Revenue oriented air quality prediction microservices for smart cities

A notion of creating smart cities with integrated high end ICT technologies, including cloud, edge, IoTs, and so forth, has increased recently in order to provide a reasonably high comfort to city residents with limited human intervention. In fact, one of the major challenges faced by city authorities of developing countries is to control air pollution, which might cause chronic diseases and financial concerns to the residents. Although a few air quality monitoring and air pollution control measures exist, these solutions are not remaining as assets to cities. In this paper, a revenue oriented, cloud microservice based air quality prediction framework is proposed for smart cities. This framework collects the air quality parameters such as SO2, NO2, RSPM, and so forth, for different cities in a mongodb based cloud database; it develops air quality prediction models based on Random Forests (RF); and it predicts the air quality parameter values accordingly. For each access to the proposed air quality prediction framework, revenue oriented cloud microservices, written in golang, are initiated and the credits for city stations are calculated based on the prediction queries. In the experiments, the prediction framework for predicting SO2, NO2, and RSPM attained over 70 to 90 percentage of prediction accuracies.