A CIRCULAR LAYERED IOT-BASED FOREST FIRE DETECTION AND EARLY WARNING SYSTEM WITH EDGE-BASED VISION VERIFICATION

Abstract

Forest fires have become an increasing environmental threat, causing severe damage to ecosystems, wildlife, and human settlements. Early detection of fire, particularly in remote forest regions, remains a major challenge. This paper presents a circular layered IoT-based forest fire detection and early warning system that integrates environmental sensing with edge-level visual verification. Low-power sensor nodes continuously monitor parameters such as temperature, infrared radiation, and gas concentration. When abnormal conditions are detected, a camera-enabled head node is selectively activated to confirm fire presence using an embedded AI model. This conditional activation strategy reduces false alarms and improves energy efficiency. Confirmed fire events, along with location information, are transmitted to a base station for rapid alert generation. Experimental results indicate reliable detection with low latency and efficient power usage. The proposed system offers a practical and scalable solution for real-time forest fire monitoring.