IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i15p11713-d1205703.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/15/11713/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/15/11713/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bello, Omar & Fontes de Meira, Luciana, 2020. "The use of technology and innovative approaches in disaster and risk management: a characterization of Caribbean countries’ experiences," Studies and Perspectives – ECLAC Subregional Headquarters for The Caribbean 45990, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
    2. Christian Geiß & Hannes Taubenböck, 2013. "Remote sensing contributing to assess earthquake risk: from a literature review towards a roadmap," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 68(1), pages 7-48, August.
    3. Mohamed S. Abdalzaher & Hussein A. Elsayed & Mostafa M. Fouda & Mahmoud M. Salim, 2023. "Employing Machine Learning and IoT for Earthquake Early Warning System in Smart Cities," Energies, MDPI, vol. 16(1), pages 1-22, January.
    4. A. Reyana & Sandeep Kautish & Khalid Abdulaziz Alnowibet & Hossam M. Zawbaa & Ali Wagdy Mohamed, 2023. "Opportunities of IoT in Fog Computing for High Fault Tolerance and Sustainable Energy Optimization," Sustainability, MDPI, vol. 15(11), pages 1-14, May.
    5. Hirokazu Moriya, 2021. "Identification of Similar Seismic Waves Using the Phase-Only Correlation Function and Wavelet Transform," Energies, MDPI, vol. 14(15), pages 1-11, July.
    6. Matt Luckcuck, 2023. "Using formal methods for autonomous systems: Five recipes for formal verification," Journal of Risk and Reliability, , vol. 237(2), pages 278-292, April.
    7. Yangyang Yue & Yiqing Lv, 2023. "A Machine Learning-Based Decision Support System for Predicting and Repairing Cracks in Undisturbed Loess Using Microbial Mineralization and the Internet of Things," Sustainability, MDPI, vol. 15(10), pages 1-18, May.
    8. Angelin Blessy & Avneesh Kumar & Prabagaran A & Abdul Quadir Md & Abdullah I. Alharbi & Ahlam Almusharraf & Surbhi B. Khan, 2023. "Sustainable Irrigation Requirement Prediction Using Internet of Things and Transfer Learning," Sustainability, MDPI, vol. 15(10), pages 1-20, May.
    9. Tzu-Hsuan Lin & Jing-Ting Huang & Alan Putranto, 2022. "Integrated smart robot with earthquake early warning system for automated inspection and emergency response," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(1), pages 765-786, January.
    10. Erik L. Olson & Richard M. Allen, 2005. "The deterministic nature of earthquake rupture," Nature, Nature, vol. 438(7065), pages 212-215, November.
    11. Gemma Cremen & Carmine Galasso & Elisa Zuccolo, 2022. "Investigating the potential effectiveness of earthquake early warning across Europe," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    12. Kaljot Sharma & Darpan Anand & Munish Sabharwal & Pradeep Kumar Tiwari & Omar Cheikhrouhou & Tarek Frikha, 2021. "A Disaster Management Framework Using Internet of Things-Based Interconnected Devices," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-21, May.
    13. Omar Hamdy & Hanan Gaber & Mohamed S. Abdalzaher & Mahmoud Elhadidy, 2022. "Identifying Exposure of Urban Area to Certain Seismic Hazard Using Machine Learning and GIS: A Case Study of Greater Cairo," Sustainability, MDPI, vol. 14(17), pages 1-24, August.
    14. Mohamed S. Abdalzaher & Mostafa M. Fouda & Ahmed Emran & Zubair Md Fadlullah & Mohamed I. Ibrahem, 2023. "A Survey on Key Management and Authentication Approaches in Smart Metering Systems," Energies, MDPI, vol. 16(5), pages 1-27, March.
    15. Mohamed S. Abdalzaher & Mostafa M. Fouda & Mohamed I. Ibrahem, 2022. "Data Privacy Preservation and Security in Smart Metering Systems," Energies, MDPI, vol. 15(19), pages 1-19, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mohamed S. Abdalzaher & Hussein A. Elsayed & Mostafa M. Fouda & Mahmoud M. Salim, 2023. "Employing Machine Learning and IoT for Earthquake Early Warning System in Smart Cities," Energies, MDPI, vol. 16(1), pages 1-22, January.
    2. Mohamed S. Abdalzaher & Mostafa M. Fouda & Ahmed Emran & Zubair Md Fadlullah & Mohamed I. Ibrahem, 2023. "A Survey on Key Management and Authentication Approaches in Smart Metering Systems," Energies, MDPI, vol. 16(5), pages 1-27, March.
    3. Mohamed S. Abdalzaher & Sayed S. R. Moustafa & Mohamed Yassien, 2024. "Development of smoothed seismicity models for seismic hazard assessment in the Red Sea region," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(13), pages 12515-12544, October.
