Internet of Things Based Environment Monitoring and PM10 Prediction for Smart Home

People spend 80 to 90% of their time in the building environment, including home and office. Therefore, maintaining a comfortable living environment in indoor premises is extremely relevant. Among several hazardous indoor air pollutants, the particulate matter has been considered to leave a considerable impact on human health and well-being. It is observed to be the main cause of several chronic health issues such as respiratory illness, lung cancer, allergies, and asthma. In this study, the framework of an Internet of Things based environment monitoring system is presented to deal with the rising threats of indoor air pollution. Furthermore, PM₁₀ concentration is predicted using Random Forest algorithm. Predicting PM₁₀ levels can help the building occupants to take adequate ventilation measures ahead of time. The overall performance of the proposed prediction system is measured using five different parameters where RMSE =0.594, MSE = 0.353, MAE =0.337, R² Score =0.996, and MAPE = 3.90%. Furthermore, the overall accuracy of the system on the test dataset is 97.72%. This system can work as an integral part of a smart home while ensuring real-time pollution monitoring. The guided and controlled switching of heating and ventilation systems can reduce the overall energy consumption.