A collaborative Bluetooth-based approach to localization of mobile devices

In this paper we present a collaborative Bluetooth localization method, which aggregates the location information about Bluetooth devices that is provided by multiple mobile devices. The method aims to take advantage of an enhanced version of the KNN algorithm in which the location of a mobile device can be determined based on a Bluetooth distance measure. The experimental results showed that there are a sufficient number of Bluetooth devices that are discoverable, which can be used collaboratively to assist with localization requests from mobile devices. In addition, it is shown that the Bluetooth localization method was able to successfully localize the mobile device using the Bluetooth radio. Furthermore, the response time of Bluetooth localization is shown to be less than GPS, and greater than Wi-Fi and Cellular localization methods.

[1]  Feng Zhao,et al.  Energy-accuracy trade-off for continuous mobile device location , 2010, MobiSys '10.

[2]  Injong Rhee,et al.  Towards Mobile Phone Localization without War-Driving , 2010, 2010 Proceedings IEEE INFOCOM.

[3]  Jatinder Pal Singh,et al.  Improving energy efficiency of location sensing on smartphones , 2010, MobiSys '10.

[4]  Reynold Cheng,et al.  Energy-Efficient Monitoring of Mobile Objects with Uncertainty-Aware Tolerances , 2007, 11th International Database Engineering and Applications Symposium (IDEAS 2007).

[5]  R. Suganya,et al.  Data Mining Concepts and Techniques , 2010 .

[6]  Majid Ahmadi,et al.  Robust indoor positioning using differential wi-fi access points , 2010, IEEE Transactions on Consumer Electronics.

[7]  Wesley Chan.  DealFinder: A Collaborative, Location-Aware Mobile Shopping Application* , 2001 .

[8]  Sudarshan S. Chawathe.  Low-latency indoor localization using bluetooth beacons , 2009, 2009 12th International IEEE Conference on Intelligent Transportation Systems.

[9]  Josef Hallberg,et al.  Positioning with Bluetooth , 2003, 10th International Conference on Telecommunications, 2003. ICT 2003..

[10]  Deborah Estrin,et al.  SensLoc: sensing everyday places and paths using less energy , 2010, SenSys '10.

[11]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[12]  Polly Huang,et al.  Impact of sensor-enhanced mobility prediction on the design of energy-efficient localization , 2008, Ad Hoc Networks.

[13]  G. N. Lance,et al.  Mixed-Data Classificatory Programs I - Agglomerative Systems , 1967, Aust. Comput. J..

[14]  Yi Wang,et al.  A framework of energy efficient mobile sensing for automatic user state recognition , 2009, MobiSys '09.