Mobile Networks and Applications

, Volume 22, Issue 5, pp 825–833 | Cite as

Virtual and Oriented WiFi Fingerprinting Indoor Positioning based on Multi-Wall Multi-Floor Propagation Models



Virtual fingerprints have been proposed in the context of WiFi Fingerprinting Indoor Positioning systems in order to reduce the effort dedicated to offline measurements. In this work, the use of Multi-Wall Multi-Floor indoor propagation models to generate such virtual fingerprints is investigated. A strategy taking into account the impact of user/device orientation on the signal propagation is proposed, leading to the creation of virtual and oriented fingerprints. The work analyzes then the trade-offs between model accuracy and measurement efforts by means of experimental results, showing that good modeling accuracy can be guaranteed while significantly reducing the complexity of the offline measurement phase.


Indoor positioning WiFi fingerprinting Indoor propagation modeling Multi-wall multi-floor 


  1. 1.
    Liu H, Darabi H, Banerjee P, Liu J (2007) Survey of wireless indoor positioning techniques and systems. IEEE Trans Syst Man Cybern C Appl Rev 37(6):1067–1080CrossRefGoogle Scholar
  2. 2.
    Honkavirta V, Perälä T, Ali-Löytty S, Piché R (2009) Comparative survey of WLAN location fingerprinting methods. In: Workshop on positioning, navigation and communication. IEEE Press, New York, pp 243–251Google Scholar
  3. 3.
    Bahl P, Padmanabhan VN (2000) RADAR: An In-building RF-based user location and tracking system. In: IEEE International conference on computer communications, pp. 775–784 (2). IEEE Press, New YorkGoogle Scholar
  4. 4.
    Kessel M, Werner M (2011) SMARTPOS: Accurate And precise indoor positioning on mobile phones. In: International conference on mobile services, resources, and users. IARIA XPS Press, pp 158–163Google Scholar
  5. 5.
    Liao I-E, Kao K-F (2008) Enhancing the accuracy of WLAN-based location determination systems using predicted orientation information. Inform Science 178(4):1049–1068CrossRefGoogle Scholar
  6. 6.
    Hossain A. KMM, Van HN, Jin Y, Soh W-S (2007) Indoor localization using multiple wireless technologies. In: IEEE International conference on mobile adhoc and sensor systems. IEEE Press, New York, pp 1–8Google Scholar
  7. 7.
    Widyawan, Klepal M, Pesch D (2007) Influence of Predicted and Measured Fingerprint on the Accuracy of RSSI-based Indoor Location Systems. In: Workshop on positioning, navigation, and communication. IEEE Press, New York, pp 145–151Google Scholar
  8. 8.
    Chintalapudi K, Iyer AP, Padmanabhan VN (2010) Indoor localization without the pain. In: International conference on mobile computing and networking. ACM Press, New York, pp 173–184Google Scholar
  9. 9.
    Eleryan A, Elsabagh M, Youssef M (2011) Synthetic generation of radio maps for device-free passive localization. In: IEEE Global communications conference. IEEE Press, New York, pp 1–5Google Scholar
  10. 10.
    COST Action 231 (1999) Digital mobile radio towards future generation systems. Technical report, European CommissionGoogle Scholar
  11. 11.
    Borrelli A, Monti C, Vari M, Mazzenga F (2004) Channel models for IEEE 802.11b indoor system design. In: IEEE International conference on communications. IEEE Press, New York, pp 3701–3705Google Scholar
  12. 12.
    Small J, Smailagic A, Siewiorek DP (2000) Determining User Location For Context Aware Computing Through the Use of a Wireless LAN Infrastructure. [Online at]
  13. 13.
    Youssef M, Agrawala A, Udaya Shankar A (2003) WLAN Location determination via clustering and probability distributions. In: IEEE International conference on pervasive computing and communications. IEEE Press, New York, pp 143–151Google Scholar
  14. 14.
    Roos TT, Myllymäki P, Tirri H, Misikangas P, Sievänen J (2002) A Probabilistic Approach to WLAN User Location Estimation. Int J Wireless Inform Network 9(3):155–164CrossRefGoogle Scholar
  15. 15.
    Ladd AM, Bekris KE, Rudys A, Kavraki LE, Wallach DS (2005) Robotics-based location sensing using wireless ethernet. Wirel Netw 11(1-2):189–204CrossRefGoogle Scholar
  16. 16.
    Kaemarungsi K, Krishnamurthy P (2004) Properties of indoor received signal strength for WLAN location fingerprinting. In: IEEE International conference on mobile and ubiquitous systems: Networking and services. IEEE Press, New York, pp 14– 23Google Scholar
  17. 17.
    Li B, Kam J, Lui J, Dempster AG (2007) Use of directional information in wireless LAN based indoor positioning. In: Symposium on GPS/GNSS (IGNSS)Google Scholar
  18. 18.
    Liu H, Yang J, Sidhom S, Wang Y, Chen Y, Ye F (2014) Accurate WiFi Based Localization for Smartphones Using Peer Assistance. IEEE Trans Mobile Comput 13(10):2199–2214CrossRefGoogle Scholar
  19. 19.
    Jekabsons G, Zuravlyov V (2010) Refining Wi-Fi based indoor positioning. In: International scientific conference applied information and communication technologies, pp 87–95Google Scholar
  20. 20.
    Feng C, Au WSA, Valaee S, Tan Z (2012) Received-Signal-Strength-Based Indoor positioning using compressive sensing. IEEE Trans Mobile Comput 11(12):1983–1993CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.DIET DepartmentSapienza University of RomeRomeItaly

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