Edge-Docker–Based Architecture for Intelligent Indoor Air Quality Management With Sensing Calibration and Automatic Controlling

Reports show that poor indoor air quality can be harmful to vulnerable groups and lead to various health problems. To address this problem, this work proposes a management architecture for enhancing indoor air quality by integrating the analytical learning models and regulation of indoor and outdoor pollutant concentrations, which coordinates the activation or deactivation of the pollutant control devices. The proposed system incorporates predictive and calibration functionalities to enhance overall system stability and effectiveness. This work tests the prediction accuracy of multilayer perceptron and recurrent neural network models. The experimental results show that the bidirectional long short-term memory (Bi-LSTM) with a land use regression (LUR)–based feature extraction model achieves the best predictive performance with a mean absolute error of 5.74 and a mean absolute percentage error of 15.7%, respectively. Comparing the existing Bi-LSTM work for PM2.5 prediction, the proposed Bi-LSTM model with feature selection delivers superior accuracy by about 14.58% in terms of the mean absolute error performance. To further assess the system feasibility, a self-designed air box with the Docker technology is developed to customize system parameters for various monitoring needs. The system has undergone validations through Ansys indoor airflow simulation software and scenario testing, demonstrating its effectiveness and great promise for the rapid removal of indoor pollutants.