Detecting Inaccurate Sensors on a Large-Scale Sensor Network Using Centralized and Localized Graph Neural Networks

Abstract

This paper conducts an empirical study on detecting faulty sensors in a large-scale sensor network containing approximately 10, 000 sensors distributed over 36, 000 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . First, we discuss the practical challenge of this task. We compare rule-based models, traditional machine learning models, deep learning models without graph neural networks, and deep learning models with graph neural networks. The experimental results show that graph neural networks identify more problematic sensors in fewer trials than rule-based models and other machine learning and deep learning models. In addition to training the models in a central server, we also show that localized versions of the deep learning models with graph neural networks yield predictive power comparable to centralized training. Consequently, each sensor may perform a local inspection to identify its health status and only send reminder signals to a centralized server if it is self-diagnosed as a faulty sensor.