Abstract
In long-term deployments of sensor networks, monitoring the quality of gathered data is a critical issue. Over the time of deployment, sensors are exposed to harsh conditions, causing some of them to fail or to deliver less accurate data. If such a degradation remains undetected, the usefulness of a sensor network can be greatly reduced. We present an approach that learns spatio-temporal correlations between different sensors, and makes use of the learned model to detect anomalous sensors by using distributed computation and only local communication between nodes. We introduce SODESN, a distributed recurrent neural network architecture, and a learning method to train SODESN for fault detection in a distributed scenario. Our approach is evaluated using data from a real-world sensor-network deployment, and shows good results even with imperfect link qualities and a number of simultaneous faults.
Original language | English |
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Pages (from-to) | 261-273 |
Number of pages | 13 |
Journal | Neural Processing Letters |
Volume | 40 |
Issue number | 3 |
DOIs | |
Publication status | Published - 2014 |
Keywords
- fault detection
- neural networks (computer science)
- sensor networks