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

Paola Pierleoni1, Roberto Concetti2, Alberto Belli1

  • 1Department of Information Engineering (DII), Università Politecnica delle Marche, 60131 Ancona, Italy.

Sensors (Basel, Switzerland)
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Summary

This study introduces an innovative Internet of Things (IoT) architecture for earthquake early warning (EEW) systems. The new design enables faster earthquake detection and localization by processing data on IoT devices, improving seismic alert speed.

Keywords:
Internet of Thingscloud computingearly warning systemsearthquake localization

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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.