FAILURE PREDICTION METHOD FOR INTERNET OF THINGS NETWORKS USING MACHINE LEARNING

The article addresses the problem of ensuring the reliability and uninterrupted operation of Internet of Things networks consisting of a large number of sensor nodes, gateways, and distributed computing elements. Due to the high heterogeneity of devices, rapid topology changes, and heterogeneity of data flows, such networks are vulnerable to various types of failures—hardware, network, and software. In view of this, fault prediction methods capable of detecting risks of system destabilization in advance are becoming increasingly relevant. The advantages of using hybrid machine learning approaches that combine time series analysis and the assessment of spatial interaction between network nodes are substantiated. A fault prediction method LGFP is proposed, built on the combination of graph neural networks (GNN) and LSTM architecture, which provides comprehensive data interpretation. The method allows estimating the probability of failure occurrence based on current and previous telemetry parameters while considering the mutual influence of network elements. An analysis of existing approaches is carried out, a comparison of machine learning models is performed, and the process of data