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Indoor Location Using Fingerprinting and Fuzzy Logic

  • Conference paper

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 107))

Abstract

Indoor location systems cannot rely on technologies such as GPS (Global Positioning System) to determine the position of a mobile terminal, because its signals are blocked by obstacles such as walls, ceilings, roofs, etc. In such environments the use of alternative techniques, such as the use of wireless networks, should be considered. The location estimation is made by measuring and analysing one of the parameters of the wireless signal, usually the received power. One of the techniques used to estimate the locations using wireless networks is fingerprinting. This technique comprises two phases: in the first phase data is collected from the scenario and stored in a database; the second phase consists in determining the location of the mobile node by comparing the data collected from the wireless transceiver with the data previously stored in the database. In this paper an approach for localisation using fingerprinting based on Fuzzy Logic and pattern searching is presented. The performance of the proposed approach is compared with the performance of classic methods, and it presents an improvement between 10.24% and 49.43%, depending on the mobile node and the Fuzzy Logic parameters.

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References

  1. Audet, C.: Convergence results for pattern search algorithms are tight. Optimization and Engineering 2(5), 101–122 (2004)

    Article  MathSciNet  Google Scholar 

  2. Audet, C., Bchard, V., Digabel, S.L.: Nonsmooth optimization through mesh adaptive direct search and variable neighborhood search. J. Global Opt. (41), 299–318 (2008)

    Article  MATH  Google Scholar 

  3. Audet, C., Dennis Jr., J.E.: Analysis of generalized pattern searches. SIAM Journal on Optimization 13(3), 889–903 (2002)

    Article  MathSciNet  Google Scholar 

  4. Audet, C., Dennis Jr., J.E.: Mesh adaptive direct search algorithms for constrained optimization. SIAM Journal on Optimization (17), 188–217 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  5. Audet, C., Dennis Jr., J.E.: A mads algorithm with a progressive barrier for derivative-free nonlinear programming. Tech. Rep. G-2007-37, Les Cahiers du GERAD, cole Polytechnique de Montral (2007)

    Google Scholar 

  6. Audet, C., Dennis Jr., J.E., Digabel, S.L.: Globalization strategies for mesh adaptative direct search. Tech. Rep. G-2008-74, Les Cahiers du GERAD, cole Polytechnique de Montral (2008)

    Google Scholar 

  7. Bahl, P., Padmanabhan, V.: RADAR: an in-building RF-based user location and tracking system. In: INFOCOM 2000, Proceedings of IEEE Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies, vol. 2, pp. 775–784 (2000); doi:10.1109/INFCOM.2000.832252

    Google Scholar 

  8. Conn, A.R., Scheinberg, K., Vicente, L.N.: Introduction to Derivative-Free Optimization. In: MPS-SIAM Series on Optimization. SIAM, USA (2009)

    Google Scholar 

  9. Cricket Project: Cricket v2 User Manual. MIT Computer Science and Artificial Intelligence Lab, Cambridge, ma 02139 edn, 9-11 (2005)

    Google Scholar 

  10. Hightower, J., Borriello, G.: Location sensing techniques. Tech. rep., University of Washington, Department of Computer Science and Engineering, Seattle (2001)

    Google Scholar 

  11. Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-fuzzy and soft computing. In: USENIX Systems Administration Conference (1997)

    Google Scholar 

  12. Lin, C.T., Lee, C.S.G.: Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems. Prentice-Hall, Inc., Upper Saddle River (1996)

    Google Scholar 

  13. Liu, H., Darabi, H., Banerjee, P., Liu, J.: Survey of wireless indoor positioning techniques and systems. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 37(6), 1067–1080 (2007); doi:10.1109/TSMCC.2007.905750

    Article  Google Scholar 

  14. Mestre, P., Pinto, H., Serodio, C., Monteito, J., Couto, C.: A multi-technology framework for LBS using fingerprinting. In: 35th Annual Conference of IEEE Industrial Electronics, IECON 2009, pp. 2693–2698 (2009)

    Google Scholar 

  15. Orr, R.J., Abowd, G.D.: The smart floor: a mechanism for natural user identification and tracking. In: CHI 2000: Extended Abstracts on Human Factors in Computing Systems, pp. 275–276. ACM, New York (2000), http://doi.acm.org/10.1145/633292.633453

    Chapter  Google Scholar 

  16. Otsason, V., Varshavsky, A., LaMarca, A., de Lara, E.: Accurate GSM indoor location. In: Mobile Computing, Ubi Comp 2005 (2005)

    Google Scholar 

  17. Prasithsangaree, P., Krishnamurthy, P., Chrysanthis, P.: On indoor position location with wireless LANs. In: The 13th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, vol. 2, pp. 720–724 (2002)

    Google Scholar 

  18. Silva, P.M., Paralta, M., Caldeirinha, R., Rodrigues, J., Serodio, C.: Traceme - indoor real-time location system. In: 35th Annual Conference of IEEE Industrial Electronics, IECON 2009, pp. 2721–2725 (2009)

    Google Scholar 

  19. Want, R., Hopper, A., Veronica Falc, A., Gibbons, J.: The active badge location system. ACM Trans. Inf. Syst. 10(1), 91–102 (1992), http://doi.acm.org/10.1145/128756.128759

    Article  Google Scholar 

  20. Ward, A., Jones, A., Hopper, A.: A new location technique for the active office. IEEE Personal Communications 4(5), 42–47 (1997)

    Article  Google Scholar 

  21. Zadeh, L.: Fuzzy sets. Information Control 8, 338–353 (1965)

    Article  MathSciNet  MATH  Google Scholar 

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© 2011 Springer-Verlag Berlin Heidelberg

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Mestre, P. et al. (2011). Indoor Location Using Fingerprinting and Fuzzy Logic. In: Melo-Pinto, P., Couto, P., Serôdio, C., Fodor, J., De Baets, B. (eds) Eurofuse 2011. Advances in Intelligent and Soft Computing, vol 107. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24001-0_33

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  • DOI: https://doi.org/10.1007/978-3-642-24001-0_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24000-3

  • Online ISBN: 978-3-642-24001-0

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