Ageing Analysis of Embedded SRAM on a Large-Scale Testbed Using Machine
  Learning
Leandro Lanzieri Peter Kietzmann Goerschwin Fey Holger Schlarb Thomas C. Schmidt
Ageing 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
general-purpose 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 $R^2$
score of 0.77 and a mean error of 24% using regressors, and with an F1 score
above 0.6 for classifiers applying a six-months resolution.