  Air pollution is a vital issue emerging from the uncontrolled utilization of
traditional energy sources as far as developing countries are concerned. Hence,
ingenious air pollution forecasting methods are indispensable to minimize the
risk. To that end, this paper proposes an Internet of Things (IoT) enabled
system for monitoring and controlling air pollution in the cloud computing
environment. A method called Linear Regression and Multiclass Support Vector
(LR-MSV) IoT-based Air Pollution Forecast is proposed to monitor the air
quality data and the air quality index measurement to pave the way for
controlling effectively. Extensive experiments carried out on the air quality
data in the India dataset have revealed the outstanding performance of the
proposed LR-MSV method when benchmarked with well-established state-of-the-art
methods. The results obtained by the LR-MSV method witness a significant
increase in air pollution forecasting accuracy by reducing the air pollution
forecasting time and error rate compared with the results produced by the other
state-of-the-art methods
