Adaptive Weighted Fusion Algorithm for Monitoring System of Forest Fire Based on Wireless Sensor Networks

Forest fires have drawn increasing attention in recent years due to their tremendous effects on environment, humans and wild life, ecosystem function, weather, and climate. Accurate monitoring of forest fires field is important since it contributes in fire effects assessing and controlling. This study attempts to apply adaptive weighted fusion algorithm in a wireless sensor networks (WSNs) system for the forest fire monitoring. These practice wireless sensor network circuits include temperature, humidity and ultraviolet variable measurement components. These data fields of each sensor nodes contain the properties and specifications of that signal process rules. The temperature and humidity sensor node data are obtained through experiments, simulation results show that t abnormal messages of initial stages of fires are thoroughly detected, and the method proposed in this paper adopts the classical self-adapting weighting fusion algorithm to increase the reliability of fire characteristics recognition.