Data Mining Techniques Applied to Wireless Sensor Networks for Early Forest Fire Detection

Nowadays, forest fires are a serious threat to the environment and human life. The monitoring system for forest fires should be able to make a real-time monitoring of the target region and the early detection of fire threats. In this paper, we propose a new approach based on the integration of Data Mining techniques into sensor nodes for forest fire detection. This approach is based on the clustered WSN where each sensor node will individually decide on detecting fire using a classifier of Data Mining techniques. When a fire is detected, the correspondent node will send an alert through its cluster-head which will pass through gateways and other cluster-heads until it will reach the sink in order to inform the firefighters. We use the CupCarbon simulator to validate and evaluate our proposed approach. Through extensive simulation experiments, we show that our approach can provide a fast reaction to forest fires while consuming energy efficiently.