A Bluetooth User Positioning System for Locating, Informing, and Extracting Information Using Data Mining Techniques

Until now, user positioning systems were focused mainly on providing users with exact location information. This makes them computational heavy while often demanding specialized software and hardware from mobile devices. In this article we present a new user positioning system. The system is intended for use with m-commerce, by sending informative and advertising messages to users, after locating their position indoors. It is based exclusively on Bluetooth. The positioning method we use, while efficient is nevertheless simple. The m-commerce based messages, can be received without additional software or hardware installed. Moreover, the location data collected by our system are further processed using data mining techniques, in order to provide statistical information. After discussing the available technologies and methods for implementing indoor user positioning applications, we shall focus on implementation issues, as well as the evaluation of our system after testing it. Finally, conclusions are extracted.

[1]  Panayiotis Zaphiris,et al.  Human computer interaction : concepts, methodologies, tools, and applications , 2009 .

[2]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[3]  Yan Jiang.  Dynamic Spectrum Auction and Load Balancing Algorithm in Heterogeneous Network , 2011, Int. J. Adv. Pervasive Ubiquitous Comput..

[4]  Ding Xiaojun,et al.  Unique Features of Mobile Commerce , 2004 .

[5]  Upkar Varshney.  Location management support for mobile commerce applications , 2001, WMC '01.

[6]  L. Hilty.  Information and Communication Technologies for a more Sustainable World , 2011 .

[7]  M. Hasegawa,et al.  Design and implementation of a Bluetooth signal strength based location sensing system , 2004, Proceedings. 2004 IEEE Radio and Wireless Conference (IEEE Cat. No.04TH8746).

[8]  Michael J. A. Berry,et al.  An Introduction to Data Mining , 2003 .

[9]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[10]  John Ayoade,et al.  RFID for Identification of Stolen/Lost Items , 2009 .

[11]  Gregory Piatetsky-Shapiro,et al.  Knowledge Discovery in Databases: An Overview , 1992, AI Mag..

[12]  David Parry,et al.  Auto-Identification and Ubiquitous Computing Applications , 2009 .

[13]  Timo Hämäläinen,et al.  Experiments on local positioning with Bluetooth , 2003, Proceedings ITCC 2003. International Conference on Information Technology: Coding and Computing.

[14]  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).

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

[16]  Yunhao Liu,et al.  ANDMARC: Indoor Location Sensing Using Active RFID , 2003, PerCom.

[17]  Giuseppe Anastasi,et al.  Experimenting an indoor bluetooth-based positioning service , 2003, 23rd International Conference on Distributed Computing Systems Workshops, 2003. Proceedings..

[18]  Timo Ojala,et al.  Bluetooth and WAP push based location-aware mobile advertising system , 2004, MobiSys '04.