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.

[1]  N. Stanietsky,et al.  The interaction of TIGIT with PVR and PVRL2 inhibits human NK cell cytotoxicity , 2009, Proceedings of the National Academy of Sciences.

[2]  Nicu Sebe,et al.  Money walks: a human-centric study on the economics of personal mobile data , 2014, UbiComp.

[3]  Petter Holme,et al.  Predictability of population displacement after the 2010 Haiti earthquake , 2012, Proceedings of the National Academy of Sciences.

[4]  Piotr Sapiezynski,et al.  Measuring Large-Scale Social Networks with High Resolution , 2014, PloS one.

[5]  Sun-Ki Chai,et al.  Social Computing, Behavioral-Cultural Modeling and Prediction , 2014, Lecture Notes in Computer Science.

[6]  A. Pentland,et al.  Life in the network: The coming age of computational social science: Science , 2009 .

[7]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[8]  Jun Rekimoto,et al.  LifeTag: WiFi-Based Continuous Location Logging for Life Pattern Analysis , 2007, LoCA.

[9]  Xiao Liang,et al.  Unraveling the origin of exponential law in intra-urban human mobility , 2012, Scientific Reports.

[10]  Jing Liu,et al.  Survey of Wireless Indoor Positioning Techniques and Systems , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[11]  Alex Pentland,et al.  The predictability of consumer visitation patterns , 2010, Scientific Reports.

[12]  Dieter Fox,et al.  Gaussian Processes for Signal Strength-Based Location Estimation , 2006, Robotics: Science and Systems.

[13]  Vincent Roca,et al.  Short paper: WifiLeaks: underestimated privacy implications of the access_wifi_state android permission , 2014, WiSec '14.

[14]  Ryuichi Kitamura,et al.  Micro-simulation of daily activity-travel patterns for travel demand forecasting , 2000 .

[15]  Chong Meng Samson See,et al.  A Real-Time Indoor WiFi Localization System Utilizing Smart Antennas , 2007, IEEE Transactions on Consumer Electronics.

[16]  Nobuo Kawaguchi.  WiFi Location Information System for Both Indoors and Outdoors , 2009, IWANN.

[17]  E. Kandel,et al.  Proceedings of the National Academy of Sciences of the United States of America. Annual subject and author indexes. , 1990, Proceedings of the National Academy of Sciences of the United States of America.

[18]  Aravind Srinivasan,et al.  Modelling disease outbreaks in realistic urban social networks , 2004, Nature.

[19]  Alessandro Vespignani,et al.  Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions , 2007, PLoS medicine.

[20]  Philip B. Stark.  Privacy, Big Data, and the Public Good: Frameworks for Engagement , 2016 .

[21]  Kay W. Axhausen,et al.  Efficient detection of contagious outbreaks in massive metropolitan encounter networks , 2014, Scientific Reports.

[22]  T. Geisel,et al.  Forecast and control of epidemics in a globalized world. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[23]  Juan M. Corchado,et al.  Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living, 10th International Work-Conference on Artificial Neural Networks, IWANN 2009 Workshops, Salamanca, Spain, June 10-12, 2009. Proceedings, Part II , 2009, IWANN.

[24]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[25]  Albert-László Barabási,et al.  Limits of Predictability in Human Mobility , 2010, Science.

[26]  César A. Hidalgo,et al.  Unique in the Crowd: The privacy bounds of human mobility , 2013, Scientific Reports.

[27]  Alex Pentland,et al.  Predicting Personality Using Novel Mobile Phone-Based Metrics , 2013, SBP.

[28]  V. Stodden,et al.  Privacy, Big Data, and the Public Good: Frameworks For Engagement , 2014 .