An Integrated Framework for Smart Earthquake Prediction: IoT, Fog, and Cloud Computing

Earthquakes are among the most complex and expensive natural catastrophes that humans face, which strikes without warning. As a result, earthquake prediction has become a challenging and crucial task for humanity. Efficacious earthquake prediction has the potential to significantly minimize earthquake destruction, which is highly pertinent for the community and people, and there appears to be an upsurge of interest and scientific research on seismic events prediction. With the technological revolution in the fields of sensing applications, cloud computing, communication networks, internet of things, fog computing, and big data, it is now plausible to design an integrated earthquake prediction system that can effectively alert earthquake-affected regions. In this study, a collaborative Internet of Things (IoT)-centered smart earthquake monitoring and prediction framework is proposed with the congruence of fog and cloud computing. The IoT methodology is employed to collect data from sensors and transferred it to the fog layer for pre-processing, feature extraction, selection, and classification using random forest. Further, Adaptive Neuro Fuzzy Inference System (ANFIS) is used for forecasting earthquake magnitude at the cloud layer. The experimental results of the proposed model demonstrate its effectiveness in monitoring and predicting earthquakes. Moreover, testing of the developed prototype has revealed that by leveraging fog, it is possible to achieve significant delay reduction while maintaining high accuracy and throughput, making it more real-time compliant.