From Pressure to Path: Barometer-based Vehicle Tracking

Pervasive mobile devices have enabled countless context- and location-based applications that facilitate navigation, life-logging, and more. As we build the next generation of smart cities, it is important to leverage the rich sensing modalities that these numerous devices have to offer. This work demonstrates how mobile devices can be used to accurately track driving patterns based solely on pressure data collected from the device's barometer. Specifically, by correlating pressure time-series data against topographic elevation data and road maps for a given region, a centralized computer can estimate the likely paths through which individual users have driven, providing an exceptionally low-power method for measuring driving patterns of a given individual or for analyzing group behavior across multiple users. This work also brings to bear a more nefarious side effect of pressure-based path estimation: a mobile application can, without consent and without notifying the user, use pressure data to accurately detect an individual's driving behavior, compromising both user privacy and security. We further analyze the ability to predict driving trajectories in terms of the variance in barometer pressure and geographical elevation, demonstrating cases in which more than 80% of paths can be accurately predicted.

[1]  Jun Han,et al.  ACComplice: Location inference using accelerometers on smartphones , 2012, 2012 Fourth International Conference on Communication Systems and Networks (COMSNETS 2012).

[2]  A. B. M. Musa,et al.  Tracking unmodified smartphones using wi-fi monitors , 2012, SenSys '12.

[3]  E. Pasero,et al.  Environment sensing using smartphone , 2012, 2012 IEEE Sensors Applications Symposium Proceedings.

[4]  Frank van Diggelen,et al.  A-GPS: Assisted GPS, GNSS, and SBAS , 2009 .

[5]  Mun Choon Chan,et al.  Using mobile phone barometer for low-power transportation context detection , 2014, SenSys.

[6]  Romit Roy Choudhury,et al.  Tapprints: your finger taps have fingerprints , 2012, MobiSys '12.

[7]  Azeem J. Khan,et al.  Barometric phone sensors: more hype than hope! , 2014, HotMobile.

[8]  Sasu Tarkoma,et al.  Accelerometer-based transportation mode detection on smartphones , 2013, SenSys '13.

[9]  References , 1971 .

[10]  Mani B. Srivastava,et al.  Privacy risks emerging from the adoption of innocuous wearable sensors in the mobile environment , 2011, CHI.

[11]  Mani B. Srivastava,et al.  Truth Discovery in Crowdsourced Detection of Spatial Events , 2014, IEEE Transactions on Knowledge and Data Engineering.

[12]  Klara Nahrstedt,et al.  Identity, location, disease and more: inferring your secrets from android public resources , 2013, CCS.

[13]  T. K. Vintsyuk.  Speech discrimination by dynamic programming , 1968 .

[14]  Seth J. Teller,et al.  Online pose classification and walking speed estimation using handheld devices , 2012, UbiComp '12.

[15]  Jun Han,et al.  ACCessory: password inference using accelerometers on smartphones , 2012, HotMobile '12.

[16]  Prabal Dutta,et al.  AutoWitness: locating and tracking stolen property while tolerating GPS and radio outages , 2010, SenSys '10.

[17]  Gabi Nakibly,et al.  Gyrophone: Recognizing Speech from Gyroscope Signals , 2014, USENIX Security Symposium.

[18]  P. Tavella,et al.  The Ornstein–Uhlenbeck process as a model of a low pass filtered white noise , 2008 .

[19]  Ramachandran Ramjee,et al.  Nericell: rich monitoring of road and traffic conditions using mobile smartphones , 2008, SenSys '08.

[20]  Qiang Wang,et al.  Energy efficient GPS sensing with cloud offloading , 2012, SenSys '12.