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Title	Supervised Learning-Based Indoor Positioning System Using Wifi Fingerprints
ID_Doc	53644
Authors	Suleiman B.; Anaissi A.; Xiao Y.; Yaqub W.; Raju A.S.; Alyassine W.
Year	2023
Published	Lecture Notes in Networks and Systems, 700 LNNS
DOI	http://dx.doi.org/10.1007/978-3-031-33743-7_5
Abstract	We propose to leverage the WiFi fingerprint of people in confined areas to monitor and manage the mobility of the crowd in a smart city. We transform the indoor positioning problem into a supervised learning problem that takes as an input the WiFi fingerprint of a person and predicts their availability within a confined area. We investigate the accuracy and the granularity of multiple supervised learning methods in the WiFi fingerprint-based indoor positioning. Preliminary experiments show promising results for different granularity levels. 99.88% of balanced accuracy is achieved to predict the availability of a person at the building level, and 88.56% to 93.44% of accuracy is achieved to predict the availability of a person at the floor level. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Author Keywords	Clustering; Data Analysis; Indoor Positioning; Machine Learning; WiFi Fingerprint

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