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Sensors 2018, 18(7), 2202; https://doi.org/10.3390/s18072202

On-Device Learning of Indoor Location for WiFi Fingerprint Approach

1
Information Technology Department, Universidad Politécnica de Victoria, Ciudad Victoria 87130, Mexico
2
Information Technology Laboratory, Cinvestav-Tamaulipas, Ciudad Victoria 87130, Mexico
3
Cátedras CONACYT, Autonomous University of Tamaulipas, Ciudad Victoria 87000, Mexico
*
Authors to whom correspondence should be addressed.
Received: 30 May 2018 / Revised: 12 June 2018 / Accepted: 14 June 2018 / Published: 9 July 2018
(This article belongs to the Section Sensor Networks)
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Abstract

Indoor positioning is a recent technology that has gained interest in industry and academia thanks to the promising results of locating objects, people or robots accurately in indoor environments. One of the utilized technologies is based on algorithms that process the Received Signal Strength Indicator (RSSI) in order to infer location information without previous knowledge of the distribution of the Access Points (APs) in the area of interest. This paper presents the design and implementation of an indoor positioning mobile application, which allows users to capture and build their own RSSI maps by off-line training of a set of selected classifiers and using the models generated to obtain the current indoor location of the target device. In an early experimental and design stage, 59 classifiers were evaluated, using data from proposed indoor scenarios. Then, from the tested classifiers in the early stage, only the top-five classifiers were integrated with the proposed mobile indoor positioning, based on the accuracy obtained for the test scenarios. The proposed indoor application achieves high classification rates, above 89%, for at least 10 different locations in indoor environments, where each location has a minimum separation of 0.5 m. View Full-Text
Keywords: mobile application; classifier; WiFi fingerprint; indoor localization mobile application; classifier; WiFi fingerprint; indoor localization
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Nuño-Maganda, M.A.; Herrera-Rivas, H.; Torres-Huitzil, C.; Marisol Marín-Castro, H.; Coronado-Pérez, Y. On-Device Learning of Indoor Location for WiFi Fingerprint Approach. Sensors 2018, 18, 2202.

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