Abstract
Forest fires are a major global concern, and the use of wireless sensor networks (WSNs) has emerged as a promising solution for prevention.The purpose of this article is to propose a WSN model based on the ZigBee protocol for forest fire monitoring and prevention.Existing methods and systems for forest fire hazard forecasting and monitoring are analyzed, and the advantages of the proposed approach are presented.The system employs self-powered wireless sensors to monitor environmental parameters such as carbon monoxide concentration, temperature, and humidity in real-time.The proposed system structure, which is an adaptation of the clustered tree topology, is more memory-efficient and easier to create than the traditional reticular structure.The performance and usefulness of the neural system are illustrated, and it is suggested that it can be used as an addition to existing methods for monitoring and preventing forest fires.Future work will address issues such as power consumption, node placement, and clock synchronization to improve the technology for monitoring forest fires.