Intelligent IoT monitoring system using rule-based for decision supports in fired forest images

Abstract

Recently, many investigations focus on studying to detect of forest fires using IoT devices such as remote sensors or conventional fire detector sensors. However, supports in fire forest in real-time are hard for current studies in large forests. This paper has presented a novel approach to forest fire detection implemented using an improved rule-based integrated with k-means algorithm to improve the detection of forest fires. The rules in knowledge based can be considered in a camera as forest fires in real-time detection. The research explores the construction of Time-Lapse Videos from cluttered consecutive image. Mechanisms have been developed to automatically render the images with these elements from the scenes to produce more ‘truthful’ videos which more accurately describe of forest fires. The experimental results show that our proposed IoT monitoring system achieves significant improvements in ‘real-time’ fire detection.