Mobile User Location Tracking with Unreliable Data

  • Samia ZouaouiEmail author
  • Abdelmalik BachirEmail author
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 1)


We present an architecture of a location tracking system based on the deployment of completely passive monitors that capture WiFi messages transmitted by mobile users for reasons other than localization, such as those messages transmitted for connectivity management (e.g. probe request messages). Being completely passive, our system has the main advantage of being potentially able to track any WiFi equipped devices without the device contributing to the tracking or even being aware of it. However, the feature of complete passivity comes with the challenge of getting accurate localization at regular time intervals, because some devices may not transmit any WiFi message if they are not being actively used. In addition, some messages transmitted by the device might not be captured by monitors due to many reasons such as collision with another message, temporarily changing channel conditions, or software glitches due to the driver of capturing system. The missing of those messages affects the location accuracy of our tracking system because sometimes, the system has to rely on messages captured by less than three monitors. Therefore, we present two techniques to compensate for that missing data by estimating the current position of the user based on its previous positions. The first technique is called Direction and it targets selecting the most probable current position that minimizes the direction change compared to the past positions. The second method is called Speed; it takes as the most probable position the one that leads to the least speed change compared to previous speeds. Both Direction and Speed are inspired from the assumption that humans tend not to make abrupt changes in their speeds and directions while moving under normal circumstances. We evaluate our proposed techniques in comparison with Dead Reckoning technique by simulation with computer generated mobility data and with real mobility data from the CRAWDAD project. By using NS3 we evaluated our techniques with log-normal and indoor propagation models. NS3 simulations on both log-normal and indoor propagation models show that both methods can lead to satisfactory results and missing of data can be compensated by the proposed heuristics.


Location tracking Trilateration WiFi NS3 simulation 



This work is funded in part by the Algerian Ministry of Higher Education and Scientific Research under contract B*01420130132. We gratefully acknowledge the use of wireless data from the CRAWDAD archive at Dartmouth College.


  1. 1.
    Larson, J.S., Bradlow, E.T., Fader, P.S.: An exploratory look at supermarket shopping paths. Int. J. Res. Mark. 22(4), 395–414 (2005)Google Scholar
  2. 2.
    Schiller, J., Voisard, A.: Location-Based Services. Elsevier (2004)Google Scholar
  3. 3.
    Lasla, N., Younis, M.F., Ouadjaout, A., Badache, N.: An effective area-based localization algorithm for wireless networks. IEEE Trans. Comput. 64(8), 2103–2118 (2015)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Zhang, D., Xia, F., Yang, Z., Yao, L., Zhao, W.: Localization technologies for indoor human tracking. In: 2010 5th International Conference on Future Information Technology (FutureTech), pp. 1–6. IEEE (2010)Google Scholar
  5. 5.
    IEEE standard for information technology–telecommunications and information exchange between systems local and metropolitan area networks–specific requirements Part 11: wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications. IEEE Std 802.11-2012 (Revision of IEEE Std 802.11-2007), pp. 1–2793, March (2012)Google Scholar
  6. 6.
    Cisco Systems. Location tracking approaches. In: Wi-Fi Location-Based Services 4.1 Design Guide. Cisco Systems (2008)Google Scholar
  7. 7.
    Chuan-Chin, P., Chuan-Hsian, P., Lee, H.-J.: Indoor location tracking using received signal strength indicator. In: Emerging Communications for Wireless Sensor Networks. InTech (2011)Google Scholar
  8. 8.
    Guvenc, I., Abdallah, C.T., Jordan, R., Dedeoglu, O.: Enhancements to rss based indoor tracking systems using kalman filters (2003)Google Scholar
  9. 9.
    Lasla, N.: Toward an efficient localization system for wireless sensor networks. PhD Thesis, USTHB (2015)Google Scholar
  10. 10.
    Rashid, H., Turuk, A.K.: Dead reckoning localisation technique for mobile wireless sensor networks. IET Wirel. Sens. Syst. 5(2), 87–96 (2015)CrossRefGoogle Scholar
  11. 11.
    Bezouts theorem. Accessed 28 Jan 2016
  12. 12.
    NS3 project. Accessed 15 July 2015
  13. 13.
    Rampfl, S.: Network simulation and its limitations. In: Proceeding zum Seminar Future Internet, Innovative Internet Technologien und Mobilkommunikation und Autonomous Communication Networks, vol. 57 (2013)Google Scholar
  14. 14.
    Parasuraman, R., Caccamo, S., Baberg, F., Ogren, P.: CRAWDAD dataset kth/rss (v. 2016-01-05) (2016).
  15. 15.
    King, T., Kopf, S., Haenselmann, T., Lubberger, C., Effelsberg, W.: CRAWDAD dataset mannheim/compass (v. 2008-04-11) (2008).
  16. 16.
    Panayiotou, C., Laoudias, C., Piche, R.: Device self-calibration in location systems using signal strength histograms. J. Location Bas. Serv. 7(3), 165–181 (2013). KIOS WiFi RSS dataset (PDF Download Available). doi: 10.1080/17489725.2013.816792. Accessed 6 Feb 2016Google Scholar
  17. 17.
    Bauer, K., Anderson, E.W., McCoy, D., Grunwald, D., Sicker, D.C.: CRAWDAD dataset cu/rssi (v. 2009-05-28) (2009).

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.LINFI Laboratory, Department of Computer ScienceUniversity of BiskraBiskraAlgeria
  2. 2.LMA Laboratory, Department of MathematicsUniversity of BiskraBiskraAlgeria

Personalised recommendations