Lanzieri, LeandroLeandroLanzieriKietzmann, PeterPeterKietzmannFey, GörschwinGörschwinFeySchmidt, Thomas C.Thomas C.SchmidtSchlarb, HolgerHolgerSchlarb2024-04-262024-04-26202326th Euromicro Conference on Digital System Design: 335-242 (2023)979-835034419-6https://hdl.handle.net/11420/47293Ageing detection and failure prediction are essential in many Internet of Things (IoT) deployments, which operate huge quantities of embedded devices unattended in the field for years. In this paper, we present a large-scale empirical analysis of natural SRAM wear-out using 154 boards from a generalpurpose testbed. Starting from SRAM initialization bias, which each node can easily collect at startup, we apply various metrics for feature extraction and experiment with common machine learning methods to predict the age of operation for this node. Our findings indicate that even though ageing impacts are subtle, our indicators can well estimate usage times with an R2 score of 0.77 and a mean error of 24% using regressors, and with an Fl score above 0.6 for classifiers applying a six-months resolution.enEmbedded hardwareIoTmachine learningpredictive maintenanceMLE@TUHHComputer SciencesEngineering and Applied OperationsConference Paper10.1109/DSD60849.2023.00054Conference Paper