Graph-Neural-Network-Based Intermittent Fault Diagnosis for Reliability of Symbiotic Internet of Things

Rapid iterations and updates in both software and hardware, along with significant advancements in communication technology, have given rise to the concepts of symbiotic Internet of Things (IoT) and ubiquitous interconnectivity, providing strong evidence for the flourishing development of the IoT. However, the limited resources and computing capabilities, along with the heterogeneity of deployment environments, make symbiotic IoT devices more susceptible to security threats and operational issues. Intermittent failures are especially prevalent in the symbiotic IoT, leading to more significant risks for devices. In this article, we present an intermittent fault diagnosis based on long short-term memory and graph attention network (IFDGAT-LSTM) framework for diagnosing intermittent failures in wireless sensing devices within the symbiotic IoT. The framework is based on a graph neural network and takes into account not only the time series characteristics of symbiotic IoT devices but also their deployment topology. By incorporating both aspects, we achieve more accurate diagnostics of intermittent failures in the symbiotic IoT, thus enhancing its reliability. First, we propose the concept of a quasi-dynamic graph based on the variations in the topology within the symbiotic IoT. Subsequently, we introduce an intermittent failure diagnosis framework that combines a graph neural network to identify intermittent failure nodes within the quasi-dynamic graph. Finally, we performed experiments on the WADI symbiotic IoT dataset to evaluate the performance of our model in diagnosing intermittent failure nodes. We used the precision, recall, and F1 score metrics for assessment. The experimental outcomes show that our proposed model, IFDGAT-LSTM, achieves an Precision of 99.58% in diagnosing intermittent failure nodes. This highlights the strong performance and efficacy of the IFDGAT-LSTM model.