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Proceedings Article

Distributed neural computations for embedded sensor networks

[+] Author Affiliations
Courtney A. Peckens, Jerome P. Lynch

Univ. of Michigan (USA)

Jin-Song Pei

The Univ. of Oklahoma (USA)

Proc. SPIE 7981, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2011, 79811U (April 14, 2011); doi:10.1117/12.880057
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From Conference Volume 7981

  • Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2011
  • Masayoshi Tomizuka
  • San Diego, California, USA | March 06, 2011

abstract

Wireless sensing technologies have recently emerged as an inexpensive and robust method of data collection in a variety of structural monitoring applications. In comparison with cabled monitoring systems, wireless systems offer low-cost and low-power communication between a network of sensing devices. Wireless sensing networks possess embedded data processing capabilities which allow for data processing directly at the sensor, thereby eliminating the need for the transmission of raw data. In this study, the Volterra/Weiner neural network (VWNN), a powerful modeling tool for nonlinear hysteretic behavior, is decentralized for embedment in a network of wireless sensors so as to take advantage of each sensor's processing capabilities. The VWNN was chosen for modeling nonlinear dynamic systems because its architecture is computationally efficient and allows computational tasks to be decomposed for parallel execution. In the algorithm, each sensor collects it own data and performs a series of calculations. It then shares its resulting calculations with every other sensor in the network, while the other sensors are simultaneously exchanging their information. Because resource conservation is important in embedded sensor design, the data is pruned wherever possible to eliminate excessive communication between sensors. Once a sensor has its required data, it continues its calculations and computes a prediction of the system acceleration. The VWNN is embedded in the computational core of the Narada wireless sensor node for on-line execution. Data generated by a steel framed structure excited by seismic ground motions is used for validation of the embedded VWNN model.

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Citation

Courtney A. Peckens ; Jerome P. Lynch and Jin-Song Pei
"Distributed neural computations for embedded sensor networks", Proc. SPIE 7981, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2011, 79811U (April 14, 2011); doi:10.1117/12.880057; http://dx.doi.org/10.1117/12.880057


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