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

초록

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/m(3) 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.

키워드