Cloud-fog-based approach for smart wildfire monitoring

Abstract

Wildfires have disturbed the delicate balance of the Earth's ecosystem, exacerbating global warming, floods, droughts, and causing a reduction in oxygen level. Early detection of wildfires is crucial in order to mitigate the risk of losses. The Internet of Things (IoT) has emerged as a critical tool in bridging disparate technologies, allowing for the real-time collection of data from sensors and further analysis of factors influencing the occurrence of wildfires (FIWs) on fog devices or cloud servers. However, most deployment of these sensor nodes in hostile and unmanned environments presents significant challenges to the reliability information collection of FIWs and the effectiveness of energy-conserving technologies. To address these challenges, this paper proposes a novel wildfire monitoring model based on an IoT-Fog-Cloud three-tier architecture. This model incorporates energy-conserving and fault-tolerant scheduling algorithms in the fog layer, ensuring that sensor nodes operate reliably. In the cloud layer, the multi-Grained Cascade Forest (GC-Forest) algorithm is utilized to analyze both spectral and FIWs data. Furthermore, in order to accurately assess the spread trend of forest fires, the Wang Zhengfei model is improved. Experimental results demonstrate that the energy-conserving algorithm reduces energy consumption by approximately 25.48%, while the fault-tolerant scheduling algorithm remains a fault detection rate of over 98.7%. The GC-Forest algorithm exhibits a low error rate of only 16.25%, with a hit rate of 83.75%, and a Hanssen-Kuiper skill score of 63.73%.