학술논문

Energy-Efficient Data Mining Techniques for Emergency Detection in Wireless Sensor Networks
Document Type
Conference
Source
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) UIC-ATC-SCALCOM-CBDCOM-IOP-SMARTWORLD 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), 2016 Intl IEEE Conferences. :766-771 Jul, 2016
Subject
Communication, Networking and Broadcast Technologies
Computing and Processing
Wireless sensor networks
Logic gates
Routing
Data mining
Monitoring
Energy consumption
Clustering algorithms
Fire detection
Data Mining
Intelligent Decision Making
Language
Abstract
Event detection is an important part in many Wireless Sensor Network (WSN) applications such as forest fire, environmental pollution. In this kind of applications, the event must be detected early in order to reduce the threats, 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, 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.