Published Paper Details:

Air pollution and its detriment to mortal health has come a serious problem in numerous metropolises at the world. In recent times, exploration interests in measuring and prognosticating the quality of air around people has spiked. Since the Internet of effects (IoT) has been extensively used in different disciplines to ameliorate the quality life for people by connecting multiple detectors in different places, it also makes the air pollution covering further easier than ahead. Traditional way of using fixed detectors cannot effectively give a comprehensive view of air pollution in people's immediate surroundings, since the closest detectors can be conceivably long hauls down. Our exploration focuses on modeling the air quality pattern in a given region by espousing both fixed and moving IOT detectors, which are placed on vehicles patrolling around the region. With our approach, a full diapason of how air quality varies in near regions can be anatomized. We demonstrate the feasibility of our approach in effectively measuring and prognosticating air quality using different machine learning algorithms with real world data. Our evaluation shows a promising result for effective air quality monitoring and vaticination for a smart megacity operation.