Multiple-Perspective Clustering of Passive Wi-Fi Sensing Trajectory Data
This work provides a systematic analysis framework for urban planners and researchers using low-cost, wide-coverage passive Wi-Fi sensing data, representing an incremental step in data analysis.
This paper addresses the lack of a standardized framework for analyzing spatiotemporal human flow data collected via passive Wi-Fi sensing. It proposes using k-means and hierarchical agglomerative clustering to analyze a five-month real-world dataset, examining clustering by time, person, and location.
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.