Indoor Wi-Fi positioning: techniques and systems

  • F. Lassabe
  • P. Canalda
  • P. Chatonnay
  • F. Spies


If outdoor positioning is widely treated and quite precise, positioning indoors or, more generally, in heterogeneous environments, as well as mobility prediction, requires important devices. New wireless technologies (e.g., Wi-Fi, Ultra Wide Band) combine the mobility of terminals with large bandwidth. Terminal mobility is one of the major pillars of applications attempting to become context-aware, and a large bandwidth enables new services such as multimedia contents streaming towards mobile terminals. Being context-aware and able to provide services in a mobile environment requires the knowledge of spatial and temporal data about the terminal. The key phase in the achievement of mobility management is the positioning process. We propose a layered positioning system based on a model combining a reference point-based approach with a trilateration-based one. Several layers of refinement are offered based on the knowledge of the topology and devices deployed. The more data are known, the better adapted to its area the positioning system can be.


Indoor positioning Wi-Fi network Friis-based calibrated model Model by refinement Media guide Middleware Multimedia platform 


  1. 1.
    Anwar AK, Ioannis G, Pavlidou FN, (2008) Evaluation of indoor location based on combination of AGPS/HSGPS. In: Procs of 3rd int symp on wireless pervasive computing (ISWPC08), pp 383–387Google Scholar
  2. 2.
    Bahl P, Balachandran A, Padmanabhan V (2000) Enhancements to the radar user location and tracking system. Technical reportGoogle Scholar
  3. 3.
    Brunato M, Kalló CK (2002) Transparent location fingerprinting for wireless services. Technical report, University of Trento. In: Proceedings of Med-Hoc-Net, Mediterranean wokshop on ad-hoc networks, Baia Chia, CagliariGoogle Scholar
  4. 4.
    Bourgeois J, Mory E, Spies F (2003) Video transmission adaptation on mobile devices. J Systems Archit 49:475–484CrossRefGoogle Scholar
  5. 5.
    Bahl P, Padmanabhan VN (2000) RADAR: an in-building RF-based user location and tracking system. In: INFOCOM (2), pp 775–784Google Scholar
  6. 6.
    Castro P, Chiu P, Kremenek T, Muntz R (2001) A probabilistic room location service for wireless networked environments. In: Proceedings of the 3rd international conference on Ubiquitous Computing, Ubicomp 2001, vol 2201, pp 18–34Google Scholar
  7. 7.
    Caratori J, François M, Samama N, Vervisch-Picois A (2004) Upgrade RnS indoor positioning system in an office building. In: Proceedings of ION GNSS 2004, pp 1959–1979Google Scholar
  8. 8.
    Charlet D, Lassabe F, Canalda P, Chatonnay P, Spies F (2006) Mobility prediction for multimedia services. In: Ibrahim IK, Johannes Kepler University Linz (eds) Handbook of research in mobile multimedia, chapter 33. Idea Group Inc, pp 491–506. ISBN: 1591408660Google Scholar
  9. 9.
    Crow BP, Widjaja I, Kim JG, Sakai P (1997) IEEE 802.11 wireless local area networks. IEEE Commun Mag 35(9):116–126CrossRefGoogle Scholar
  10. 10.
    Deblauwe N (2008) GSM-based positioning: techniques and applications. PhD thesis, Vrije Universiteit BrusselsGoogle Scholar
  11. 11.
    Eissfeller B, Gnsch D, Mller S, Teuber A (2004) Indoor positioning using wireless LAN radio signals. In: Proceedings of ION GNSS 17th international technical meeting of the satellite divisionGoogle Scholar
  12. 12.
    Evennou F (2007) Techniques et technologies de localisation avancées pour terminaux mobiles dans les environnements indoor. PhD thesis, Universite Joseph Fourier - Grenoble IGoogle Scholar
  13. 13.
    Fang BT (1986) Trilateration and extension to global positioning system navigation. J Guid Control Dyn 9:715–717CrossRefGoogle Scholar
  14. 14.
    Fukumoto M, Shinagawa M (2005) Carpetlan: a novel indoor wireless(-like) networking and positioning system. In: Proceedings of the 7th international conference on Ubiquitous Computing (UbiComp 2005), pp 1–18Google Scholar
  15. 15.
    Interlink Networks, Inc (2002) A practical approach to identifying and tracking unauthorized 802.11 cards and access points. Technical reportGoogle Scholar
  16. 16.
    Kusuma J, Maravic I, Vetterli M (2003) Sampling with finite rate of innovation: channel and timing estimation for uwb and gps. In: IEEE international conference on communications (ICC’03)Google Scholar
