Context-Aware Monitoring and Control of Ventilation Rate in Indoor Environments Using Internet of Things

As the proportion of time spent by humans in indoor environment increases, it becomes challenging to maintain good air quality for healthy and productive life. The need to develop a context aware, reliable system capable of providing real time information and alerts on indoor air quality is addressed in this article. The proposed Internet-of-Things (IoT) system serves to collect data, predict ventilation states, and provide alerts and recommendations to the end user. A novel method for determination of ventilation states using three indoor pollutants PM_2.5, PM₁₀, and CO is proposed. Multilevel logistic regression is first used to define indoor ventilation states using ventilation rate which is calculated with the help of indoor CO₂ concentration. K-NN classification technique then predicts indoor ventilation state with the help of three input attributes, PM_2.5, PM₁₀, and CO. Context-aware information about indoor environment and current ventilation state is conveyed to the end-user in form of an alert, through a smartphone application. The system is found to determine the poor ventilation state with accuracy, precision, recall and F1 score values of 94.34%, 0.91, 0.88, and 0.89, respectively.