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Indoor Cooperative Positioning Based on Fingerprinting and Support Vector Machines

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Abstract

For location in indoor environments, the fingerprinting technique seems the most attractive one. It gives higher localization accuracy than the parametric technique because of the existence of multipath propagation and fast fading phenomena that are difficult to model. This paper introduces a novel positioning system based on wireless the IEEE802.15.4/ZigBee standard and employs Support Vector Machines (SVMs). The system is cost-effective since it works with real deployed IEEE 802.15.4/ZigBeeTM sensors nodes. The whole system requires minimal setup time, which makes it readily available for real-world applications. The resulting algorithm demonstrates a superior performance compared to the conventional algorithms.

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References

  1. Holger, K., Willig, A.: Protocols and Architecture for Wireless Sensor Networks. John Wiley and Sons, Chichester (2005)

    Google Scholar 

  2. Chehri, A., Fortier, P., Tardif, P.-M.: UWB-based Sensor Networks for Localization In Mining Environments. Elsevier Journal Ad Hoc Networks (October 2008)

    Google Scholar 

  3. Bulusu, N., Heidemann, J., Estrin, D.: GPS-less Low Cost Outdoor Localization for Very Small Devices. IEEE Personal Communications Magazine 7(5), 28–34 (2000)

    Article  Google Scholar 

  4. Priyantha, N.B., Chakraborty, A., Balakrishnan, H.: The Cricket location-support system. In: The 6th Annual ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom 2000), Boston, MA, USA, pp. 32–43 (2000)

    Google Scholar 

  5. Paramvir, B., Padmanabhan, B.V.: RADAR: An In Building RF-based User Location and Tracking System. In: Proceedings of IEEE Infocom 2000, Tel Aviv (March 2000)

    Google Scholar 

  6. Nerguizian, C., Despins, C., Affes, A.: Geolocation in mines with an impulse response fingerprinting technique and neural networks. IEEE Transactions on Wireless Comm. 5(2), 603–611 (2006)

    Article  Google Scholar 

  7. Wassi, G.I., Grenier, D., Despins, C., Nerguizian, C.: Radiolocation Using Fingerprinting Technique in an Underground Mining Environment. In: 1st International Workshop on Wireless Comm. In Underground and Confined Areas, Val-d’Or, Canada, pp. 163–166 (June 2005)

    Google Scholar 

  8. Dayek, S., Affes, S., Kandil, N., Nerguizian, C.: Cooperative Localization in Mines Using Fingerprinting and Neural Networks. In: Proc. of IEEE WCNC 2010, Sydney, Australia, April 18-21 (2010)

    Google Scholar 

  9. Pahlavan, K., Li, X.: Indoor Geolocation Science and Technogy. IEEE Communications Magazine (February 2002)

    Google Scholar 

  10. Castro, P., Chiu, P., Kremenek, T., Muntz, R.: A probabilistic room location service for wireless networked environments. In: Abowd, G.D., Brumitt, B., Shafer, S. (eds.) UbiComp 2001. LNCS, vol. 2201, pp. 18–34. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  11. Ekahau, http://www.ekahau.com/

  12. Youssef, M., Agrawala, A., Shankar, A.: WLAN location determination via clustering and probability distributions. In: IEEE International Conference on Pervasive Computingand Communications (PerCom), pp. 143–150 (2003)

    Google Scholar 

  13. Gwon, Y., Jain, R., Kawahara, T.: Robust indoor location estimation of stationary and mobile users. In: Proc. of the 23rd Annual Joint Conference of the IEEE Computer and Communication Societies (INFOCOM 2004), pp. 1032–1043 (2004)

    Google Scholar 

  14. Pandya, D., Jain, R., Lupu, E.: Indoor location using multiple wireless technologies. In: Proc. IEEE PIMRC, pp. 2208–2212 (2003)

    Google Scholar 

  15. Taok, A., Kandil, N., Affes, S.: Neural Networks for Fingerprinting-based Indoor Localization Using Ultra-Wideband. Journal of Communications 4(4) (2009)

    Google Scholar 

  16. http://www.silabs.com

  17. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Berlin (1995)

    Book  MATH  Google Scholar 

  18. Suykens, J.A.K., Van Gestel, T., De Brabanter, J., De Moore, B., Vandewalle, J.: Least Squares Support Vector Machines. World Scientific, Singapore (2002)

    Book  MATH  Google Scholar 

  19. Hatami, A.: Application of Channel Modeling for Indoor Localization Using TOA and RSS. PhD thesis, Worcester polytechnic institute (2006)

    Google Scholar 

  20. Kaemarungsi, K.: Design of indoor positioning systems based on location fingerprinting technique. Ph.D. dissertation, University of Pittsburgh, Pittsburgh (2005)

    Google Scholar 

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© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering

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Chehri, A., Mouftah, H., Farjow, W. (2012). Indoor Cooperative Positioning Based on Fingerprinting and Support Vector Machines. In: Sénac, P., Ott, M., Seneviratne, A. (eds) Mobile and Ubiquitous Systems: Computing, Networking, and Services. MobiQuitous 2010. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 73. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29154-8_10

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  • DOI: https://doi.org/10.1007/978-3-642-29154-8_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29153-1

  • Online ISBN: 978-3-642-29154-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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