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