WiFi Fingerprint Clustering for Urban Mobility Analysis

• Main Authors: Sumudu Hasala Marakkalage, Billy Pik Lik Lau, Yuren Zhou, Ran Liu, Chau Yuen, Wei Quin Yow, Keng Hua Chong
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: POI extraction; clustering; data fusion; mobility analysis; unsupervised learning
• Online Access: https://ieeexplore.ieee.org/document/9422709/

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, neighborhood 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.