Smart Data Mining Techniques for Emergency Detection Using Wireless Sensor Networks

Massinissa Saoudi (1) , Ahcène Bounceur (2, 3) , Reinhardt Euler (4) , Tahar Kechadi (5)

1 Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
2 UBO - Université de Brest
3 Lab-STICC_ENSTAB_ CACS_MOCS
4 Lab-STICC_UBO_CACS_MOCS
5 School of Computer Science and Informatics [Dublin]

Environmental emergencies such as forest fires present a serious threat to the environment and human life. The monitoring system of emergencies should be capable of early detection in order to reduce possible damage. 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 node will individually decide on detecting fire using a classifier of Data Mining techniques. When a fire is detected, the 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. The proposed approach is evaluated using the CupCarbon simulator. The simulation experiments show that our approach can provide fast detection of forest fires while consuming energy efficiently.

Domaines: Informatique [cs]

Massinissa Saoudi, Ahcène Bounceur, Reinhardt Euler, Tahar Kechadi. Smart Data Mining Techniques for Emergency Detection Using Wireless Sensor Networks. 11ème Colloque du GDR SoC-SiP, Jun 2016, Nantes, France. ⟨hal-02508022⟩