Using the Internet of Things in Early Warning Systems

Abstract

This study aims to examine the role of the Internet of Things in enhancing early warning systems by analyzing how smart connected devices are used to collect and process environmental data in real time, thereby improving the prediction of natural disasters and reducing their impacts. The study adopts a descriptive-analytical approach through reviewing relevant literature and recent studies, as well as analyzing practical applications of IoT in disaster management.

The research addresses the theoretical framework of IoT, including its concept, components, and role in monitoring natural disasters such as floods, earthquakes, and wildfires through the use of smart sensors. It also explains the operational mechanism of early warning systems, which rely on data collection, transmission, analysis, and timely alert generation.

Furthermore, the study focuses on the integration between IoT and artificial intelligence, highlighting its contribution to improving prediction accuracy through big data analysis and pattern recognition. It also discusses the technical and security challenges associated with these systems and provides a comparative analysis between traditional and modern systems, demonstrating the superiority of IoT-based systems in terms of speed and accuracy.

The study concludes that IoT represents an effective tool for improving early warning systems, emphasizing the importance of developing digital infrastructure, strengthening cybersecurity, and supporting scientific research to maximize the benefits of these technologies.