Survey of WLAN Fingerprinting Positioning System

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The increasing demand for indoor location based services has motivated the development of various indoor positioning methods. Among them, fingerprinting based positioning system in wireless local area network (WLAN) has been paid more attentions due to its cost effectiveness and relatively high accuracy. This paper investigates various optimization techniques for WLAN fingerprinting positioning comprehensively. The fingerprinting positioning comprises five major steps: Radio Map construction, location clustering, feature extraction, location estimation and tracking. The optimization techniques in these five steps are discussed and finally the future trends are presented.

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2499-2505

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August 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] M. Chon, and H. Cha, LifeMap: A smartphone-based context provider for location-based services, IEEE Pervasive Computing, vol. 10, no. 2, pp.58-67, (2011).

DOI: 10.1109/mprv.2011.13

Google Scholar

[2] K. Petrova, et al, Location-based services deployment and demand: a roadmap model, Electron Commer Res, vol. 11, p.5–29, (2011).

DOI: 10.1007/s10660-010-9068-7

Google Scholar

[3] H. D. Chon, et al, Using RFID for accurate positioning, Journal of Global Positioning Systems, vo. 3, no. 1-2, pp.32-39, (2004).

Google Scholar

[4] Y. Zhang, W. Liu, Y. Fang, et al, Secure localization and authentication in ultra-wideband sensor networks, IEEE Journal of Selected Areas on Communications, vol. 24, no. 4, pp.829-835, (2006).

DOI: 10.1109/jsac.2005.863855

Google Scholar

[5] L. Wirola, T. A. Laine, and J. Syrjarinne, Mass-market requirements for indoor positioning and indoor navigation, Proceedings of 2010 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp.1-7, September (2010).

DOI: 10.1109/ipin.2010.5646748

Google Scholar

[6] H. Liu, et al, Survey of wireless indoor positioning techniques and systems, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 37, no. 6, pp.1067-1080, (2007).

DOI: 10.1109/tsmcc.2007.905750

Google Scholar

[7] K. Kaemarungsia, and P. Krishnamurthy, Analysis of WLAN's received signal strength indication for indoor location fingerprinting, Pervasive and Mobile Computing, vol. 8, p.292–316, (2012).

DOI: 10.1016/j.pmcj.2011.09.003

Google Scholar

[8] A. Krishnakumar and P. Krishnan, The theory and practice of signal strength-based location estimation, Collaborative Computing: Networking, Applications and Worksharing, (2005).

DOI: 10.1109/colcom.2005.1651253

Google Scholar

[9] J. Prieto, S. Mazuelas, et al, Adaptive data fusion for wireless localization in harsh environments, IEEE Transactions on Signal Processing, vol. 60, no. 41, pp.585-1596, (2012).

DOI: 10.1109/tsp.2012.2183126

Google Scholar

[10] A. Goldsmith, Wireless Communications. Cambridge University Press, (2005).

Google Scholar

[11] X. Chai, Q. Yang, et al, Reducing the calibration effort for probabilistic indoor location estimation, IEEE Transactions on Mobile Computing, vol. 6, no. 6, pp.649-662, (2007).

DOI: 10.1109/tmc.2007.1025

Google Scholar

[12] Z. Zhuang, et al, Improving energy efficiency of location sensing on smartphones, Proc. ACM MobiSys 2010, San Francisco, CA, USA, pp.315-330, (2010).

Google Scholar