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The Scientific World Journal
Volume 2013, Article ID 589610, 11 pages
http://dx.doi.org/10.1155/2013/589610
Research Article

Unsupervised User Similarity Mining in GSM Sensor Networks

Department of Computer Science and Technology, University of Science and Technology of China, Huangshan Road, Hefei, Anhui 230027, China

Received 29 December 2012; Accepted 26 January 2013

Academic Editors: Y.-P. Huang and M.-A. Sicilia

Copyright © 2013 Shafqat Ali Shad and Enhong Chen. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Mobility data has attracted the researchers for the past few years because of its rich context and spatiotemporal nature, where this information can be used for potential applications like early warning system, route prediction, traffic management, advertisement, social networking, and community finding. All the mentioned applications are based on mobility profile building and user trend analysis, where mobility profile building is done through significant places extraction, user’s actual movement prediction, and context awareness. However, significant places extraction and user’s actual movement prediction for mobility profile building are a trivial task. In this paper, we present the user similarity mining-based methodology through user mobility profile building by using the semantic tagging information provided by user and basic GSM network architecture properties based on unsupervised clustering approach. As the mobility information is in low-level raw form, our proposed methodology successfully converts it to a high-level meaningful information by using the cell-Id location information rather than previously used location capturing methods like GPS, Infrared, and Wifi for profile mining and user similarity mining.