AIoT-Powered Real-Time Sensing and Calibration of Low-Cost Particulate Matter Sensors Using the TCM Network

Godwin Msigwa, Minji An, Ester Ntambala, Jaeseok Yun

Abstract: The artificial intelligence of things (AIoT) integrates artificial intelligence (AI) and the Internet of Things (IoT) to create smart systems capable of environmental sensing and real-time interaction. Low-cost sensors (LCSs) offer scalable and continuous air quality monitoring, but their accuracy is often limited due to inherent biases, even after factory calibration. Real-world calibration is therefore essential for reliable measurements. This study presents an AIoT-based solution called the real-time particulate matter sensing and calibration (RPM-SC) system, which uses customized PMS7003 sensors equipped with externally controlled pulsewidth modulation (PWM) fans to improve airflow and measurement consistency. The sensors are integrated into a oneM2M-compliant Internet of Things (IoT) platform for standardized data collection and communication. To enhance measurement accuracy, we propose a novel calibration model: the trans-convolutional fusion memory-aware network (TCM-Net). This model combines transformer networks, temporal convolutional networks (TCNs), and a memory-aware module to capture temporal dependencies and correct sensor bias effectively. TCM-Net was trained on real-world datasets from RPM-SC systems co-located with a certified particulate matter (PM) reference instrument. It achieved a root mean squared error (RMSE) of $0.325~\mu $ g/m3 and a coefficient of determination ( $R^{2}$ ) of 0.999, outperforming conventional models. The results confirm the effectiveness of the proposed AIoT architecture and calibration model in providing accurate and reliable PM2.5 data from LCSs.

External IDs:doi:10.1109/jiot.2025.3631631