Optimizing Sensor Node Placement for Forest Fire Prevention Using Clustering and Regression

Abstract

Forest fire monitoring and prevention are essential in forested areas, where the strategic deployment of sensor networks plays a pivotal role. This research study presents an innovative approach to optimize sensor node placement in forest fire prevention systems. The method utilizes the influence of meteorological conditions and incorporates both K-means clustering based on density and height analysis and linear regression employing 1/height. It harnesses unsupervised learning algorithms to proficiently cluster trees based on their density and height within specific geographical regions, considering meteorological factors such as temperature, humidity, and wind speed. These identified tree clusters play a significant role in strategically determining optimal sensor node placements, ensuring comprehensive coverage while minimizing the number of sensors required. This approach contributes to enhancing forest fire prevention and management by enabling swift detection and response to potential fire outbreaks, thereby mitigating damages and losses