceedings of USENIX Technical Annual Conference (2002)Google Scholar
  14. 14.
    Shang, Y., Ruml, W.: Improved mds-based localization. In: Proceedings of Infocom (2004)Google Scholar
  15. 15.
    Sinnott, R.W.: Virtues of haversine. Sky and Telescope 68(2) (1984)Google Scholar
  16. 16.
    Smith, I.E.: Social mobile applications. IEEE Computer 38(4), 84–85 (2005)CrossRefGoogle Scholar
  17. 17.
    Sprint. Location based services network overview. Technical report (2005)Google Scholar
  18. 18.
    Varshavsky, A., Chen, M., de Lara, E., Froehlich, J., Haehnel, D., Hightower, J., LaMarca, A., Potter, F., Sohn, T., Tang, K., Smith, I.: Are GSM phones THE solution for localization? In: IEEE Workshop on Mobile Computing Systems and Applications (April 2006)Google Scholar
  19. 19.
    GPS-Enabled Location-Based Services (LBS) Subscribers Will Total 315 Million in Five Years,

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Alex Varshavsky
    • 1
  • Denis Pankratov
    • 1
  • John Krumm
    • 2
  • Eyal de Lara
    • 1
  1. 1.Department of Computer ScienceUniversity of TorontoCanada
  2. 2.Microsoft ResearchUSA

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