Indoor Crowd 3D Localization in Big Buildings from Wi-Fi Access Anonymous Data

Abstract

Indoor crowd localization and counting in big public buildings pose problems of infrastructure deployment, signal processing, and privacy. Conventional approaches based on optical cameras, either in the visible or infrared range, received signal strength in wireless networks, sound or chemical sensing in sensor networks need careful calibration, noise removal, and sophisticated data processing to achieve results in limited scenarios. Moreover, personal data protection is a growing concern, so that detection methods that preserve the privacy of people are highly desirable. The aim of this paper is to provide a technique that may generate estimations of the localization of people in a big public building using anonymous data from already-deployed Wi-Fi infrastructure. We present a method applying geostatistical techniques to the access data acquired from Access Points (AP) in an open Wi-Fi network. Specifically, only the time series of the number of accesses per AP is required. Geostatistical methods produce a 3D high-quality spatial distribution representation of the people inside the building based on the interaction of their mobile devices with the APs. We report encouraging results obtained from data acquired at a building of Wroclaw University of Science and Technology.

Monitoring Large Crowds With WiFi: A Privacy-Preserving Approach

“…The work [6] proposes a crowd monitoring solution for user localization in large buildings. They rely on clients connected to access points they control.…”

Computational Intelligence in Remote Sensing: An Editorial

“…Moreover, personal data protection is a growing concern, so that detection methods that preserve the privacy of people are highly desirable. The aim of [1] is to provide a technique that may generate estimations of the localization of people in a big public building using anonymous data from already-deployed Wi-Fi infrastructure. We present a method applying geostatistical techniques to the access data acquired from access points in an open Wi-Fi network.…”

“…The appearance of new and more powerful remote sensing technologies has produced a surge of remote sensing data to be processed for a variety of applications in the natural sciences, such as agriculture, forestry or ecological monitoring, and others, such as the automotive industry. This special issue contains a broad sample of such applications, including indoor crowd detection and localization by means of anonymous and non invasive sensors [1]; applications in the automotive industry, such as the detection of the incoming obstacles/vehicles by on-board radar [2], and the detection of highly contaminant vehicles [3]; land cover segmentation for several purposes, such as ecological monitoring [4,5], and land uses [6,7].…”

Monitoring Large Crowds With WiFi: A Privacy-Preserving Approach

“…The work [12] proposes a crowd monitoring solution for user localization in large buildings. They rely on clients connected to access points to which they have access.…”