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Early Detection of Earthquakes Using IoT and Cloud Infrastructure: A Survey

Author

Listed:
  • Mohamed S. Abdalzaher

    (Department of Seismology, National Research Institute of Astronomy and Geophysics, Cairo 11421, Egypt)

  • Moez Krichen

    (Faculty of Computer Science and Information Technology, Al-Baha University, Al-Baha 65528, Saudi Arabia
    ReDCAD Laboratory, National School of Engineers of Sfax, University of Sfax, Sfax 3029, Tunisia)

  • Derya Yiltas-Kaplan

    (Department of Computer Engineering, Faculty of Engineering, Istanbul University-Cerrahpaşa, Istanbul 34320, Türkiye)

  • Imed Ben Dhaou

    (Department of Computer Science, Hekma School of Engineering, Computing and Informatics, Dar Al-Hekma University, Jeddah 22246, Saudi Arabia
    Department of Computing, University of Turku, 20500 Turku, Finland
    Higher Institute of Computer Sciences and Mathematics, Department of Technology, University of Monastir, Monastir 5000, Tunisia)

  • Wilfried Yves Hamilton Adoni

    (Helmholtz-Zentrum Dresden-Rossendorf, Center for Advanced Systems Understanding, Untermarkt 20, 02826 Görlitz, Germany
    Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Chemnitzer Str. 40, 09599 Freiberg, Germany)

Abstract

Earthquake early warning systems (EEWS) are crucial for saving lives in earthquake-prone areas. In this study, we explore the potential of IoT and cloud infrastructure in realizing a sustainable EEWS that is capable of providing early warning to people and coordinating disaster response efforts. To achieve this goal, we provide an overview of the fundamental concepts of seismic waves and associated signal processing. We then present a detailed discussion of the IoT-enabled EEWS, including the use of IoT networks to track the actions taken by various EEWS organizations and the cloud infrastructure to gather data, analyze it, and send alarms when necessary. Furthermore, we present a taxonomy of emerging EEWS approaches using IoT and cloud facilities, which includes the integration of advanced technologies such as machine learning (ML) algorithms, distributed computing, and edge computing. We also elaborate on a generic EEWS architecture that is sustainable and efficient and highlight the importance of considering sustainability in the design of such systems. Additionally, we discuss the role of drones in disaster management and their potential to enhance the effectiveness of EEWS. Furthermore, we provide a summary of the primary verification and validation methods required for the systems under consideration. In addition to the contributions mentioned above, this study also highlights the implications of using IoT and cloud infrastructure in early earthquake detection and disaster management. Our research design involved a comprehensive survey of the existing literature on early earthquake warning systems and the use of IoT and cloud infrastructure. We also conducted a thorough analysis of the taxonomy of emerging EEWS approaches using IoT and cloud facilities and the verification and validation methods required for such systems. Our findings suggest that the use of IoT and cloud infrastructure in early earthquake detection can significantly improve the speed and effectiveness of disaster response efforts, thereby saving lives and reducing the economic impact of earthquakes. Finally, we identify research gaps in this domain and suggest future directions toward achieving a sustainable EEWS. Overall, this study provides valuable insights into the use of IoT and cloud infrastructure in earthquake disaster early detection and emphasizes the importance of sustainability in designing such systems.

Suggested Citation

  • Mohamed S. Abdalzaher & Moez Krichen & Derya Yiltas-Kaplan & Imed Ben Dhaou & Wilfried Yves Hamilton Adoni, 2023. "Early Detection of Earthquakes Using IoT and Cloud Infrastructure: A Survey," Sustainability, MDPI, vol. 15(15), pages 1-38, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:15:p:11713-:d:1205703
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    References listed on IDEAS

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    1. Leogrande, Angelo, 2024. "Strategie innovative per la logistica: il valore del kitting e assembly nel settore idrotermosanitario," MPRA Paper 122746, University Library of Munich, Germany.
    2. Pedro Gomes & Tiago Cardoso, 2024. "Utilização de IA para previsão de desastres naturais em Portugal," GEE Papers 185, Gabinete de Estratégia e Estudos, Ministério da Economia, revised Sep 2024.

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