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Tsinghua Science and Technology 2014, 19(3): 225-234 https://doi.org/10.1109/TST.2014.6838193
Open Access | Issue | Published: 18 June 2014

Mining Sensor Data in Cyber-Physical Systems

Show Author's Information Lu-An Tang( ),  Jiawei Han,  Guofei Jiang
NEC Laboratory America, Princeton, NJ 08540, USA.
Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
Keywords:
cyber-physical system, sensor network, data trustworthiness
Cite this article:
Tang L-A, Han J, Jiang G. Mining Sensor Data in Cyber-Physical Systems. Tsinghua Science and Technology, 2014, 19(3): 225-234. https://doi.org/10.1109/TST.2014.6838193
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Abstract Full text About this article

A Cyber-Physical System (CPS) integrates physical devices (i.e., sensors) with cyber (i.e., informational) components to form a context sensitive system that responds intelligently to dynamic changes in real-world situations. Such a system has wide applications in the scenarios of traffic control, battlefield surveillance, environmental monitoring, and so on. A core element of CPS is the collection and assessment of information from noisy, dynamic, and uncertain physical environments integrated with many types of cyber-space resources. The potential of this integration is unbounded. To achieve this potential the raw data acquired from the physical world must be transformed into useable knowledge in real-time. Therefore, CPS brings a new dimension to knowledge discovery because of the emerging synergism of the physical and the cyber. The various properties of the physical world must be addressed in information management and knowledge discovery. This paper discusses the problems of mining sensor data in CPS: With a large number of wireless sensors deployed in a designated area, the task is real time detection of intruders that enter the area based on noisy sensor data. The framework of IntruMine is introduced to discover intruders from untrustworthy sensor data. IntruMine first analyzes the trustworthiness of sensor data, then detects the intruders’ locations, and verifies the detections based on a graph model of the relationships between sensors and intruders.


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About this article

Mining Sensor Data in Cyber-Physical Systems

Show Author's information Lu-An Tang( ), Jiawei Han, Guofei Jiang
NEC Laboratory America, Princeton, NJ 08540, USA.
Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.

Abstract

A Cyber-Physical System (CPS) integrates physical devices (i.e., sensors) with cyber (i.e., informational) components to form a context sensitive system that responds intelligently to dynamic changes in real-world situations. Such a system has wide applications in the scenarios of traffic control, battlefield surveillance, environmental monitoring, and so on. A core element of CPS is the collection and assessment of information from noisy, dynamic, and uncertain physical environments integrated with many types of cyber-space resources. The potential of this integration is unbounded. To achieve this potential the raw data acquired from the physical world must be transformed into useable knowledge in real-time. Therefore, CPS brings a new dimension to knowledge discovery because of the emerging synergism of the physical and the cyber. The various properties of the physical world must be addressed in information management and knowledge discovery. This paper discusses the problems of mining sensor data in CPS: With a large number of wireless sensors deployed in a designated area, the task is real time detection of intruders that enter the area based on noisy sensor data. The framework of IntruMine is introduced to discover intruders from untrustworthy sensor data. IntruMine first analyzes the trustworthiness of sensor data, then detects the intruders’ locations, and verifies the detections based on a graph model of the relationships between sensors and intruders.

Keywords: cyber-physical system, sensor network, data trustworthiness

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Received: 07 May 2014
Accepted: 09 May 2014
Published: 18 June 2014
Issue date: June 2014

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© The author(s) 2014

Acknowledgements

The work was supported in part by the U.S. Army Research Laboratory under Cooperative Agreement Nos. W911NF-09-2-0053 (NS-CTA) and W911NF-11-2-0086 (Cyber-Security), the U.S. Army Research Office under Cooperative Agreement No. W911NF-13-1-0193, DTRA, and U.S. National Science Foundation grants CNS-0931975, IIS-1017362, IIS-1320617, and IIS-1354329. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.

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