W-UDDR: A Unified Framework for Automated Drift Detection in IoT-based Air Quality Monitoring Systems

The use of low-cost sensors (LCS) in Internet of Things (IoT) networks offers a promising way to improve air quality monitoring. However, there is a major concern regarding their long-term accuracy due to continuous data drift, which requires frequent data recalibration. To address this, we present window-based uncertainty drift detection and recalibration (W-UDDR), a unified system that automates the entire process. Our system uses a Bayesian approach with Gaussian processes (GP) to automatically and accurately detect when sensor output needs to be corrected. To achieve this, the uncertainty of the estimates is quantified using predictive confidence intervals alongside a window system that detects the need for recalibration in real time. We validate our approach using an air quality real-world sensor deployment, systematically assessing key performance metrics such as the frequency of recalibration and the required sample size. Our results show that W-UDDR successfully triggers automatic events when it detects drifts, achieving significant long-term accuracy improvements ranging from 40% to 95%. Hence, this tool provides an automated real-time mechanism to facilitate long-term sensor deployment maintenance.