Multiple-Perspective Clustering of Passive Wi-Fi Sensing Trajectory Data

• URL: http://arxiv.org/abs/2012.11796v1
• Date: Tue, 22 Dec 2020 02:30:16 GMT
• Title: Multiple-Perspective Clustering of Passive Wi-Fi Sensing Trajectory Data
• Authors: Zann Koh, Yuren Zhou, Billy Pik Lik Lau, Chau Yuen, Bige Tuncer, and Keng Hua Chong
• Abstract summary: We propose a systematic approach by using unsupervised machine learning methods to analyze data collected through a passive Wi-Fi sniffing method. We examine three aspects of clustering of the data, namely by time, by person, and by location. We present the results obtained by applying our proposed approach on a real-world dataset collected over five months.
• Score: 19.301855796680613
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Information about the spatiotemporal flow of humans within an urban context has a wide plethora of applications. Currently, although there are many different approaches to collect such data, there lacks a standardized framework to analyze it. The focus of this paper is on the analysis of the data collected through passive Wi-Fi sensing, as such passively collected data can have a wide coverage at low cost. We propose a systematic approach by using unsupervised machine learning methods, namely k-means clustering and hierarchical agglomerative clustering (HAC) to analyze data collected through such a passive Wi-Fi sniffing method. We examine three aspects of clustering of the data, namely by time, by person, and by location, and we present the results obtained by applying our proposed approach on a real-world dataset collected over five months.

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