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

Saini, Kanika; Kalra, Sheetal; Sood, Sandeep K.

doi:10.1007/s10723-022-09600-7

Cited by 12 publications

(6 citation statements)

References 30 publications

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“…Fourier transform can convert the signal from the time domain to the frequency domain, to obtain the amplitude and phase information of each frequency component of the waveform signal to facilitate the analysis of the signal characteristics [3]. The current data are discrete, so it is necessary to use the discrete Fourier transform, and the positive inverse transform formula for signal 𝑥𝑥(𝑛𝑛) and its spectrum 𝑋𝑋(k) is：…”

Section: Fourier Transformmentioning

confidence: 99%

Research on Earthquake Discrimination and Magnitude Prediction Based on Fourier Power Spectrum Sample Entropy and Machine Learning Algorithm

Gao,

Xu,

Wang

2023

JAMC

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In this paper, the characteristics of seismic wave data are extracted mainly through windowed Fourier transform and power spectrum sample entropy. A support vector machine classification model and random forest regression model are respectively established to classify seismic events and predict the grade of the earthquake. The results showed that for earthquake discrimination, the accuracy of the test set reached 82.7%; For grade prediction, the MAE reached 0.58. Therefore, classification support vector machine and regression random forest can be used for earthquake identification and grade prediction. Additionally, the power spectrum sample entropy of the waveform signal can be used to measure the characteristics of the waveform. The earthquake discrimination and magnitude prediction methods based on Fourier power spectrum sample entropy and machine learning algorithms have potential applications in the field of earthquake monitoring and early warning. These research results provide a feasible technical tool for earthquake-related decision-making and help improve the prediction and response capability of earthquake hazards. However, further research and validation are still needed to further improve and optimize the performance and stability of the method.

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Section: Fourier Transformmentioning

confidence: 99%

Research on Earthquake Discrimination and Magnitude Prediction Based on Fourier Power Spectrum Sample Entropy and Machine Learning Algorithm

Gao,

Xu,

Wang

2023

JAMC

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“…Fog computing, a decentralized architecture that enables computation closer to the data source, reduces latency, and improves overall performance [ 37 , 38 ]. It is particularly suitable for real-time applications like video analytics and those requiring high levels of security and privacy [ 39 , 40 ]. On the other hand, cloud computing, a centralized architecture reliant on remote servers, is useful for applications with extensive storage and processing requirements without immediate response times.…”

Section: Introductionmentioning

confidence: 99%

A Quality-of-Service-Aware Service Composition Method in the Internet of Things Using a Multi-Objective Fuzzy-Based Hybrid Algorithm

Hamzei,

Khandagh,

Jafari Navimipour

2023

Sensors

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The Internet of Things (IoT) represents a cutting-edge technical domain, encompassing billions of intelligent objects capable of bridging the physical and virtual worlds across various locations. IoT services are responsible for delivering essential functionalities. In this dynamic and interconnected IoT landscape, providing high-quality services is paramount to enhancing user experiences and optimizing system efficiency. Service composition techniques come into play to address user requests in IoT applications, allowing various IoT services to collaborate seamlessly. Considering the resource limitations of IoT devices, they often leverage cloud infrastructures to overcome technological constraints, benefiting from unlimited resources and capabilities. Moreover, the emergence of fog computing has gained prominence, facilitating IoT application processing in edge networks closer to IoT sensors and effectively reducing delays inherent in cloud data centers. In this context, our study proposes a cloud-/fog-based service composition for IoT, introducing a novel fuzzy-based hybrid algorithm. This algorithm ingeniously combines Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC) optimization algorithms, taking into account energy consumption and Quality of Service (QoS) factors during the service selection process. By leveraging this fuzzy-based hybrid algorithm, our approach aims to revolutionize service composition in IoT environments by empowering intelligent decision-making capabilities and ensuring optimal user satisfaction. Our experimental results demonstrate the effectiveness of the proposed strategy in successfully fulfilling service composition requests by identifying suitable services. When compared to recently introduced methods, our hybrid approach yields significant benefits. On average, it reduces energy consumption by 17.11%, enhances availability and reliability by 8.27% and 4.52%, respectively, and improves the average cost by 21.56%.

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“…According to the results, data-driven models outperform knowledge-driven models in susceptibility mapping. Data-driven models can predict earthquake dispersion patterns, which the human eye cannot [19].…”

Section: Introductionmentioning

confidence: 99%

2-D Deep Convolutional Neural Network for Predicting the Intensity of Seismic Events

Turarbek¹,

Adetbekov²,

Bektemesov³

2023

IJACSA

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Machine learning has advanced rapidly in the last decade, promising to significantly change and improve the function of big data analysis in a variety of fields. When compared to traditional methods, machine learning provides significant advantages in complex problem solving, computing performance, uncertainty propagation and handling, and decision support. In this paper, we present a novel end-to-end strategy for improving the overall accuracy of earthquake detection by simultaneously improving each step of the detection pipeline. In addition, we propose a Conv2D convolutional neural network (CNN) architecture for processing seismic waveforms collected across a geophysical system. The proposed Conv2D method for earthquake detection was compared to various machine-learning approaches and state-of-the-art methods. All of the methods used were trained and tested on real data collected in Kazakhstan over the last 97 years, from 1906 to 2022. The proposed model outperformed the other models with accuracy, precision, recall, and f-score scores of 63%, 82.4%, 62.7%, and 83%, respectively. Based on the results, it is possible to conclude that the proposed Conv2D model is useful for predicting realworld earthquakes in seismic zones.

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

Cited by 12 publications

References 30 publications

Research on Earthquake Discrimination and Magnitude Prediction Based on Fourier Power Spectrum Sample Entropy and Machine Learning Algorithm

Research on Earthquake Discrimination and Magnitude Prediction Based on Fourier Power Spectrum Sample Entropy and Machine Learning Algorithm

A Quality-of-Service-Aware Service Composition Method in the Internet of Things Using a Multi-Objective Fuzzy-Based Hybrid Algorithm

2-D Deep Convolutional Neural Network for Predicting the Intensity of Seismic Events

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