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Experimental Comparison of Bluetooth and WiFi Signal Propagation for Indoor Localisation

  • Desislava C. Dimitrova
  • Islam Alyafawi
  • Torsten Braun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7277)

Abstract

Systems for indoor positioning using radio technologies are largely studied due to their convenience and the market opportunities they offer. The positioning algorithms typically derive geographic coordinates from observed radio signals and hence good understanding of the indoor radio channel is required. In this paper we investigate several factors that affect signal propagation indoors for both Bluetooth and WiFi. Our goal is to investigate which factors can be disregarded and which should be considered in the development of a positioning algorithm. Our results show that technical factors such as device characteristics have smaller impact on the signal than multipath propagation. Moreover, we show that propagation conditions differ in each direction. We also noticed that WiFi and Bluetooth, despite operating in the same radio band, do not at all times exhibit the same behaviour.

Keywords

Sensor Node Probability Density Function Receive Signal Strength Indicator Anchor Node Multipath Propagation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Ahmed, I., Orfali, S., Khattab, T., Mohamed, A.: Characterization of the indoor-outdoor radio propagation channel at 2.4 ghz. In: 2011 IEEE GCC Conference and Exhibition (GCC), pp. 605–608 (February 2011)Google Scholar
  2. 2.
    Akl, R., Tummala, D., Li, X.: Indoor propagation modeling at 2.4 ghz for IEEE 802.11 networks. In: Sixth IASTED International Multi-Conference on Wireless and Optical Communications. IASTED/ACTA Press (2006)Google Scholar
  3. 3.
    Bargh, M.S., de Groote, R.: Indoor localization based on response rate of Bluetooth inquiries. In: Proc. of 1st ACM International Workshop on Mobile Entity Localization and Tracking in GPS-less Environments, MELT 2008, pp. 49–54. ACM (2008)Google Scholar
  4. 4.
    Bose, A., Foh, C.H.: A practical path loss model for indoor wifi positioning enhancement. In: 2007 6th International Conference on Information, Communications Signal Processing, pp. 1–5 (2007)Google Scholar
  5. 5.
    Byoung-Suk, C., Joon-Woo, L., Ju-Jang, L., Kyoung-Taik, P.: Distributed sensor network based on RFID system for localization of multiple mobile agents. In: Wireless Sensor Network, vol. 3-1, pp. 1–9. Scientific Research (2011)Google Scholar
  6. 6.
    Cherukuri, J.: Comparative study of stochastic indoor propagation models. Technical report, The University of North Carolina at Charlotte (2004)Google Scholar
  7. 7.
    Ciurana, M., Barceló-Arroyo, F., Cugno, S.: A robust to multi-path ranging technique over IEEE 802.11 networks. Wireless Networks 16, 943–953 (2010)CrossRefGoogle Scholar
  8. 8.
    Fang, S.-H., Lin, T.-N.: Projection-based location system via multiple discriminant analysis in wireless local area networks. IEEE Transactions on Vehicular Technology 58(9), 5009–5019 (2009)CrossRefGoogle Scholar
  9. 9.
    Fuchs, C., Aschenbruck, N., Martini, P., Wieneke, M.: Indoor tracking for mission critical scenarios: A survey. Pervasive Mobile Computing 7, 1–15 (2011)CrossRefGoogle Scholar
  10. 10.
    Gezici, S., Zhi, T., Giannakis, G.B., Kobayashi, H., Molisch, A.F., Poor, H.V., Sahinoglu, Z.: Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks. IEEE Signal Processing Magazine 22(4), 70–84 (2005)CrossRefGoogle Scholar
  11. 11.
    Gwon, Y., et al.: Robust indoor location estimation of stationary and mobile users (2004)Google Scholar
  12. 12.
    Haeberlen, A., Flannery, E., Ladd, A.M., Rudys, A., Wallach, D.S., Kavraki, L.E.: Practical robust localization over large-scale 802.11 wireless networks. In: Proc. of 10th Annual International Conference on Mobile Computing and Networking, MobiCom 2004, pp. 70–84. ACM (2004)Google Scholar
  13. 13.
    Hashemi, H.: The indoor radio propagation channel. Proceedings of the IEEE 81(7), 943–968 (1993)CrossRefGoogle Scholar
  14. 14.
    Hay, S., Harle, R.: Bluetooth Tracking without Discoverability. In: Choudhury, T., Quigley, A., Strang, T., Suginuma, K. (eds.) LoCA 2009. LNCS, vol. 5561, pp. 120–137. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  15. 15.
  16. 16.
  17. 17.
    Kotanen, A., Hannikainen, M., Leppakoski, H., Hamalainen, T.D.: Experiments on local positioning with bluetooth. In: International Conference on Information Technology: Coding and Computing [Computers and Communications], pp. 297–303 (2003)Google Scholar
  18. 18.
    Liu, H., Darabi, H., Banerjee, P.: A new rapid sensor deployment approach for first responders. Intelligent Control and Systems 10(2), 131–142 (2005)Google Scholar
  19. 19.
    Mahtab Hossain, A.K.M., Nguyen Van, H., Jin, Y., Soh, W.S.: Indoor localization using multiple wireless technologies. In: Proc. of Mobile Adhoc and Sensor Systems, MASS 2007, pp. 1–8 (2007)Google Scholar
  20. 20.
    Martin-Escalona, I., Barcelo-Arroyo, F.: A new time-based algorithm for positioning mobile terminals in wireless networks. Journal on Advances in Signal Processing, EURASIP (2008)Google Scholar
  21. 21.
    Perez-Vega, C., Garcia, J.L., Lopez Higuera, J.M.: A simple and efficient model for indoor path-loss prediction, vol. 8, p. 1166 (1997)Google Scholar
  22. 22.
    Want, R., Hopper, A., Falcão, V., Gibbons, J.: The active badge location system. ACM Trans. Inf. Syst. 10, 91–102 (1992)CrossRefGoogle Scholar
  23. 23.
    Zhang, G., Krishnan, S., Chin, F., Ko, C.C.: UWB multicell indoor localization experiment system with adaptive TDOA combination. In: IEEE 68th Vehicular Technology Conference, VTC 2008-Fall, pp. 1–5 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Desislava C. Dimitrova
    • 1
  • Islam Alyafawi
    • 1
  • Torsten Braun
    • 1
  1. 1.University of BernSwitzerland

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