ABSTRACT
Quick urbanization and change in the smart city facilities owing to technology call for new and innovative means of pollution monitoring and managing. In this regard, the current study proposes a communication protocol in the 6 G networks combined with dynamic resource allocation for pollution monitoring. Additionally, the study adopts machine learning technologies including ANN , DT , LR , and SVM to accurately predict pollution levels and trends in smart city situations. The solution allows for the application of an accurate machine-learning-based approach to pollution prediction while offering a communication protocol that can ensure interruption-free connection between the pollution sensor networks and the central pollution monitoring system. The proposed resource allocation scheme guarantees that the network resources including, but not restricted to, computing power and bandwidth are used efficiently. The performance of each of the four models in accurately predicting pollution is tested by calculating precision, recall, F1 score, and accuracy metrics. The performance of each of the models in distinguishing between classes is analyzed closely by drawing the confusion matrix. The results show that all models have a superior level of accuracy with ANN being slightly more accurate than the other models. The results suggest that using advanced communication protocols combined with accurate machine learning technologies can help with pollution monitoring. In this way, the relevant stakeholders can make informed decisions prior to the onset of adverse pollution levels.187
