You are here

Energy-Efficient Data Mining Techniques for Emergency Detection in Wireless Sensor Networks


Massinissa Saoudi, Ahcène Bounceur, Reinhardt Euler, Tahar Kechadi, Alfredo Luzzocrea

Publication Type: 
Refereed Conference Meeting Proceeding
Event detection is an important part in many Wireless Sensor Network (WSN) applications such as forest fire and environmental pollution. In this kind of applications, the event must be detected early in order to reduce the threats and damages. In this paper, we propose a new approach for early forest fire detection, which is based on the integration of Data Mining techniques into sensor nodes. The idea is to partition the node set into clusters so that each node can individually detect fires using classification techniques. Once a fire is detected, the corresponding node will send an alert to its cluster-head. This alert will then be routed via gateways and other cluster-heads to the sink in order to inform the firefighters. The approach is validated using the CupCarbon simulator. The results show that our approach can provide a fast reaction to forest fires with efficient energy consumption
Conference Name: 
2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld)
Digital Object Identifer (DOI): 
Publication Date: 
National University of Ireland, Dublin (UCD)
Open access repository: