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Open AccessArticle

Indoor Crowd 3D Localization in Big Buildings from Wi-Fi Access Anonymous Data

1
Faculty of Computer Science and Management, Wroclaw University of Science and Technology, 50-370 Wrocław, Poland
2
Computational Intelligence Group, Computer Science Faculty, University of the Basque Country, UPV/EHU, 00685 San Sebastián, Spain
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(19), 4211; https://doi.org/10.3390/s19194211
Received: 6 July 2019 / Revised: 15 September 2019 / Accepted: 24 September 2019 / Published: 27 September 2019
(This article belongs to the Special Issue Computational Intelligence in Remote Sensing)
Indoor crowd localization and counting in big public buildings pose problems of infrastructure deployment, signal processing, and privacy. Conventional approaches based on optical cameras, either in the visible or infrared range, received signal strength in wireless networks, sound or chemical sensing in sensor networks need careful calibration, noise removal, and sophisticated data processing to achieve results in limited scenarios. Moreover, personal data protection is a growing concern, so that detection methods that preserve the privacy of people are highly desirable. The aim of this paper is to provide a technique that may generate estimations of the localization of people in a big public building using anonymous data from already-deployed Wi-Fi infrastructure. We present a method applying geostatistical techniques to the access data acquired from Access Points (AP) in an open Wi-Fi network. Specifically, only the time series of the number of accesses per AP is required. Geostatistical methods produce a 3D high-quality spatial distribution representation of the people inside the building based on the interaction of their mobile devices with the APs. We report encouraging results obtained from data acquired at a building of Wroclaw University of Science and Technology. View Full-Text
Keywords: remote sensing; indoor crowd detection; geostatistical methods; Wi-Fi sensors; wireless sensor network remote sensing; indoor crowd detection; geostatistical methods; Wi-Fi sensors; wireless sensor network
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MDPI and ACS Style

Kamińska-Chuchmała, A.; Graña, M. Indoor Crowd 3D Localization in Big Buildings from Wi-Fi Access Anonymous Data. Sensors 2019, 19, 4211. https://doi.org/10.3390/s19194211

AMA Style

Kamińska-Chuchmała A, Graña M. Indoor Crowd 3D Localization in Big Buildings from Wi-Fi Access Anonymous Data. Sensors. 2019; 19(19):4211. https://doi.org/10.3390/s19194211

Chicago/Turabian Style

Kamińska-Chuchmała, Anna; Graña, Manuel. 2019. "Indoor Crowd 3D Localization in Big Buildings from Wi-Fi Access Anonymous Data" Sensors 19, no. 19: 4211. https://doi.org/10.3390/s19194211

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