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arXiv 2012.117962020-12-22cs.LGstat.AP

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

Singapore University of Technology and Design（新加坡科技设计大学）

Zann Koh, Yuren Zhou, Billy Pik Lik Lau, Chau Yuen, Bige Tuncer, Keng Hua Chong

备注：: 15 pages, 11 figures
DOI：: 10.1109/TBDATA.2020.3045154

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英文摘要：

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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