Tracking Human Mobility using WiFi signals

TL;DR

This study demonstrates that WiFi signals can reliably track human mobility over six months, revealing both potential for high-resolution outdoor positioning and privacy concerns due to side-channel location inference.

Contribution

It introduces a method to infer human locations using WiFi signals with minimal GPS data, highlighting the stability of WiFi-based location tracking over extended periods.

Findings

WiFi scans contain strong latent location signals.

One GPS sample per day can estimate WiFi access point locations.

WiFi-based tracking accounts for 80% of mobility in the studied population.

Abstract

We study six months of human mobility data, including WiFi and GPS traces recorded with high temporal resolution, and find that time series of WiFi scans contain a strong latent location signal. In fact, due to inherent stability and low entropy of human mobility, it is possible to assign location to WiFi access points based on a very small number of GPS samples and then use these access points as location beacons. Using just one GPS observation per day per person allows us to estimate the location of, and subsequently use, WiFi access points to account for 80\% of mobility across a population. These results reveal a great opportunity for using ubiquitous WiFi routers for high-resolution outdoor positioning, but also significant privacy implications of such side-channel location tracking.