Temporal Pattern-Based Denoising and Calibration for Low-Cost Sensors in IoT Monitoring Platforms

Abstract

The introduction of low-cost sensors (LCSs) in air quality Internet of Things (IoT) monitoring platforms presents the challenge of improving the quality of the data that these sensors provide. In this paper, we propose two algorithms to perform denoising and calibration for LCSs used in IoT monitoring platforms. Sensors are first calibrated in-situ using linear or nonlinear machine learning models that only take into account instantaneous measurements. The best calibration model is used to estimate the values measured by the sensor during the sensor deployment. To improve the values of the estimates produced by the in-situ calibration model, we propose to take into account the temporal patterns present in signals such as temperature or tropospheric ozone that have regular patterns, e.g. daily. The first method, which we call temporal pattern-based denoising (TPB-D), performs signal denoising by projecting the daily signals of the in-situ calibrated LCS onto a subspace generated by the daily signals stored in a database taken by reference instruments. The second method, which we call temporal pattern-based calibration (TPB-C), considers that if we also have a reference instrument co-located to the LCSs over a period of time, we can correct with a linear mapping with regularization the daily LCS signals projected in the subspace produced by the reference database to be as similar as possible to the projected signals of the co-located reference instrument. The results show that TPB-D improves the estimates made by in-situ calibration by up to 10-20% while the TPB-C improves the estimates made by in-situ calibration by up to 20-40%.