Analyzing passive Wi-Fi fingerprinting for privacy-preserving indoor-positioning

Wi-Fi fingerprinting is the most actively investigated indoor positioning technique, yielding adequate positioning accuracy on existing wireless infrastructures. However, the positioning process commonly requires active 802.11 scans where probe requests are sent out. These frames can easily be captured and used for tracking mobile devices without the users' consent or even awareness. In order to preserve the users' privacy, a fully passive positioning process for Wi-Fi fingerprinting is proposed in this paper. With the presented approach, a mobile device passively listens for beacon frames in monitor mode to determine a valid RSSI fingerprint while not sending out any information. Our passive method is evaluated against common active fingerprinting in a real-world environment. The obtained results yield the conclusion that the proposed approach performs even slightly better in terms of accuracy and precision. Furthermore, less time is needed for obtaining a position fix, while preserving the users' privacy during the acquisition of position updates.

[1]  Nikos Pelekis,et al.  Privacy-Preserving Indoor Localization on Smartphones , 2015, IEEE Transactions on Knowledge and Data Engineering.

[2]  Komwut Wipusitwarakun,et al.  Indoor localization improvement via adaptive RSS fingerprinting database , 2013, The International Conference on Information Networking 2013 (ICOIN).

[3]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[4]  C. Rizos,et al.  Method for yielding a database of location fingerprints in WLAN , 2005 .

[5]  Limin Sun,et al.  Achieving privacy preservation in WiFi fingerprint-based localization , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

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

[7]  Philipp Marcus,et al.  Estimating crowd densities and pedestrian flows using wi-fi and bluetooth , 2014, MobiQuitous.

[8]  Helen J. Wang,et al.  Preserving location privacy in wireless lans , 2007, MobiSys '07.

[9]  Simo Ali-Löytty,et al.  A comparative survey of WLAN location fingerprinting methods , 2009, 2009 6th Workshop on Positioning, Navigation and Communication.

[10]  Martin Werner,et al.  SMARTPOS: Accurate and Precise Indoor Positioning on Mobile Phones , 2011 .

[11]  Florian Gschwandtner,et al.  Spontaneous privacy-friendly indoor positioning using enhanced WLAN beacons , 2011, 2011 International Conference on Indoor Positioning and Indoor Navigation.

[12]  Wolfgang Effelsberg,et al.  COMPASS: A probabilistic indoor positioning system based on 802.11 and digital compasses , 2006, WINTECH.