IoT Based Air Quality Monitoring with Multi-Model Analysis using Machine Learning Algorithms

Abstract

Using conventional methodological analysis, air automatic monitoring system has decent perfection and accuracy with high cost and bulky systems which makes the unsuitable for large-scale installation. Grounded on introducing embedded system into the field of environmental protection, this design puts forward a kind of real-time air pollution monitoring system. By using embedded, this system can reduce the tackle cost into 1/10 as ahead. The system can be laid out in a large number in monitoring area to form monitoring detector network mesh. Besides the functions of conventional air automatic monitoring system, it also exhibits the function of development trend of air pollution within a certain time range by analyzing the data attained by embedded system. Furthermore, the system logs the data collected to cloud server which can be used for analysis and prediction using multiple ML algorithms for better accuracy and consistent results.