Cepair: an AI-powered and fog-based predictive CEP system for air quality monitoring

Air pollution is one of the influential problems threatening the environment and human health today. Therefore, it is critical to develop predictive systems for proactive decisions in solving this problem. Since the prediction of air pollution depends on several complicated factors such as the accuracy of meteorology reports, air pollution accumulation, traffic flow, and industrial emissions, the contribution of historical or real-time predictions to the solution of the problem is limited. To address the existing limitations, we propose a novel AI-powered and Fog-based predictive complex event processing system (CepAIr) for the prediction of future air pollution rates. CepAIr predicts the future air quality of pollutant gases using RNN, LSTM, CNN, and SVR models. Then, it sends the prediction results to decision-makers in an understandable format, enabling them to take proactive actions. Finally, we evaluate the performance of the CepAIr with SVR and DL models. Additionally, we examine CepAIr in terms of end-to-end network delay and measure its impact on the network. The extensive simulation results demonstrate that the CepAIr predicts future pollutant gas concentrations with DL models (especially with CNN) with a high success rate while guaranteeing minimum end-to-end network delay.