CAIL: Cross-calibration for accurate indoor localization without extensive site survey

WiFi-based indoor localization is a complex problem due to high variations of radio frequency (RF) signals in indoor environment. Many popular techniques based on RF fingerprinting require an extensive site survey, which involves time intensive logging of Received Signal Strength (RSS). This paper presents CAIL, a smartphone-based indoor localization system, that utilizes the site survey done by a phone to create an RF fingerprint which is utilized by new phones for location prediction. CAIL neither makes any assumptions about the site and placement of access points (APs) nor does it require any additional infrastructure. CAIL provides these new phones a minimal set of best locations to log at, thereby reducing the war-driving efforts for these phones. CAIL was tested in a building of six floors. Experimental results show that CAIL provides an accuracy of 76%, comparable to the accuracy of 81% on complete site survey, with an 84% reduction in effort.

[1]  Venkata N. Padmanabhan,et al.  Indoor localization without the pain , 2010, MobiCom.

[2]  Moustafa Youssef,et al.  The Horus WLAN location determination system , 2005, MobiSys '05.

[3]  Haiyun Luo,et al.  Zero-configuration indoor localization over IEEE 802.11 wireless infrastructure , 2010, Wirel. Networks.

[4]  Prathima Agrawal,et al.  ARIADNE: a dynamic indoor signal map construction and localization system , 2006, MobiSys '06.

[5]  Romit Roy Choudhury,et al.  SurroundSense: mobile phone localization via ambience fingerprinting , 2009, MobiCom '09.

[6]  Ravi Jain,et al.  Error characteristics and calibration-free techniques for wireless LAN-based location estimation , 2004, MobiWac '04.

[7]  Jie Yang,et al.  Accurate WiFi Based Localization for Smartphones Using Peer Assistance , 2014, IEEE Transactions on Mobile Computing.

[8]  Bill N. Schilit,et al.  Place Lab: Device Positioning Using Radio Beacons in the Wild , 2005, Pervasive.

[9]  Yunhao Liu,et al.  WILL: Wireless indoor localization without site survey , 2012, 2012 Proceedings IEEE INFOCOM.

[10]  Y. Ebihara.  Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[11]  Anshul Rai,et al.  Zee: zero-effort crowdsourcing for indoor localization , 2012, Mobicom '12.

[12]  William G. Griswold,et al.  ActiveCampus: experiments in community-oriented ubiquitous computing , 2004, Computer.

[13]  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).

[14]  Paramvir Bahl,et al.  DAIR: A Framework for Managing Enterprise Wireless Networks Using Desktop Infrastructure , 2005 .

[15]  Haiyun Luo,et al.  Zero-Configuration, Robust Indoor Localization: Theory and Experimentation , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.