## 1 Introduction
By using edge and fog computing, this data can be analyzed in real-time, allowing for EWS to be put in place and emergency responders to be deployed more quickly [80]. Similarly, sensors can be used to detect seismic activity and predict earthquakes, with data processed locally to provide early warning and minimize damage. Overall, fog and edge computing play an important role in natural disaster detection and management by enabling real-time data processing and analysis at the edge of the network [81]. This approach can help improve the speed and accuracy of disaster response, ultimately leading to better outcomes for affected communities. Verification and validation (V&V) techniques are crucial for ensuring the quality, reliability, and security of software systems in the context of IoT and cloud computing [82; 83]. These systems involve a complex network of devices, sensors, and services that must work together seamlessly and securely [84]. V&V techniques provide a framework for testing and validating these systems, ensuring that they meet the specified requirements and perform as intended. By implementing V&V techniques, developers can identify and correct defects and errors before they cause significant problems, ultimately leading to higher quality and more reliable IoT and cloud systems [85]. In [86], the authors reviewed geospatial and remote sensing technologies in earthquake research and disaster management, analyzing their historical and future applications, limitations, and methodologies. It provides a framework for earthquake hazard, vulnerability, and risk analysis using geospatial technologies. In [87], the study examined remote sensing applications, including Landsat satellite imaging, LiDAR, optical satellite photography, InSAR, and DEMETER in earthquake research. Many other studies [88; 89; 90] explore the role of IoT in disaster management and compares IoT-based options for various calamities. It highlights IoT EWS for fires and earthquakes and advises stakeholders on leveraging IoT technology to secure smart cities' infrastructure and minimize risks. The studies also evaluate Caribbean DRM (Disaster and Risk Management) systems, emphasizing the need for technology and new methods in monitoring disaster risks in small island states. It assesses technology in the five DRM pillars and proposes improvements for technology adoption in the Caribbean subregion. The research contributes to the global discussion on technology and innovation in DRM and addresses sustainable development concerns in Caribbean SIDS (Small Islands Developing States). The review paper [91] explores building damage mapping techniques in post-earthquake scenarios, emphasizing machine learning (ML) and deep learning frameworks. It addresses the drawbacks of manual interpretation of remote sensing imagery and identifies research gaps. The study of [92] reviews remote sensing methods for earthquake risk assessment, highlighting the importance of vulnerability assessment and the need for a comprehensive approach. In [93], satellite remote sensing technology for EEWS is suggested to achieve more improvements for EWS. The research of [94] examines post-earthquake damage investigation using optical remote sensing data and change detection algorithms, discussing their challenges and potential. The authors in [95] analyze how emerging technologies improve disaster management processes and call for further investigation. Lastly, the work done in [96] presents a procedure for managing pre- and post-earthquake stages of structure management using digital tools and emphasizes the role of BIM models and IDM standards. Compared to previous works, our paper presents a comprehensive overview of the role of EEWS in disaster assessment and relief, specifically focusing on the use of IoT networks and cloud infrastructure. The paper provides fundamental concepts about seismic waves and associated signal processing, details on the EEWS IoT system, and a taxonomy of EEWSapproaches using emerging IoT and cloud facilities.