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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 t...

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Detalles Bibliográficos
Autores principales: Sapiezynski, Piotr, Stopczynski, Arkadiusz, Gatej, Radu, Lehmann, Sune
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4489206/
https://www.ncbi.nlm.nih.gov/pubmed/26132115
http://dx.doi.org/10.1371/journal.pone.0130824
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author Sapiezynski, Piotr
Stopczynski, Arkadiusz
Gatej, Radu
Lehmann, Sune
author_facet Sapiezynski, Piotr
Stopczynski, Arkadiusz
Gatej, Radu
Lehmann, Sune
author_sort Sapiezynski, Piotr
collection PubMed
description 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.
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spelling pubmed-44892062015-07-14 Tracking Human Mobility Using WiFi Signals Sapiezynski, Piotr Stopczynski, Arkadiusz Gatej, Radu Lehmann, Sune PLoS One Research Article 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. Public Library of Science 2015-07-01 /pmc/articles/PMC4489206/ /pubmed/26132115 http://dx.doi.org/10.1371/journal.pone.0130824 Text en © 2015 Sapiezynski et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Sapiezynski, Piotr
Stopczynski, Arkadiusz
Gatej, Radu
Lehmann, Sune
Tracking Human Mobility Using WiFi Signals
title Tracking Human Mobility Using WiFi Signals
title_full Tracking Human Mobility Using WiFi Signals
title_fullStr Tracking Human Mobility Using WiFi Signals
title_full_unstemmed Tracking Human Mobility Using WiFi Signals
title_short Tracking Human Mobility Using WiFi Signals
title_sort tracking human mobility using wifi signals
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4489206/
https://www.ncbi.nlm.nih.gov/pubmed/26132115
http://dx.doi.org/10.1371/journal.pone.0130824
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