International Journal of Environmental Engineering and Development
E-ISSN: 2945-1159
Volume 2, 2024
Internet of Things (IoT) for Remote Earthquake and Fire Detection Monitoring: Linking Safety
Author:
Abstract: Two examples of interconnected systems and gadgets that have evolved as a result of fast technological developments are the Internet of Things (IoT) and the Internet of Vehicles (IoV). The public safety and catastrophe management sectors are only two of many that may see revolutionary changes brought forth by this technology. An Internet of Things (IoT) and Internet of Vehicles (IoV)-based safety system for remote real-time monitoring and seismic detection is proposed in this study. To monitor seismic activity and detect impending fires, the proposed system employs a network of sensors installed in various buildings and vehicles. Among the many data-gathering instruments included in these sensors are vibration sensors, which can detect earthquakes, and smoke detectors, which may detect fires. The acquired data is received by a centralised control unit using wireless transmission. In order to accurately detect earthquakes and fires, the control unit processes the provided data using advanced data analytics and machine learning techniques. The system also makes use of the IoV concept to make the reaction measures more efficient and effective. It utilises the real-time position and trajectory data of cars to aid in rapid emergency response and optimise evacuation routes. There are several advantages to the proposed method over more traditional forms of monitoring and safety systems. Authorities can respond swiftly to earthquakes and fires because to its real-time monitoring capabilities, which in turn reduces the likelihood of human deaths and property damage. Improving the system's responsiveness, the integration of IoV enables intelligent decision-making based on the varying traffic circumstances and available resources.
Search Articles
Keywords: remote monitoring, real-time safety system, seismic and fire detection, data analytics, ML, rescuing from disasters, and optimising evacuations
Pages: 290-295
DOI: 10.37394/232033.2024.2.26