Supervised Learning-Based Indoor Positioning System Using WiFi Fingerprints

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.