Name
Title | ||
|---|---|---|
Data Mining Techniques Applied to Wireless Sensor Networks for Early Forest Fire Detection. |
Abstract | ||
|---|---|---|
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 present a new approach for forest fire detection based on the integration of Data Mining techniques into sensor nodes. The idea is to use a 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 corresponding 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. 1 |
Year | Venue | Field |
|---|---|---|
2016 | ICC 2016 | Sensor node,Early detection,Data mining,Intelligent decision making,Monitoring system,Computer science,Computer security,Classifier (linguistics),Fire detection,Wireless sensor network |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Massinissa Saoudi | 1 | 8 | 1.87 |
| Ahcène Bounceur | 2 | 306 | 35.05 |
| Reinhardt Euler | 3 | 95 | 28.50 |
| M. Tahar Kechadi | 4 | 326 | 59.59 |