Sensor drift is a critical challenge in the reliable long-term operation of nondispersive infrared (NDIR) gas sensors. Traditional error models primarily focus on static uncertainties, but sensor drift introduces a time-variant component that can significantly degrade measurement reliability. In this work, we formalize the drift source decomposition problem and show how a constrained additive drift decomposition model (ADDM) enables identifiable separation of reversible environmental effects and irreversible instrumental drift. Using penalized least squares estimation, we identify and quantify these terms from long-term in-field CO2 measurement data. Experimental results demonstrate that the proposed approach effectively separates irreversible drift from reversible environmental effects, and also helps to improve the accuracy and interpretability of baseline calibration methods. The proposed approach is potentially generalizable to other sensor types and provides a foundation for enhancing long-term measurement stability and reliability.

Efficient calibration of smart sensors is critical in resource-constrained systems. This work introduces a decomposition-based framework for inverse transfer-function approximation, enabling fine-grained control over accuracy and computational complexity. Using a thermistor circuit as a case study, we evaluate different low-cost implementations of a key nonlinear calibration step. Our results on ARM Cortex-M23 show that exponential LUTs with interpolation strike a strong balance between accuracy and resource usage.

IoT-based low-cost gas sensors networks are important for environmental monitoring, but their regular calibrations are needed to achieve acceptable sensing performance. A critical step in network calibration is identifying when sensors within the network are sensing the same phenomenon, which is essential for accurate calibration. In this paper, we propose an adaptive similarity-search-based method for detecting these periods of similarity under the assumption of linear sensor drift. Our method leverages the relationships between neighboring sensors' measurements to enhance calibration accuracy, outperforming the commonly used Pearson correlation approach. We validate the effectiveness of our method through experiments with both synthetic data and real-world CO2 sensor networks, demonstrating improved calibration accuracy and reliability.

Low-cost non-dispersive infrared (NDIR) CO2 sensors are widely used for continuous air quality monitoring, but their long-term reliability is hindered by instrumental drift. This paper presents an empirical and theoretical investigation into the drift behavior of such sensors, based on long-term measurement data. After isolating environmental effects, we observe that the residual instrumental drift exhibits meanreverting characteristics. We model this behavior using the Ornstein-Uhlenbeck process, a stochastic differential equation well-suited for capturing such dynamics. Maximum likelihood estimation is employed to fit the model to real sensor data, revealing sensor-specific long-term means and reversion rates. The results have implications for the design of more effective calibration strategies, particularly for one-point methods like automatic baseline correction. Limitations of the OU model are also discussed, motivating directions for future modeling improvements.