Skip to main content
SpringerLink
Log in

Performance Analysis of Received Signal Strength Fingerprinting Based Distributed Location Estimation System for Indoor WLAN

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Location Estimation has become important for many applications of indoor wireless networks. Received Signal Strength (RSS) fingerprinting methods have been widely used for location estimation. Most of the location estimation system suffers with the problem of scalability and unavailability of all the access points at all the location for large site. The accuracy and response time of estimation are critical issues in location estimation system for large sites. In this paper, we have proposed a distributed location estimation method, which divide the location estimation system into subsystems. Our method partitions the input signal space and output location space into clusters on the basis of visibility of access points at various locations of the site area. Each cluster of input signal space together with output location subspace is used to learn the association between RSS fingerprint and their respective location in a subsystem. We have performed experimentation on two RSS dataset, which are gathered on different testbeds, and compared our results with benchmark RADAR method. Experimental results show that our method provide better results in terms of accuracy and response time in comparison to centralized systems, in which a single system is used for large site.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Liu H., Darabi H., Banerjee P., Liu J. (2007) Survey of wireless indoor positioning techniques and systems. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 37(6): 1067–1080

    Article  Google Scholar 

  2. Ahmad, U., Gavrilov, A., Lee, S., & Lee, Y. (2008). A modular classification model for received signal strength based location systems. Neuro Computing, 71(13–15), 2669.

    Google Scholar 

  3. Bahl, P., & Padmanabhan, V. (2000). RADAR: An in-building RF-based user location and tracking system. In Proceedings of 19th IEEE annual joint conference of computer and communications societies, INFOCOM 2000 (Vol. 2, pp. 775–784).

  4. Krishnan, P., Krishnakumar, A., Ju, W.H., Mallows, C., & Gamt, S. (2004). A system for lease: location estimation assisted by stationary emitters for indoor rf wireless networks. In Proceedings of 23rd IEEE annual joint conference of computer and communications societies, INFOCOM 2004 (Vol. 2, pp. 1001–1011). doi:10.1109/INFCOM.2004.1356987.

  5. Youssef, M., Agrawala, A., & Udaya Shankar, A. (2003). Wlan location determination via clustering and probability distributions. In Proceedings of the first IEEE international conference on pervasive computing and communications, (PerCom 2003) (pp. 143–150). IEEE.

  6. Michaelides M., Panayiotou C. (2009) Snap: Fault tolerant event location estimation in sensor networks using binary data. IEEE Transactions on Computers 58(9): 1185–1197. doi:10.1109/TC.2009.60

    Article  MathSciNet  Google Scholar 

  7. Bahl, P., Padmanabhan, V., & Balachandran, A. (2000). Enhancements to the RADAR user location and tracking system. Microsoft Research, (MSR-TR-2000-12) pp. 13.

  8. Battiti, R., Nhat, T., & Villani, A. (2002). Location-aware computing: a neural network model for determining location in wireless LANs. Technical Report DIT-02-083, Ingegneria e Scienza dell’Informazione, University of Trento.

  9. Pahlavan K., Li X., Makela J. (2002) Indoor geolocation science and technology. IEEE Communications Magazine 40(2): 112–118. doi:10.1109/35.983917

    Article  Google Scholar 

  10. Youssef M., Agrawala A. (2008) The Horus location determination system. Wireless Networks 14(3): 357–374

    Article  Google Scholar 

  11. Ahmad U., Gavrilov A., Lee Y., Lee S. (2008) Context-aware, self-scaling Fuzzy ArtMap for received signal strength based location systems. Soft Computing-A Fusion of Foundations, Methodologies and Applications 12(7): 699–713

    Google Scholar 

  12. Chen, C. (2005). Hybrid location estimation and tracking system for mobile devices. In Proceedings of 61st IEEE vehicular technology conference, VTC 2005 (Vol. 4, pp. 2648–2652).

  13. Ding, X., Li, H., Li, F., & Wu, J. (2008). A novel infrastructure WLAN locating method based on neural network. In Proceedings of 4th Asian conference on internet engineering (pp. 47–55).

  14. Gupta, A., Tapaswi, & S., Jain, V. (2009). Recurrent grid based voting approach for location estimation in wireless sensor networks. In Proceedings of symposia and workshops on ubiquitous, autonomic and trusted computing, UIC-ATC’09 (pp. 263–267).

  15. Yang B., Xu J., Yang J., Li M. (2010) Localization algorithm in wireless sensor networks based on semi-supervised manifold learning and its application. Cluster Computing 13(4): 435–446

    Article  Google Scholar 

  16. Jain, V., Tapaswi, S., & Shukla, A. (2010). Distributed growing radial basis function neural networks for location estimation in indoor wireless networks. In 6th International conference on wireless communications networking and mobile computing (WiCOM) (pp. 1–6). doi:10.1109/WICOM.2010.5601059.

  17. Hightower J., Borriello G. (2001) Location systems for ubiquitous computing. Computer 34(8): 57–66

    Article  Google Scholar 

  18. Laoudias, C., Michaelides, M., & Panayiotou, C. (2011). Fault tolerant fingerprint-based positioning. In IEEE international conference on communications (ICC) (pp. 1–5). doi:10.1109/icc.2011.5963136.

  19. Qiang Yang Sinno Jialin Pan, V.W.Z. (2007). IEEE ICDM Data Mining Contest 2007. http://www.cse.ust.hk/~qyang/ICDMDMC07/. Accessed 20 April 2011.

Download references

Author information

Authors and Affiliations

  1. Atal Bihari Vajpayee—Indian Institute of Information Technology and Management, Gwalior, M.P., India

    V. K. Jain, Shashikala Tapaswi & Anupam Shukla

Authors
  1. V. K. Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shashikala Tapaswi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anupam Shukla
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shashikala Tapaswi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Jain, V.K., Tapaswi, S. & Shukla, A. Performance Analysis of Received Signal Strength Fingerprinting Based Distributed Location Estimation System for Indoor WLAN. Wireless Pers Commun 70, 113–127 (2013). https://doi.org/10.1007/s11277-012-0682-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-012-0682-7

Keywords

Search

Navigation