Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Distributed Fault Detection in Sensor Networks using a Recurrent Neural
  Network   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 misbehaving 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 different types of sensors and is able to work well even with
less-than-perfect link qualities and more than 50% of failed nodes.

6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph