Hazards or intentional perturbations must be identified in safety- and security-critical applications. Digital sensors have been shown to be an appealing approach to detect such abnormalities. However, as any sensor technology, digital sensors are prone to mis-calibration. In particular, even if the digital sensor initial calibration is correct, the rate of false and missed alarms might increase when the sensor is aged. In this paper, we thoroughly study the impact of aging-induced false and missed alarms. Indeed aging relates to the usage time, and a priori model (historical data for environmental variation) for predicting the aging is unrealistic for digital sensors as tracking the usage time with related temperature and voltage variation imposes high overhead. Accordingly, we propose an alternative approach where not one but two sensors are deployed. In practice, one sensor is used to detect environmental deviations, while the second one is used as the reference. In this respect, the second sensor is only operated seldom, mostly to re-calibrate the active sensor when aged. From this dual input (unaged and aged sensor), corrective models are derived. We account for two methods, namely simple but effective offset correction, and adjustment based on machine-learning. We conduct extensive characterizations (both pre-silicon simulations and post-silicon measurements on FPGA) which quantitatively confirm the applicability and high sensitivity of digital sensors.

必须在安全和安保关键应用中识别危险或故意扰动。数字传感器已被证明是检测此类异常的一种有吸引力的方法。然而，与任何传感器技术一样，数字传感器容易出现校准错误。特别是，即使数字传感器初始校准是正确的，当传感器老化时，误报和漏报率可能会增加。在本文中，我们深入研究了老化引起的误报和漏报的影响。确实老化与使用时间有关，并且先验用于预测老化的模型（环境变化的历史数据）对于数字传感器来说是不现实的，因为跟踪使用时间以及相关的温度和电压变化会带来很高的开销。因此，我们提出了一种替代方法，其中部署的不是一个而是两个传感器。在实践中，一个传感器用于检测环境偏差，而第二个传感器用作参考。在这方面，第二个传感器很少操作，主要是在老化时重新校准有源传感器。从这个双输入（未老化和老化的传感器），可以得出校正模型。我们考虑了两种方法，即简单但有效的偏移校正和基于机器学习的调整。