基于循环神经网络的传感器网络分布式故障检测
Distributed Fault Detection in Sensor Networks using a Recurrent Neural Network
摘要：
在传感器网络的长期部署中，监测收集的数据的质量是一个关键问题。在部署过程中，传感器会暴露在恶劣的条件下，导致其中一些传感器出现故障或提供不太准确的数据。如果这样的退化仍然未被检测到，传感器网络的有用性可以大大降低。我们提出了一种方法，学习不同的传感器之间的时空相关性，并利用学习到的模型，通过使用分布式计算和节点之间的本地通信来检测行为不端的传感器。我们介绍SODESN，分布式循环神经网络架构，和学习方法训练SODESN故障检测在分布式的情况下。我们的方法是使用来自不同类型的传感器的数据进行评估，并能够很好地工作，即使不太完美的链路质量和超过50%的失败节点。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
