Wireless Sensor Network based Integrated LEACH Protocol with YOLO and Feed-Forward Resnet for Detecting Real Time Forest Fire to Promote Environmental Sustainability

The real-time detection and suppression of forest fires are addressed in this research using an integrated approach. YOLO and Feedforward ResNet are sophisticated deep learning models that are used with the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol within a wireless sensor network (WSN). By using the dispersed sensor nodes of the WSN, LEACH optimizes energy use and assigns cluster heads for effective data reporting. A real-time object detection system called YOLO analyses pictures taken by cameras that are positioned in key places to recognize fires and smoke. YOLO is enhanced by ResNet's effectiveness. In this configuration, data is transmitted from sensor nodes to cluster heads, who then pass it on to a central station for in-the-moment processing. As soon as a forest fire is discovered, alarms are immediately transmitted to the relevant authorities. The system's efficacy is confirmed through deployments in the actual world and simulations, which minimize false positives. By enabling speedier reactions, reducing the impact of forest fires, and safeguarding natural resources, this integration improves environmental sustainability, constituting an important step in the direction of environmental preservation.