  17. 17.
    Kubrak D (2007) Hybridisation of a GPS receiver with low-cost sensors for personal positioning in urban environment. PhD thesis, ENST - COMELEC Communication et ElectroniqueGoogle Scholar
  18. 18.
    Mac Larnon B (1998) TAPR’s spread spectrum update, chapter VHF/UHF/microwave radio propagation: a primer for digital experimenters. Tucson Amateur Packet Radio CorporationGoogle Scholar
  19. 19.
    Lassabe F, Baala O, Canalda P, Chatonnay P, Spies F (2005) A friis-based calibrated model for wifi terminals positioning. In: Proceedings of IEEE int symp on a world of wireless, mobile and multimedia networks (WoWMoM 2005)Google Scholar
  20. 20.
    Lassabe F, Canalda P, Charlet D, Chatonnay P, Spies F (2006) Refining WiFi indoor positioning renders pertinent deploying location-based multimedia guide. In: Procs of IEEE int workshop on pervasive computing and ad hoc communications (PCAC06), in conjunction with the IEEE 20th int conf on advanced information networking and applications (AINA06), vol 2, Vienna, pp 126–130Google Scholar
  21. 21.
    Lassabe F, Charlet D, Canalda P, Chatonnay P, Spies F (2006) Predictive mobility models based on Kth Markov models. In: IEEE int conf on pervasive services 2006 (ICPS’06), Lyon, pp 303–306Google Scholar
  22. 22.
    Ladetto Q, Merminod B (2002) Digital magnetic compass and gyroscope integration for pedestrian navigation. In: Proceedings of the 9th saint petersburg international conference on integrated navigation systemsGoogle Scholar
  23. 23.
    Muthukrishnan K, Hazas M (2009) In: Quigley A, Choudhury T (eds) Fourth international symposium on location and context awareness (LoCA 2009), Co-located with Pervasive09, Tokyo, lecture notes in Computer ScienceGoogle Scholar
  24. 24.
    De Nardis L, Di Benedetto M-G (2006) Positioning accuracy in ultra wide band low data rate networks of uncoordinated terminals. In: Proceedings of the IEEE international conference on UWB 2006 (ICUWB2006)Google Scholar
  25. 25.
    Priyantha NB, Chakaborty A, Balakrishnan H (2000) The cricket location-support system. In: Proceedings of the 6th annual acm international conference on mobile computing and networking (MobiCom 2000), pp 32–43Google Scholar
  26. 26.
    Runser K, Jullo E, Gorce J-M (2003) Wireless LAN planning using the multi-resolution FDPF propagation model. In: Proceedings of the 12th international conference on antennas and propagation, vol 1, pp 80–83Google Scholar
  27. 27.
    Roos R, Myllymäki P, Tirri H, Misikangas P, Sievänen J (2002) A probabilistic approach to WLAN user location estimation. Int J Wirel Inf Netw 9(3):155–164CrossRefGoogle Scholar
  28. 28.
    Samama N (2008) Global positioning: technologies and application. Wiley, New YorkGoogle Scholar
  29. 29.
    Smailagic A, Kogan D (2002) Location sensing and privacy in a context-aware computing environment. IEEE Wirel Commun 9(5):10–17CrossRefGoogle Scholar
  30. 30.
    Tauber JA (2002) Indoor location systems for pervasive computing. Massachusetts Institute of Technology. Area exam reportGoogle Scholar
  31. 31.
    US Army Corps of Engineer (ed) (2003) Engineering and design—NAVSTAR global positioning system surveying. Number EM 1110-1-1003. Department of the Army, Washington, DCGoogle Scholar
  32. 32.
    Ward A, Jones A, Harper A (1997) A new location technique for the active office. IEEE Pers Commun 4(5):42–47CrossRefGoogle Scholar
  33. 33.
    Wang Y, Jia X, Lee HK (2003) An indoors wireless positioning system based on wireless local area network infrastructure. In: 6th int symp on satellite navigation technology including mobile positioning & location services, number paper 54, MelbourneGoogle Scholar
  34. 34.
    Youssef MA, Agrawala A, Shankar AU, Noh SH (2002) A probabilistic clustering-based indoor location determination system. Technical report, Maryland Information and Network Dynamics LaboratoryGoogle Scholar

Copyright information

© Institut TELECOM and Springer-Verlag 2009

Authors and Affiliations

  • F. Lassabe
    • 1
  • P. Canalda
    • 1
  • P. Chatonnay
    • 1
  • F. Spies
    • 1
  1. 1.LIFC—Laboratoire d’Informatique de l’Université de Franche-Comté - EA 4269Numérica—Multimedia Developpement CenterMontbéliard CedexFrance

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