Vol. 42,  No. 2, pp. 536-544, Feb.  2017


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  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.

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  Cite this article

[IEEE Style]

C. Keum, Ki-Sook Chung, Changsup Keum, "," The Journal of Korean Institute of Communications and Information Sciences, vol. 42, no. 2, pp. 536-544, 2017. DOI: .

[ACM Style]

Changsup Keum, Ki-Sook Chung, and Changsup Keum. 2017. . The Journal of Korean Institute of Communications and Information Sciences, 42, 2, (2017), 536-544. DOI: .

[KICS Style]

Changsup Keum, Ki-Sook Chung, Changsup Keum, "," The Journal of Korean Institute of Communications and Information Sciences, vol. 42, no. 2, pp. 536-544, 2. 2017.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880