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

TL;DR

This paper introduces a systematic framework using unsupervised machine learning to analyze passive Wi-Fi sensing data for understanding human movement patterns in urban areas.

Contribution

It presents a novel approach applying k-means and hierarchical clustering to passive Wi-Fi data, analyzing it from multiple perspectives including time, person, and location.

Findings

Effective clustering of Wi-Fi data by time, person, and location

Demonstrated approach on real-world dataset over five months

Provides insights into human mobility patterns in urban environments

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.