MAIC: Metalearning-Based Adaptive In-Field Calibration for IoT Air Quality Monitoring System

Abstract

Air pollution has become a global threat to human health. Fine-grained air quality monitoring has attracted much attention in recent years. Low-cost calibrated sensors make it possible for the large-scale deployment of IoT air quality monitoring systems. In practice, the calibration performance degrades after deployment due to the dynamic and diversity of system conditions. However, it is infeasible to collect sufficient in-field reference data to train calibration models for these new conditions. To address the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multicondition</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">few-data</i> challenge, we proposed metalearning-based adaptive in-field calibration (MAIC), a metalearning-based adaptive in-field calibration algorithm. Specifically, MAIC adopts metalearning to learn how to adapt to new conditions quickly. To effectively leverage historical data, we first develop task generation strategies for sensor calibration under this scheme. Then, task-oriented optimization is introduced to train a model with superior adaptability in the offline training phase. Furthermore, an adaptation method is presented to learn the task-specific data distribution without forgetting the metaknowledge, enabling continual learning to utilize the temporal dependencies between multiple conditions. Our evaluations on synthetic and real-world data sets show that MAIC has high robustness and adaptability under multiple complicated conditions. Our proposed method outperforms the state-of-the-art calibration algorithms by 4.23%–29.46% in the real-world deployment data set, but with fewer requirements for the available in-field reference data.