Paper
26 March 2012 Heterogeneous wireless sensor networks for computational partitioning of Markov parameter-based system identification
Jeff D. Bergman, Junhee Kim, Jerome P. Lynch
Author Affiliations +
Abstract
Embedded computation in wireless sensor networks (WSN) can extract useful information from sensor data in a fast and efficient manner. Embedded computing has the benefit of saving both bandwidth and power. However, computational capability often comes at the expense of power consumption on the wireless sensor node. This is an especially critical issue for battery powered wireless sensor nodes. By developing a hybrid network consisting of wireless units optimized for sensing interspersed with more powerful computationally focused units, it is now possible to build a network that is more efficient and flexible than a homogeneous WSN. For this project, such a network was developed using Narada units as low-power sensing units and iMote2 units as ultra-efficient computational engines. In order to demonstrate the capabilities of such a configuration a network was created to extract structural modal parameters based on Markov parameters. This paper validates the performance of the heterogeneous WSN using a laboratory structure tested under impulse loading.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jeff D. Bergman, Junhee Kim, and Jerome P. Lynch "Heterogeneous wireless sensor networks for computational partitioning of Markov parameter-based system identification", Proc. SPIE 8345, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2012, 83450N (26 March 2012); https://doi.org/10.1117/12.916055
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KEYWORDS
Sensor networks

Sensors

Data modeling

System identification

Clocks

Energy efficiency

Matrices

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