    4. Rui Zhang & Hao Zhang & Jianqiao Ye & Jiaqing Wang & Qing Liu & Shenghu Li, 2023. "Eigen-Sensitivity-Based Sliding Mode Control for LFO Damping in DFIG-Integrated Power Systems," Energies, MDPI, vol. 16(10), pages 1-18, May.
    5. Chankook Park & Wan Gyu Heo & Myung Eun Lee, 2024. "Study on Consumers’ Perceived Benefits and Risks of Smart Energy System," International Journal of Energy Economics and Policy, Econjournals, vol. 14(3), pages 288-300, May.
    6. Mahmoud M. Badr & Mohamed I. Ibrahem & Hisham A. Kholidy & Mostafa M. Fouda & Muhammad Ismail, 2023. "Review of the Data-Driven Methods for Electricity Fraud Detection in Smart Metering Systems," Energies, MDPI, vol. 16(6), pages 1-18, March.
    7. Tao Li & Jianqiang Luo & Kaitong Liang & Chaonan Yi & Lei Ma, 2023. "Synergy of Patent and Open-Source-Driven Sustainable Climate Governance under Green AI: A Case Study of TinyML," Sustainability, MDPI, vol. 15(18), pages 1-21, September.
    8. Kenneth Nwanua Ohei & Sam Lubbe, 2022. "Taking Full Advantage of the COVID-19 Era to Intensify the Use of Information and Communication Technology Tools in Higher Education Institutes," International Review of Management and Marketing, Econjournals, vol. 12(5), pages 21-32, September.
    9. Massimiliano Pittore & Marc Wieland & Kevin Fleming, 2017. "Perspectives on global dynamic exposure modelling for geo-risk assessment," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 86(1), pages 7-30, March.
    10. Hany Habbak & Mohamed Mahmoud & Khaled Metwally & Mostafa M. Fouda & Mohamed I. Ibrahem, 2023. "Load Forecasting Techniques and Their Applications in Smart Grids," Energies, MDPI, vol. 16(3), pages 1-33, February.
    11. Alejandro Valencia-Arias & Juana Ramírez Dávila & Wilmer Londoño-Celis & Lucia Palacios-Moya & Julio Leyrer Hernández & Erica Agudelo-Ceballos & Hernán Uribe-Bedoya, 2024. "Research Trends in the Use of the Internet of Things in Sustainability Practices: A Systematic Review," Sustainability, MDPI, vol. 16(7), pages 1-23, March.
    12. Kelum Sandamal & Sachini Shashiprabha & Nitin Muttil & Upaka Rathnayake, 2023. "Pavement Roughness Prediction Using Explainable and Supervised Machine Learning Technique for Long-Term Performance," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    13. Dianyou Yu & Zheng He, 2022. "Digital twin-driven intelligence disaster prevention and mitigation for infrastructure: advances, challenges, and opportunities," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 112(1), pages 1-36, May.
    14. Christian Geiß & Hannes Taubenböck, 2017. "One step back for a leap forward: toward operational measurements of elements at risk," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 86(1), pages 1-6, March.
    15. Rasheed Abdulkader & Hayder M. A. Ghanimi & Pankaj Dadheech & Meshal Alharbi & Walid El-Shafai & Mostafa M. Fouda & Moustafa H. Aly & Dhivya Swaminathan & Sudhakar Sengan, 2023. "Soft Computing in Smart Grid with Decentralized Generation and Renewable Energy Storage System Planning," Energies, MDPI, vol. 16(6), pages 1-24, March.
    16. Ganotakis, Panagiotis & Angelidou, Sofia & Saridakis, Charalampos & Piperopoulos, Panagiotis & Dindial, Miguel, 2023. "Innovation, digital technologies, and sales growth during exogenous shocks," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    17. Christoph Aubrecht & Patrick Meier & Hannes Taubenböck, 2017. "Speeding up the clock in remote sensing: identifying the ‘black spots’ in exposure dynamics by capitalizing on the full spectrum of joint high spatial and temporal resolution," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 86(1), pages 177-182, March.
    18. Hao Wu & Zhiping Cheng & Wenzhong Shi & Zelang Miao & Chenchen Xu, 2014. "An object-based image analysis for building seismic vulnerability assessment using high-resolution remote sensing imagery," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 71(1), pages 151-174, March.
    19. Giuseppe Musolino, 2024. "Methods for Risk Reduction: Modelling Users’ Updating Utilities in Urban Transport Networks," Sustainability, MDPI, vol. 16(6), pages 1-13, March.
    20. Rami Ahmad, 2024. "Smart remote sensing network for disaster management: an overview," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 87(1), pages 213-237, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:15:p:11713-:d:1205703. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.