IoT Fog Based Framework to Predict Forest Fire

Forest Fires are great disasters that cause deadly threat to the entire species of animals and plants living in that forest. It also harms the natural resources. Therefore, multiple approaches that provide the real time monitoring of forest should be taken for the safety of the forest. This work uses Fog-assisted IoT-enabled framework for early prediction and forecasting of forest fires, with IoT sensors embedded in forests to monitor and collect real time data. Fog computing acts as a bridge between hardware and remote servers. This work intends to locally process the data in the Fog server to predict fire, then this data will be sent to the cloud for further analysis and storage. This data can be used by forest officials for saving forest. The prediction of forest fire is carried out by machine learning techniques such LDA, K-Nearest neighbour, SVM, Boosted Trees, Bagged Trees.