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International Journal of Distributed Sensor Networks
Volume 2012 (2012), Article ID 724846, 19 pages
http://dx.doi.org/10.1155/2012/724846
Research Article

Multidimensional Sensor Data Analysis in Cyber-Physical System: An Atypical Cube Approach

1University of Illinois at Urbana-Champaign, Urbana, JL 61801, USA
2National Chiao Tung University, Hsinchu 30010, Taiwan
3BBN Technologies, Cambridge, MA 02138, USA
4The Pennsylvania State University, Philadelphia, PA 16802, USA

Received 16 December 2011; Accepted 21 March 2012

Academic Editor: Chih-Yung Chang

Copyright © 2012 Lu-An Tang et al. 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

Cyber-Physical System (CPS) is an integration of distributed sensor networks with computational devices. CPS claims many promising applications, such as traffic observation, battlefield surveillance, and sensor-network-based monitoring. One important topic in CPS research is about the atypical event analysis, that is, retrieving the events from massive sensor data and analyzing them with spatial, temporal, and other multidimensional information. Many traditional methods are not feasible for such analysis since they cannot describe the complex atypical events. In this paper, we propose a novel model of atypical cluster to effectively represent such events and efficiently retrieve them from massive data. The basic cluster is designed to summarize an individual event, and the macrocluster is used to integrate the information from multiple events. To facilitate scalable, flexible, and online analysis, the atypical cube is constructed, and a guided clustering algorithm is proposed to retrieve significant clusters in an efficient manner. We conduct experiments on real sensor datasets with the size of more than 50 GB; the results show that the proposed method can provide more accurate information with only 15% to 20% time cost of the baselines.