Tracking Human Mobility using WiFi signals

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

WiFi routers are located where people live.

<p>a: Map of Greater Copenhagen Area, divided into parishes with color indicating average number of routers discovered per scan; rectangle overlay indicates the city center. b: The number of access points visible in each scan is correlated with the population density (<i>r</i>² = 0.5). Even in low population density areas there are several routers visible on average in each scan. Therefore, knowing the positions of only a subset of routers is enough for precise location sensing. c: Distribution of number of routers per scan. In our dataset 92% of scans contain at least one router.</p

48 hours of location data of one of the authors, with the four visited locations visited marked in blue: home, two offices, and a food market.

<p>Even though the author’s phone has sensed 3 822 unique routers in this period, only a few are enough to describe the location more than 90% of time. a. traces recorded with GPS; b. traces reconstructed using all available data on WiFi routers locations—the transition traces are distorted, but all stop locations are visible and the location is known 97% of the time. c. with 8 top routers it is still possible to discover stop locations in which the author spent 95% of the time. In this scenario transitions are lost. d. timeseries showing when during 48 hours each of the top routers were seen. It can be assumed that AP 1 is home, as it’s seen every night, while AP 2 and AP 3 are offices, as they are seen during working hours. The last row shows the combined 95% of time coverage provided by the top 8 routers.</p

The time coverage provided by the routers with known position depends on who collects the corresponding location data and when it happens.

<p>In each subplot the orange line describes the scenario where each individual collects data about themselves and does not share it with others; the blue line corresponds to a system in which the location of routers discovered by one person is made known to other users; the green line presents a situation where each individual can use the common pool of known routers but does not discover access points herself. <b>a. Stability of location</b>. Learning the location of APs seen during the first seven (left panel) or 28 (right panel) days, leads to performance gradually decreasing with time in the personal case (orange line). The histograms of time coverage distribution for day 190 show that this decline is driven by a growing number of people who spend only ∼10% of time in the locations they visited in the beginning of the observation. The global approach (blue line) does not show this tendency, which indicates that people rotate between common locations rather than moving to entirely new places. <b>b, c. Representativeness of randomly selected locations.</b> Random subsampling with an average period of 24 hours (less than 1% of all available location estimations) is sufficient to find the most important locations in which people spend more than 80% of their time; using an average period of 4 hours (2.5% percent of all available location data) results in ∼85% coverage. The personal database does not expire since the location is sampled throughout the experiment, not only in the beginning. <b>d. Limited number of important locations.</b> Although querying commercial services for WiFi geolocation is costly, knowing the location of only the 20 most prevalent routers per person in the dataset results in an average coverage of ∼90%. Since people’s mobility overlaps, there is a benefit of using a global database rather than treating all mobility disjointly.</p