A Comparative and Analytical Review of Iot-Enabled Smart Accidental Management Systems

Abstract

One of the most important issues that emerging nations are addressing is road accidents. It is important to develop smart accidental management systems with low cost and efforts to prevent accidents and causalities. The amalgamation of Intelligent Transportation Systems (ITS) and Information and Communications Technology (ICT) is expected to dramatically change how people experience driving by enabling cutting-edge traffic monitoring and incident detection strategies. This analysis focuses on various components of SAMS, such as sensor networks, communication protocols, data processing techniques, and decision-making algorithms. It examines how these components work together to create a connected infrastructure capable of detecting and responding to accidents promptly. The review highlights the role of data analytics in enhancing accident prediction and prevention. By processing and analyzing enormous real-time data from cameras, sensors, and other sources, IoT-driven SAMS can identify patterns and anomalies, allowing for proactive measures to avoid accidents in various settings, including transportation, industries, and public spaces.