Indoor geo-fencing and access control for wireless networks

Having an idea of a user's location when he/she is using network services has been an area of interest ever since wireless networks became very popular. As the costs of wireless technologies decrease more and more, we observe the rise of an extremely diverse market of wireless capable devices. However, the field of indoor positioning is still wide open. In this field, most of the existing technologies are dependent on additional hardware and/or infrastructure, which increases the requirements for users. In this research, we investigate the ways of coupling indoor geo-fencing with access control including authentication and registration. To achieve this, we apply a classification based geo-fencing approach using received signal strength indicator. Consequently, we are mainly focusing on associating accurate geo-fencing with secure communication and computing. Experimental results show that we have achieved considerable positioning accuracy while providing a secure way of communication. Favouring diversity, our implementation does not mandate users to undergo any system software modification or adding new hardware components.

[1]  Roberto Battiti,et al.  Location-aware computing: a neural network model for determining location in wireless LANs , 2002 .

[2]  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).

[3]  Fusion of Wi-Fi and bluetooth for indoor localization , 2011, MLBS '11.

[4]  Kamalika Chaudhuri,et al.  Location determination of a mobile device using IEEE 802.11b access point signals , 2003, 2003 IEEE Wireless Communications and Networking, 2003. WCNC 2003..

[5]  Luca Benini,et al.  Bluetooth indoor localization with multiple neural networks , 2010, IEEE 5th International Symposium on Wireless Pervasive Computing 2010.

[6]  Fabrice Reclus,et al.  Geofencing for fleet & freight management , 2009, 2009 9th International Conference on Intelligent Transport Systems Telecommunications, (ITST).

[7]  V. Padmanabhan,et al.  Enhancements to the RADAR User Location and Tracking System , 2000 .

[8]  Roy Fielding,et al.  Architectural Styles and the Design of Network-based Software Architectures"; Doctoral dissertation , 2000 .

[9]  Henry Tirri,et al.  A Probabilistic Approach to WLAN User Location Estimation , 2002, Int. J. Wirel. Inf. Networks.

[10]  Henry Tirri,et al.  Topics in probabilistic location estimation in wireless networks , 2004, 2004 IEEE 15th International Symposium on Personal, Indoor and Mobile Radio Communications (IEEE Cat. No.04TH8754).

[11]  Moustafa Youssef,et al.  WLAN location determination via clustering and probability distributions , 2003, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..

[12]  Ramón F. Brena,et al.  Wifi bluetooth based combined positioning algorithm , 2012 .

[13]  Carsten Kleiner,et al.  BYOD — Bring Your Own Device , 2013, HMD Praxis der Wirtschaftsinformatik.

[14]  Gordon Thomson BYOD: enabling the chaos , 2012, Netw. Secur..

[15]  Hua Lu,et al.  Improving Wi-Fi Based Indoor Positioning Using Bluetooth Add-Ons , 2011, 2011 IEEE 12th International Conference on Mobile Data Management.

[16]  Hirokazu Taki,et al.  Adequate RSSI Determination Method by Making Use of SVM for Indoor Localization , 2006, KES.

[17]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[18]  Michael Rohs,et al.  BYOD: bring your own device , 2004 .

[19]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[20]  Ted Kremenek,et al.  A Probabilistic Room Location Service for Wireless Networked Environments , 2001, UbiComp.