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Sim, Sung-Han
Smart Infrastructure Systems Lab
Research Interests
  • Structural Dynamics, Smart Sensor and Sensor Network, Structural Health Monitoring, Digital Image Processing

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Decentralized random decrement technique for efficient data aggregation and system identification in wireless smart sensor networks

Cited 13 times inthomson ciCited 21 times inthomson ci
Title
Decentralized random decrement technique for efficient data aggregation and system identification in wireless smart sensor networks
Author
Sim, Sung-HanFrancisco Carbonell-Marquez, JuanSpencer, B. F., Jr.Jo, Hongki
Keywords
Data aggregation; Random decrement technique; Structural health monitoring; System identification; Wireless smart sensors
Issue Date
2011-01
Publisher
ELSEVIER SCI LTD
Citation
PROBABILISTIC ENGINEERING MECHANICS, v.26, no.1, pp.81 - 91
Abstract
Smart sensors have been recognized as a promising technology with the potential to overcome many of the inherent difficulties and limitations associated with traditional wired structural health monitoring (SHM) systems. The unique features offered by smart sensors, including wireless communication, on-board computation, and cost effectiveness, enable deployment of the dense array of sensors that are needed for monitoring of large-scale civil infrastructure. Despite the many advances in smart sensor technologies, power consumption is still considered as one of the most important challenges that should be addressed for the smart sensors to be more widely adopted in SHM applications. Data communication, the most significant source of the power consumption, can be reduced by appropriately selecting data processing schemes and the related network topology. This paper presents a new decentralized data aggregation approach for system identification based on the Random Decrement Technique (RDT). Following a brief overview of the RDT, which is an output-only system identification approach, a decentralized hierarchical approach is described and shown to be suitable for implementation in the intrinsically distributed computing environment found in wireless smart sensor networks (WSSNs). RDT-based decentralized data aggregation is then implemented on the Imote2 smart sensor platform based on the Illinois Structural Health Monitoring Project (ISHMP) Services Toolsuite. Finally, the efficacy of the RDT method is demonstrated experimentally in terms of the required data communication and the accuracy of identified dynamic properties.
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DOI
10.1016/j.probengmech.2010.07.002
ISSN
0266-8920
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UEE_Journal Papers
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