Polaris: A Fingerprint-Based Localization System over Wireless Networks

  • Nan Zhang
  • Jianhua Feng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7418)


As the foundation of location-based services, accurate localization has attracted considerable attention. A typical wi-fi localization system employs a fingerprint-based method, which constructs a fingerprint database and returns user’s location based on similar fingerprints. Existing systems cannot accurately locate users in a metropolitan-scale because of the requirement of large fingerprint data sets, complicated deployment, and the inefficient search algorithm. To address these problems, we develop a localization system called Polaris. By the contribution of users, we construct a large fingerprint database. We introduce an effective localization model based on novel similarity measures of fingerprints. For fast localization, we devise an efficient algorithm for matching similar fingerprints, and develop a cluster-based representative fingerprint selection method to improve the performance. We conduct extensive experiments on real data sets, and the experimental results show that our method is accurate and efficient, significantly outperforming state-of-the-art methods.


Wireless Network Access Point Inverted Index Representative Selection Anonymous User 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Hightower, J., Borriello, G.: Location systems for ubiquitous computing. Computer 34(8), 57–66 (2001)CrossRefGoogle Scholar
  2. 2.
    Bahl, P., Padmanabhan, V.N.: Radar: An in-building rf-based user location and tracking system. In: INFOCOM, pp. 775–784 (2000)Google Scholar
  3. 3.
    LaMarca, A., Chawathe, Y., Consolvo, S., Hightower, J., Smith, I., Scott, J., Sohn, T., Howard, J., Hughes, J., Potter, F., Tabert, J., Powledge, P., Borriello, G., Schilit, B.N.: Place Lab: Device Positioning Using Radio Beacons in the Wild. In: Gellersen, H.-W., Want, R., Schmidt, A. (eds.) PERVASIVE 2005. LNCS, vol. 3468, pp. 116–133. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Enge, P., Misra, P.: Special issue on global positioning system. Proceedings of the IEEE 87(1), 3–15 (1999)CrossRefGoogle Scholar
  5. 5.
    Chen, M.Y., Sohn, T., Chmelev, D., Haehnel, D., Hightower, J., Hughes, J., LaMarca, A., Potter, F., Smith, I., Varshavsky, A.: Practical Metropolitan-Scale Positioning for GSM Phones. In: Dourish, P., Friday, A. (eds.) UbiComp 2006. LNCS, vol. 4206, pp. 225–242. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    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, pp. 141–158. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Benikovsky, J., Brida, P., Machaj, J.: Localization in Real GSM Network with Fingerprinting Utilization. In: Chatzimisios, P., Verikoukis, C., Santamaría, I., Laddomada, M., Hoffmann, O. (eds.) MOBILIGHT 2010. LNICST, vol. 45, pp. 699–709. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Cheng, Y.C., Chawathe, Y., LaMarca, A., Krumm, J.: Accuracy characterization for metropolitan-scale wi-fi localization. In: MobiSys, pp. 233–245 (2005)Google Scholar
  9. 9.
    Haeberlen, A., Flannery, E., Ladd, A.M., Rudys, A., Wallach, D.S., Kavraki, L.E.: Practical robust localization over large-scale 802.11 wireless networks. In: MOBICOM, pp. 70–84 (2004)Google Scholar
  10. 10.
    Bahl, P., Balachandran, A., Padmanabhan, V.: Enhancements to the radar user location and tracking system. Microsoft Research Technical Report (2000)Google Scholar
  11. 11.
    Want, R., Hopper, A., Falcao, V., Gibbons, J.: The active badge location system. ACM Trans. Inf. Syst. 10(1), 91–102 (1992)CrossRefGoogle Scholar
  12. 12.
    Harter, A., Hopper, A., Steggles, P., Ward, A., Webster, P.: The anatomy of a context-aware application. In: MOBICOM, pp. 59–68 (1999)Google Scholar
  13. 13.
    Priyantha, N.B., Chakraborty, A., Balakrishnan, H.: The cricket location-support system. In: MOBICOM, pp. 32–43 (2000)Google Scholar
  14. 14.
    de Ipiña, D.L., Mendonça, P.R.S., Hopper, A.: Trip: A low-cost vision-based location system for ubiquitous computing. PUC 6(3), 206–219 (2002)Google Scholar
  15. 15.
    Azizyan, M., Constandache, I., Choudhury, R.R.: Surroundsense: mobile phone localization via ambience fingerprinting. In: MOBICOM, pp. 261–272 (2009)Google Scholar
  16. 16.
    Laitinen, H., Lahteenmaki, J., Nordstrom, T.: Database correlation method for gsm location. In: Vehicular Technology Conference, vol. 4, pp. 2504–2508 (2001)Google Scholar
  17. 17.
    Hightower, J., Borriello, G.: Particle Filters for Location Estimation in Ubiquitous Computing: A Case Study. In: Davies, N., Mynatt, E.D., Siio, I. (eds.) UbiComp 2004. LNCS, vol. 3205, pp. 88–106. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  18. 18.
    Pandya, D., Jain, R., Lupu: Indoor location estimation using multiple wireless technologies. In: PIMRC, pp. 2208–2212 (2003)Google Scholar
  19. 19.
    Smailagic, A., Kogan, D.: Location sensing and privacy in a context-aware computing environment. IEEE Wireless Communications 9(5), 10–17 (2002)CrossRefGoogle Scholar
  20. 20.
    Papapostolou, A., Chaouchi, H.: WIFE: Wireless Indoor Positioning Based on Fingerprint Evaluation. In: Fratta, L., Schulzrinne, H., Takahashi, Y., Spaniol, O. (eds.) NETWORKING 2009. LNCS, vol. 5550, pp. 234–247. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Nan Zhang
    • 1
  • Jianhua Feng
    • 1
  1. 1.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

Personalised recommendations