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http://dx.doi.org/10.7840/kics.2017.42.2.536

Wifi Fingerprint Calibration Using Semi-Supervised Self Organizing Map  

Thai, Quang Tung (Electronics and Telecommunication Research Institute)
Chung, Ki-Sook (Electronics and Telecommunication Research Institute)
Keum, Changsup (Electronics and Telecommunication Research Institute)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.42, no.2, 2017 , pp. 536-544 More about this Journal
Abstract
Wireless RSSI (Received Signal Strength Indication) fingerprinting is one of the most popular methods for indoor positioning as it provides reasonable accuracy while being able to exploit existing wireless infrastructure. However, the process of radio map construction (aka fingerprint calibration) is laborious and time consuming as precise physical coordinates and wireless signals have to be measured at multiple locations of target environment. This paper proposes a method to build the map from a combination of RSSIs without location information collected in a crowdsourcing fashion, and a handful of labeled RSSIs using a semi-supervised self organizing map learning algorithm. Experiment on simulated data shows promising results as the method is able to recover the full map effectively with only 1% RSSI samples from the fingerprint database.
Keywords
Indoor positioning; fingerprint calibration; self organizing map; RSSI;
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1 H. Liu, S. Member, H. Darabi, P. Banerjee, and J. Liu, "Survey of wireless indoor positioning techniques and systems," vol. 37, no. 6, pp. 1067-1080, 2007.   DOI
2 G. Kim, I. Park, Y. I m, A. Hong, J. Kim, and Y. Shin, "Recent trends in location-based services," J. KICS, vol 28, no. 7, pp. 3-14, Jun. 2011.
3 S. He, S. Member, S. G. Chan, and S. Member, "Wi-Fi fingerprint-based indoor positioning : Recent advances and comparisons," IEEE Commun. Surveys & Tuts., vol. 18, no. 1, pp. 466-490, 2016.   DOI
4 X. Chai and Q. Yang, "Reducing the calibration effort for probabilistic indoor location estimation," IEEE Trans. Mob. Comput., vol. 6, no. 6, pp. 649-662, 2007.   DOI
5 M. D. Redzic, C. Brennan, and N. E. O. Connor, "SEAMLOC : Seamless indoor localization based on reduced number of calibration points," IEEE Trans. Mob. Comput., vol. 13, no. 6, pp. 1326-1337, 2014.   DOI
6 T. Pulkkinen, T. Roos, and P. Myllymaki, "Semi-supervised learning for WLAN positioning," Artif. Neural Netw. Mach. Learn., vol. 6791, pp. 355-362, 2011.
7 J. Niu, et al., "WicLoc: an indoor localization system based on WiFi fingerprints and crowdsourcing," in Proc. ICC '15, pp. 3008-3013, Jun. 2015.
8 M. Alzantot, "CrowdInside : Automatic construction of indoor floorplans," in Proc. Sigspatial Gis, pp. 99-108, Redondo Beach, California, Nov. 2012.
9 A. Rai, K. K. Chintalapudi, V. N. Padmanabhan, and R. Sen, "Zee: Zero-Effort crowdsourcing for indoor localization," in Proc. Mobicom '12, p. 293, 2012.
10 H. Abdelnasser, R. Mohamed, A. Elgohary, M. Farid, H. Wang, S. Sen, R. Choudhury, and M. Youssef, "SemanticSLAM: Using environment landmarks for unsupervised indoor localization," IEEE Trans. Mob. Comput., vol. 15, no. 7, pp. 1770-1782, Sept. 2015.   DOI
11 T. Kohonen, "Self-organized formation of topologically correct feature maps," Biological Cybernetics, vol. 43, no. 1, pp. 59-69, 1982.   DOI