AIR-IoT ITINEARY: Deep DenseNet-Based Air Quality Monitoring Using Real-Time Sensors in Urban Areas

The Internet of Things (IoT) is being used increasingly in the control and monitoring of air quality. Real-time data regarding air pollutants and other environmental parameters can be gathered by deploying IoT devices with sensors and connectivity capabilities. Rapid urbanization and industry cause increasingly serious problems with air quality. A pivotal challenge in the current Air Quality Monitoring (AQM) model is its limited spatial coverage and accuracy. In this paper, a novel AQM using the IoT (AIR-IoT ITINEARY) technique is proposed to gauge the atmospheric condition efficiently and instantly. Sensors are placed in the various traffic systems to collect environmental data and process it in the Real-Time Data Analytics Module (RTDM). DenseNet is used to predict the quality of air and is classified into three classes, namely pure and impure. If pollution levels exceed the threshold, it alerts the user and suggests an alternative route. The efficacy of the proposed AIR-IoT ITINEARY technique has been evaluated using assessment actions such as accuracy, time efficiency, precision, F1 score, RMSE, MAPE, and MAE. According to the comparison analysis, the proposed AIR-IoT ITINEARY technique's accuracy rate is 10.08%, 17.64%, and 34.34% higher than the existing IdleAir, SMOTEDNN, and ETAPM-AIT techniques, respectively