FIMO: A Novel WiFi Localization Method

  • Yao Zhou
  • Leilei Jin
  • Cheqing Jin
  • Aoying Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7808)


With the development of technology and the proliferation of mobile computing devices, people’s need for pervasive computing is rapidly growing. As a critical part of pervasive computing, Location Based Service (LBS) has drawn more and more attention. Localization techniques that report the real-time position of a moving object are key in this area. So far, the outdoor localization technologies (i.e, GPS) are relatively mature, while the indoor localization technologies are still under improvement. In this paper, we propose a novel WiFi localization method, called FIMO (FInd Me Out). In this method, we take the instability of signal strength and the movement of objects into consideration when determining the location based on Fingerprint. Experimental results show the proposed method is capable of estimating a moving object’s location precisely.


LBS localization indoor WiFi 


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  1. 1.
    Bahl, P., Padmanabhan, V.N.: Radar: An in-building rf-based user location and tracking system. In: Proc. of INFOCOM, pp. 775–784 (2000)Google Scholar
  2. 2.
    Baniukevic, A., Sabonis, D., Jensen, C.S., Lu, H.: Improving wi-fi based indoor positioning using bluetooth add-ons. In: Proc. of MDM, pp. 246–255. IEEE Computer Society, Washington, DC (2011)Google Scholar
  3. 3.
    Bargh, M.S., de Groote, R.: Indoor localization based on response rate of bluetooth inquiries. In: Proc. of MELT, pp. 49–54. ACM, New York (2008)CrossRefGoogle Scholar
  4. 4.
    Chen, Q., Lee, D.-L., Lee, W.-C.: Rule-based wifi localization methods. In: Proc. of EUC, pp. 252–258. IEEE Computer Society, Washington, DC (2008)Google Scholar
  5. 5.
    Chen, Y.-C., Chiang, J.-R., Chu, H.-H., Huang, P., Tsui, A.W.: Sensor-assisted wi-fi indoor location system for adapting to environmental dynamics. In: Proc. of MSWiM, pp. 118–125. ACM, New York (2005)Google Scholar
  6. 6.
    Hernández, N., Herranz, F., Ocaña, M., Bergasa, L.M., Alonso, J.M., Magdalena, L.: Wifi localization system based on fuzzy logic to deal with signal variations. In: Proc. of ETFA, pp. 317–322. IEEE Press, Piscataway (2009)Google Scholar
  7. 7.
    Ho, W., Smailagic, A., Siewiorek, D.P., Faloutsos, C.: An adaptive two-phase approach to wifi location sensing. In: Proc. of PERCOMW, pp. 452–456. IEEE Computer Society, Washington, DC (2006)Google Scholar
  8. 8.
    Jin, G.-Y., Lu, X.-Y., Park, M.-S.: An indoor localization mechanism using active rfid tag. In: Proc. of SUTC, pp. 40–43. IEEE Computer Society, Washington, DC (2006)Google Scholar
  9. 9.
    Lee, D.L., Chen, Q.: A model-based wifi localization method. In: Proc. of InfoScale, pp. 40:1–40:7. ICST (2007)Google Scholar
  10. 10.
    Letchner, J., Fox, D., Lamarca, A.: Large-scale localization from wireless signal strength. In: Proc. of the National Conference on Artificial Intelligence, pp. 15–20. The MIT Press (2005)Google Scholar
  11. 11.
    Seidel, S.Y., Rapport, T.S.: 914 MHz path loss prediction model for indoor wireless communications in multi-floored buildings. In: Proc. of Antennas Propagation. IEEE Trars (1992)Google Scholar
  12. 12.
    Wang, R., Zhao, F., Luo, H., Lu, B., Lu, T.: Fusion of wi-fi and bluetooth for indoor localization. In: Proc. of MLBS, pp. 63–66. ACM, New York (2011)CrossRefGoogle Scholar
  13. 13.
    Yeh, L.-W., Hsu, M.-S., Lee, Y.-F., Tseng, Y.-C.: Indoor localization: Automatically constructing today’s radio map by irobot and rfids. In: Proc. of Sensors, pp. 1463–1466 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yao Zhou
    • 1
  • Leilei Jin
    • 1
  • Cheqing Jin
    • 1
  • Aoying Zhou
    • 1
  1. 1.Shanghai Key Laboratory of Trust Worthy Computing, Software Engineering InstituteEast China Normal UniversityShanghaiChina

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