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Proceedings Paper

Heterogeneous wireless sensor networks for computational partitioning of Markov parameter-based system identification
Author(s): Jeff D. Bergman; Junhee Kim; Jerome P. Lynch
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Paper 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.

Paper Details

Date Published: 26 March 2012
PDF: 15 pages
Proc. SPIE 8345, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2012, 83450N (26 March 2012);
Show Author Affiliations
Jeff D. Bergman, Univ. of Michigan (United States)
Junhee Kim, KAIST (Korea, Republic of)
Jerome P. Lynch, Univ. of Michigan (United States)

Published in SPIE Proceedings Vol. 8345:
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2012
Masayoshi Tomizuka; Chung-Bang Yun; Jerome P. Lynch, Editor(s)

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