Please use this identifier to cite or link to this item: https://hdl.handle.net/11499/57324
Title: Cepair: an AI-powered and fog-based predictive CEP system for air quality monitoring
Authors: Şimsek, M.U.
Kök, İ.
Özdemir, S.
Keywords: Air pollution
Complex event processing (CEP)
Deep learning
Fog computing
Internet of things (IoT)
Air quality
Complex networks
Decision making
Fog
Fog computing
Forecasting
Industrial emissions
Long short-term memory
Air quality monitoring
Complex event processing
Complex events
Deep learning
End-to-end network
Event Processing
Internet of thing
Network-delay
Pollutant gas
Processing systems
Internet of things
Publisher: Springer
Abstract: 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. © The Author(s) 2024.
URI: https://doi.org/10.1007/s10586-024-04434-2
https://hdl.handle.net/11499/57324
ISSN: 1386-7857
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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