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.

翻译：老化检测与故障预测在众多物联网部署中至关重要，这些系统包含大量无人值守的嵌入式设备，需在野外环境持续运行数年。本文基于通用试验平台的154块电路板，对自然SRAM磨损展开大规模实证分析。从各节点启动时易于采集的SRAM初始化偏差出发，我们采用多种指标进行特征提取，并利用常见机器学习方法实验预测节点运行时长。研究结果表明，尽管老化影响十分细微，但所提出的指标仍能有效估算设备使用时间——回归模型的$R^2$评分达0.77，平均误差为24%；分类模型在六个月分辨率下的F1分数超过0.6。