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.