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Distributed Backpropagation-Decorrelation Learning


Oliver Obst. Distributed Backpropagation-Decorrelation Learning. In NIPS Workshop: Large-Scale Machine Learning: Parallelism and Massive Datasets, 2009.


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Abstract

In long-term deployments of sensor networks, monitoring the quality of gathered data is a critical issue. Over the time of deployment, exposure to harsh condition may cause sensors to degrade or to fail. If such a degradation remains undetected, the usefulness of a sensor network is greatly reduced. We introduce SODBPDC, a distributed recurrent network architecture, and a method to learn spatio-temporal correlations between different sensors for fault detection in a distributed way. Our approach is evaluated using real sensor network data, and proves to work well with less-than-perfect link qualities and more than 50% of failed sensors.

BiBTeX Entry


@inproceedings{Obst09d,
	Abstract = {In long-term deployments of sensor networks, monitoring
		   the quality of gathered data is a critical issue. Over the time of
		   deployment, exposure to harsh condition may cause sensors to degrade or to
		   fail. If such a degradation remains undetected, the usefulness of a sensor
		   network is greatly reduced. We introduce SODBPDC, a distributed recurrent
		   network architecture, and a method to learn spatio-temporal correlations
		   between different sensors for fault detection in a distributed way. Our
		   approach is evaluated using real sensor network data, and proves to work
		   well  with less-than-perfect link qualities and more than 50% of failed
		   sensors.},
	Author = {Oliver Obst},
	Booktitle = {NIPS Workshop: Large-Scale Machine Learning:
		   Parallelism and Massive Datasets},
	Title = {Distributed Backpropagation-Decorrelation Learning},
	Year = 2009,