WiFi Fingerprint Clustering for Urban Mobility Analysis

• URL: http://arxiv.org/abs/2105.01274v1
• Date: Tue, 4 May 2021 03:46:14 GMT
• Title: WiFi Fingerprint Clustering for Urban Mobility Analysis
• Authors: Sumudu HasalaMarakkalage, Billy Pik Lik Lau, Yuren Zhou, Ran Liu, Chau Yuen, Wei Quin Yow, Keng Hua Chong
• Abstract summary: We present an unsupervised learning approach to identify user points of interest (POI) by exploiting WiFi measurements from smartphone application data. Due to the lack of GPS positioning accuracy in indoor, sheltered, and high rise building environments, we rely on widely available WiFi access points (AP) in contemporary urban areas. We propose a system architecture to scan the surrounding WiFi AP, and perform unsupervised learning to demonstrate that it is possible to identify three major insights.
• Score: 20.190366137684205
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we present an unsupervised learning approach to identify the user points of interest (POI) by exploiting WiFi measurements from smartphone application data. Due to the lack of GPS positioning accuracy in indoor, sheltered, and high rise building environments, we rely on widely available WiFi access points (AP) in contemporary urban areas to accurately identify POI and mobility patterns, by comparing the similarity in the WiFi measurements. We propose a system architecture to scan the surrounding WiFi AP, and perform unsupervised learning to demonstrate that it is possible to identify three major insights, namely the indoor POI within a building, neighbourhood activity, and micro-mobility of the users. Our results show that it is possible to identify the aforementioned insights, with the fusion of WiFi and GPS, which are not possible to identify by only using GPS.

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