Multisensor Information Fusion and Optimization Technique for Wildfire Detection and Prevention in WSN Environment

Abstract

Monitoring natural traits like temperature and humidity are imperative for evaluating climate change. Remote sensing applications in wireless sensor networks face a crucial issue in transmitting the information from sensors in a continuous period to the gateway nodes for processing. Correspondingly, sensor nodes are subject to low memory and processing limitations in concern to wireless sensor networks. Wildfires can be started intentionally or unintentionally. The result, however, is disastrous, causing irreversible damage to the ecosystem. With the advancement of intelligent sensor-based control technologies, data fusion and classification have become an essential component of the environmental monitoring system. To predict the occurrence of fire in forests, a fast and effective intelligent control system using Adaptive Decentralized Kalman Filter is proposed. The Decision tree classification can successfully solve problems on the side of the sink node. Thus the described Decision tree classification determines whether fire detection parameters are in the acceptable range and further utilizes a fuzzy-based optimization to improve the performance of the considered complex fusion environment. The experimental results of the proposed model have a detection rate of 98.3 %, demonstrating that the proposed technique is robust. Thus, providing real-time monitoring of certain environmental variables for continuous situational awareness and instant responsiveness.