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Neural Processing Letters
Erschienen in: Neural Processing Letters 3/2014

01.12.2014

Distributed Fault Detection in Sensor Networks using a Recurrent Neural Network

verfasst von: Oliver Obst

Erschienen in: Neural Processing Letters | Ausgabe 3/2014

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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, 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.
Vorheriger Artikel A Rough RBF Neural Network Based on Weighted Regularized Extreme Learning Machine
Nächster Artikel A Weakly Connected Memristive Neural Network for Associative Memory

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Metadaten
Titel
Distributed Fault Detection in Sensor Networks using a Recurrent Neural Network
verfasst von
Oliver Obst
Publikationsdatum
01.12.2014
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 3/2014
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-013-9327-4

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