Not All Those Who Drift Are Lost: Drift Correction and Calibration Scheduling for the IoT

Sensors provide a critical link between digital and physical systems in the Internet of Things (IoT). However, as they age, their accuracy degrades due to drift. This reduces data trustworthiness and requires significant maintenance investment to mitigate, especially in large-scale sensor deployments typical of IoT systems. Previous approaches to drift correction typically require large volumes of ground truth data and do not consider measurement or prediction uncertainty. In this paper, we propose a probabilistic sensor drift correction method that takes a fundamental approach to modelling the sensor response using Gaussian Process Regression. Tested using dissolved oxygen sensors, our method delivers mean squared error (MSE) reductions of up to 90% and more than 20% on average. We also propose a novel uncertainty-driven calibration schedule optimisation approach that builds on top of drift correction and further reduces MSE by up to 15.7%.