Characterizing Mobile Telephony Signals in Indoor Environments for Their Use in Fingerprinting-Based User Location

  • Alicia Rodriguez-Carrion
  • Celeste Campo
  • Carlos Garcia-Rubio
  • Estrella Garcia-Lozano
  • Alberto Cortés-Martín
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8276)


Fingerprinting techniques have been applied to locate users in indoor scenarios using WiFi signals. Although mobile telephony network is used for outdoor location, it is widely deployed and their signal more stable, thus being also a candidate to be used for fingerprinting. This paper describes the characterization of GSM/UMTS signals in indoor scenarios to check if their features allow to use them for constructing the radio maps needed for fingerprinting purposes. We have developed an Android application to collect the received signal information, such that makes the measurement process cheaper and easier. Measurements show that changes in location and device orientation can be identified by observing the received signal strength of the connected and neighboring base stations. Besides, detecting this variability is easier by using the GSM network than with UMTS technology. Therefore mobile telephony network seems suitable to perform fingerprinting-based indoor location.


fingerprinting indoor location mobile device-based location GSM UMTS 


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  1. 1.
    Bejuri, W., Mohamad, M., Sapri, M.: Ubiquitous Positioning: A Taxonomy for Location Determination on Mobile Navigation System. Signal & Image Processing: An International Journal (SIPIJ) 2(1), 24–34 (2011)Google Scholar
  2. 2.
    Honkavirta, V., Perälä, T., Ali-Löytty, S., Piché, R.: A Comparative Survey of WLAN Location Fingerprinting Methods. In: 6th Workshop on Pos., Nav. and Comm. (WPNC 2009), pp. 243–251 (2009)Google Scholar
  3. 3.
    Sanchez, D., Quinteiro, J.M., Hernandez-Morera, P., Martel-Jordan, E.: Using data mining and fingerprinting extension with device orientation information for WLAN efficient indoor location estimation. In: 2012 IEEE 8th Int. Conf. on Wireless and Mob. Comp., Netw. and Comm (WiMob 2012), pp. 77–83 (2012)Google Scholar
  4. 4.
    Ibrahim, M., Youssef, M.: CellSense: A Probabilistic RSSI-Based GSM Positioning System. In: 2010 IEEE Global Telecomm. Conf (GLOBECOM 2010), pp. 1–5 (2010)Google Scholar
  5. 5.
    Meng, W., Xiao, W., Ni, W., Xie, L.: Secure and robust Wi-Fi fingerprinting indoor localization. In: 2011 Int. Conf. on Indoor Pos. and Indoor Nav. (IPIN 2011), pp. 1–7 (2011)Google Scholar
  6. 6.
    Steiner, C., Wittneben, A.: Low Complexity Location Fingerprinting With Generalized UWB Energy Detection Receivers. IEEE Trans. on Signal Proc. 58(3), 1756–1767 (2010)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Zhou, M., Krishnamurthy, P., Xu, Y., Ma, L.: Physical Distance vs. Signal Distance: An Analysis towards Better Location Fingerprinting. In: 2011 IEEE 13th Int. Conf. on High Perform. Comp. and Comm. (HPCC 2011), pp. 977–982 (2011)Google Scholar
  8. 8.
    Alsindi, N., Chaloupka, Z., Aweya, J.: Entropy-based location fingerprinting for WLAN systems. In: 2012 Int. Conf. on Indoor Pos. and Indoor Nav (IPIN 2012), pp. 1–7 (2012)Google Scholar
  9. 9.
    Fang, S., Wang, C.: A Dynamic Hybrid Projection Approach for Improved Wi-Fi Location Fingerprinting. IEEE Trans. on Vehicular Tech. 60(3), 1037–1044 (2011)CrossRefGoogle Scholar
  10. 10.
    Khanbashi, N.A., Alsindi, N., Al-Araji, S., Ali, N., Aweya, J.: Performance evaluation of CIR based location fingerprinting. In: 2012 IEEE 23rd Int. Symp. on Personal Indoor and Mobile Radio Comm (PIMRC 2012), pp. 2466–2471 (2012)Google Scholar
  11. 11.
    Arya, A., Godlewski, P., Mellé, P.: A Hierarchical Clustering Technique for Radio Map Compression in Location Fingerprinting Systems. In: 2010 IEEE 71st Vehicular Tech. Conf. (VTC 2010), pp. 1–5 (Spring 2010)Google Scholar
  12. 12.
    Shih, C., Chen, L., Chen, G., Wu, E.H.-K., Jin, M.: Intelligent radio map management for future WLAN indoor location fingerprinting. In: 2012 IEEE Wireless Comm. and Netw. Conf. (WCNC 2012), pp. 2769–2773 (2012)Google Scholar
  13. 13.
    Jiang, X., Liu, Y., Wang, X.: An Enhanced Location Estimation Approach Based on Fingerprinting Technique. In: 2010 Int. Conf. on Comm. and Mob. Comp (CMC 2010), vol. 3, pp. 424–427 (2010)Google Scholar
  14. 14.
    Ni, W., Xiao, W., Toh, Y.K., Tham, C.K.: Fingerprint-MDS based algorithm for indoor wireless localization. In: 2010 IEEE 21st Int. Symp. on Personal Indoor and Mob. Radio Comm. (PIMRC 2010), pp. 1972–1977 (2010)Google Scholar
  15. 15.
    Kim, Y., Chon, Y., Cha, H.: Smartphone-Based Collaborative and Autonomous Radio Fingerprinting. IEEE Trans. on Systems, Man, and Cybernetics, Part C: Applications and Reviews 42(1), 112–122 (2012)CrossRefGoogle Scholar
  16. 16.
    Koweerawong, C., Wipusitwarakun, K., Kaemarungsi, K.: Indoor localization improvement via adaptive RSS fingerprinting database. In: 2013 Int. Conf. on Information Netw. (ICOIN 2013), pp. 412–416 (2013)Google Scholar
  17. 17.
    Kjærgaard, M.B.: Indoor location fingerprinting with heterogeneous clients. Perv. Mob. Comp. 7(1), 31–43 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Alicia Rodriguez-Carrion
    • 1
  • Celeste Campo
    • 1
  • Carlos Garcia-Rubio
    • 1
  • Estrella Garcia-Lozano
    • 1
  • Alberto Cortés-Martín
    • 1
  1. 1.Department of Telematic EngineeringUniversity Carlos III of MadridLeganés, MadridSpain

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