Next-Gen Forest Fire Surveillance Integrating IoT, AI, and Edge Computing for Rapid Detection

Forest fires are a chronic threat to the world's ecosystems, causing enormous damage and loss of life. Conventional methods of forest fire detection are mostly based on manual observation or scattered sensor networks, which result in late responses. This paper "Intelligent Forest Fire Monitoring: An Edge Computing Approach with Multimodal Sensor Fusion and Machine Learning" proposes a new forest fire detection system based on IoT technologies and AI-driven models for edge computing environments. The system uses a network of sensors with humidity, temperature, smoke, and acoustic sensors to collect real- time data for processing. Though the ongoing stage is optimizing and integrating AI models for detection, the architecture has been made scalable and deployable on power-limited edge devices such as Raspberry Pi. This allows feasibility in areas away from the grid with restricted power supply, facilitating real-time processing and faster alerts. The models utilize fuzzy logic for analysis of environmental data and SVM-based classifiers for pattern recognition of acoustics. Model fusion approaches also provide robust and accurate fire detection, while outputs are sent to the frontend dashboard using MQTT or HTTP protocols. This research illustrates the efficacy of the proposed method in improving detection accuracy while laying the foundation for future implementation on edge devices for real-time monitoring of forest fires.