WILDFIRE DETECTION IN DRY FORESTS USING WSN-IOT SENSORS AND K-MEANS ALGORITHM

Abstract

Abstract- This paper presents the methodology employed for optimal deployment of Wireless Sensor Network (WSN) IoT nodes used for wildfire detection. The methodology focuses on utilizing the k-means clustering algorithm for determining the most efficient positions of WSN nodes. This chapter is organized into sections describing the problem statement, data collection, k-means clustering algorithm, performance evaluation, and the wildfire detection process. The primary objective of this study is to optimize the deployment of WSN IoT nodes in a specific geographic area to improve the accuracy and efficiency of wildfire detection. The problem involves determining the best positions for WSN nodes so that they can effectively monitor and report the occurrence of wildfires while minimizing energy consumption and communication latency.