TY - GEN
T1 - Decentralized random decrement technique for data aggregation and system identification in wireless smart sensor networks
AU - Sim, Sung Han
AU - Spencer, B. F.
AU - Jo, Hongki
AU - Carbonell-Márquez, Juan Francisco
PY - 2011
Y1 - 2011
N2 - 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 RDT, which is an output-only system identification approach, a hierarchical approach is described and shown to be suitable for implementation in the intrinsically decentralized 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 decentralized RDT method is demonstrated experimentally in terms of the required data communication and the accuracy of identified dynamic properties.
AB - 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 RDT, which is an output-only system identification approach, a hierarchical approach is described and shown to be suitable for implementation in the intrinsically decentralized 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 decentralized RDT method is demonstrated experimentally in terms of the required data communication and the accuracy of identified dynamic properties.
KW - Decentralized processing
KW - Natural Excitation Technique
KW - Output-only system identification
KW - Random Decrement Technique
KW - Wireless smart sensor
UR - http://www.scopus.com/inward/record.url?scp=84861066413&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84861066413&partnerID=8YFLogxK
U2 - 10.1007/978-94-007-0732-0_30
DO - 10.1007/978-94-007-0732-0_30
M3 - Conference contribution
AN - SCOPUS:84861066413
SN - 9789400707313
T3 - Solid Mechanics and its Applications
SP - 305
EP - 314
BT - IUTAM Symposium on Nonlinear Stochastic Dynamics and Control - Proceedings of the IUTAM Symposium
T2 - IUTAM Symposium on Nonlinear Stochastic Dynamics and Control
Y2 - 10 May 2010 through 14 May 2010
ER -