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 language | English |
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Title of host publication | Abstracts and Posters of NIPS 2009 Workshop: Large-Scale Machine Learning: Parallelism and Massive Datasets, 11th December 2009, Whistler, Canada |
Publisher | Neural Information Processing Systems Foundation |
Number of pages | 1 |
Publication status | Published - 2009 |
Event | Neural Information Processing Systems Workshop - Duration: 1 Jan 2009 → … |
Conference
Conference | Neural Information Processing Systems Workshop |
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Period | 1/01/09 → … |
Keywords
- sensor networks