A Cloud-IoT Architecture for Latency-Aware Localization in Earthquake Early Warning.

Area of Science:

• Geophysics and Seismology
• Computer Science and Engineering
• Internet of Things (IoT)

Background:

• Effective earthquake early warning (EEW) systems rely on rapid and accurate earthquake source detection.
• Existing epicenter localization solutions have limited integration within the Internet of Things (IoT) framework and cloud platforms.
• Traditional regional EEW approaches often lack decentralized data processing capabilities.

Purpose of the Study:

• To propose and validate a complete IoT architecture for earthquake detection, localization, and notification.
• To implement P-wave picking directly on IoT devices for faster seismic data analysis.
• To integrate IoT devices with cloud platforms for enhanced data management and service integration.

Main Methods:

• Designed and deployed a novel IoT architecture for seismic event detection and localization.
• Implemented P-wave picking on edge IoT devices, deviating from traditional EEW methods.
• Utilized cloud-based services for pick association, source localization (hyperbola method with time difference of arrival multilateration), event declaration, and user notification.

Main Results:

• The proposed end-to-end IoT architecture provides rapid earthquake epicenter location estimates with acceptable accuracy for EEW.
• Rigorous testing against Italy's regional EEW standard demonstrated a significant 3.39-second improvement in localization speed.
• The system successfully integrates IoT devices with cloud platforms, simplifying data storage and device management.

Conclusions:

• The developed IoT architecture offers a viable and efficient solution for earthquake early warning systems.
• Decentralized P-wave picking on IoT devices enhances the speed and reliability of seismic alerts.
• The proposed system represents a significant advancement in leveraging IoT and cloud technologies for disaster preparedness.