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Open AccessArticle

GPS-Based Indoor/Outdoor Detection Scheme Using Machine Learning Techniques

Department of Electronics Engineering, Kookmin University, Seoul 02707, Korea
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(2), 500;
Received: 6 December 2019 / Revised: 4 January 2020 / Accepted: 6 January 2020 / Published: 10 January 2020
(This article belongs to the Special Issue Recent Advances in Indoor Localization Systems and Technologies)
Recent advances in mobile communication require that indoor/outdoor environment information be available for both individual applications and wireless signal transmission in order to improve interference control and serve upper-layer applications. In this paper, we present a scheme to identify the indoor/outdoor environment using GPS signals combined with machine learning classification techniques. Compared to traditional schemes, which are based on received signal strength indicator (RSSI), the proposed scheme promises a robust approach with high accuracy, smooth operation when moving between indoor and outdoor environments, as well as easy implementation and training. The proposed scheme combined information from a certain number of GPS satellites, using the GPS sensor on mobile devices. Then, data are collected, preprocessed, and classified as indoor or outdoor environment using a machine learning model that is optimized for the best performance. The GPS input data were collected in the Kookmin University area and included 850 training samples and 170 test samples. The overall accuracy reached 97%. View Full-Text
Keywords: I/O detection; GPS signal; machine learning; positioning applications. I/O detection; GPS signal; machine learning; positioning applications.
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Bui, V.; Le, N.T.; Vu, T.L.; Nguyen, V.H.; Jang, Y.M. GPS-Based Indoor/Outdoor Detection Scheme Using Machine Learning Techniques. Appl. Sci. 2020, 10, 500.

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