IoT-Based Scalable Framework for Pollution Aware Route Recommendation

Abstract

Swelling urban population and increasing the number of vehicles with finite road capacity account for a major section of air pollution in cities and towns, causing problems like the smog that lead to serious health issues. Exhaust gases from vehicles contain dangerous gases that poison the surroundings we live in as well as cause climate change. Congestion in traffic leads to a higher impact on the health of individuals as they remain in polluted conditions for a longer period which increases morbidity and mortality of travelers near high-density roads. As the environmental pollution problem has aroused more and more attention from the public, there is an increasing need to control and manage vehicles traffic, and also inform citizens regarding the ambient air quality to reduce the risk of health problems. This paper presents a cloud-based framework for effective air quality monitoring using machine learning. In the proposed approach, time series analysis is performed on the Spatio-temporal data that contains values of different gases and calculated air quality index (AQI) values concerning time and space. The experimental results indicate the efficacy of the proposed framework and prediction model. The LSTM-based proposed approach achieves 99% AQI prediction efficiency that helps to recommends pollution less route. Finally, the paper is concluded with issues, challenges, and future scope.