Study on Integrated Systems for Enhanced Forest Fire Prevention: An Embedded Intelligent Video Analysis and Collaborative IoT–Fog–Cloud Framework Approach

Abstract

Forest fires have been a major hazard to forest management, needing sophisticated monitoring and management techniques. By creating an embedded intelligent video analysis system, this research proposed a complete strategy for addressing this difficulty. The system's hardware architecture was explained, and the operating system software was detailed, using a software and hardware design based on the ZynqSoC. At the same time, an emphasis on forest fire prevention applications was maintained. Furthermore, the study investigated a unique technique for forest fire detection using Arduino as a field data collector and a fuzzy logic algorithm to improve accuracy. The proposed IoT-Fog-Cloud collaboration infrastructure offered a patented contribution to real-time wildfire monitoring, prediction, and forecasting. The framework achieved excellent accuracy in determining wildfire proneness levels and real-time alert production by utilizing fuzzy K-nearest-neighbor classification and Holt-Winter's forecasting model. The findings demonstrated the integrated system's ability to reduce the impact of wildfires, serving as a significant reference for future forest fire prevention scenarios.