Virtual sensing for remote correction in IoT air quality low-cost sensor networks

Air quality monitoring has been revolutionized by the use of low-cost sensors (LCS) mounted on the Internet of Things (IoT) nodes. These LCS technologies enable finer monitoring resolutions, e.g., measurements at street level or in specific locations of interest. When combined with government-ruled reference stations, these nodes establish heterogeneous measurement networks that allow for finer spatial resolution. However, a major problem with using LCS is their long-term data quality, as both sensors and calibration models tend to suffer from drift and concept drift, respectively. Recently, machine learning techniques, in conjunction with periodic and opportunistic recalibration and remote correction, have been proven to provide enhanced data quality performance. In this paper, we propose a hybrid mechanism that leverages remote correction based on virtual sensors alongside periodic recalibration to improve long-term data quality while reducing the operational costs associated with node co-location for recalibration. The results demonstrate that the proposed mechanism enhances the viability of LCS for long-term monitoring deployments by an improvement of up to 17%, compared to traditional recalibration approaches, while reducing operational costs.