Distributed backpropagation-decorrelation learning

Research output: Chapter in Book / Conference PaperConference Paper

Abstract

![CDATA[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.]]
Original languageEnglish
Title of host publicationAbstracts and Posters of NIPS 2009 Workshop: Large-Scale Machine Learning: Parallelism and Massive Datasets, 11th December 2009, Whistler, Canada
PublisherNeural Information Processing Systems Foundation
Number of pages1
Publication statusPublished - 2009
EventNeural Information Processing Systems Workshop -
Duration: 1 Jan 2009 → …

Conference

ConferenceNeural Information Processing Systems Workshop
Period1/01/09 → …

Keywords

  • sensor networks

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