Using Mobile and Fixed Internet of Things Sensing Networks, Real-Time Monitoring and Prediction of Air Quality in One\'s Immediate Vicinity

Abstract

Abstract: A significant global problem has emerged in several locations as a result of air pollution and its adverse impact on human well-being. Recently, there has been a rise in the number of researchers that are keen on evaluating and forecasting the air quality in close proximity to individuals. The use of the Internet of Things (IoT) across several businesses has significantly enhanced people's quality of life by interconnecting multiple sensors in diverse locations. Moreover, the Internet of Things (IoT) has streamlined the task of monitoring air pollution. The conventional utilization of stationary sensors is inadequate for acquiring an accurate and all-encompassing representation of the air pollution levels in close proximity to people. This is due to the fact that the sensors in closest proximity to people may be located many kilometers apart from each other. The objective of our study is to construct a model that precisely depicts the air quality pattern within a certain geographic area. This objective will be accomplished by using a combination of stationary and portable Internet of Things sensors. The sensors are affixed to vehicles that are carrying out surveillance in the vicinity. Our methodology allows for a thorough examination of the whole spectrum of air quality fluctuations in neighboring regions. Through the use of many machine learning algorithms and realworld data, we showcase the efficacy of our methodology in accurately discerning and forecasting air quality without compromising on accuracy. The results of our research indicate that there is significant potential for efficiently monitoring and forecasting air quality in the context of a smart city.