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Data Mining Techniques Applied to Wireless Sensor Networks for Early Forest Fire Detection
Date Issued
2016-03-23
Date Available
2017-05-08T12:12:19Z
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 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.
Other Sponsorship
French National Research Agency
Type of Material
Conference Publication
Publisher
ACM
Copyright (Published Version)
2016 ACM
Language
English
Status of Item
Peer reviewed
Part of
Boubiche, D.E., Hidoussi, F., Guezouli, L., Bounceur, A. and Toral Cruz, H. (eds.). Proceedings of the International Conference on Internet of things and Cloud Computing (ICC '16), Article 71
Conference Details
International Conference on Internet of things and Cloud Computing (ICC '16), Cambridge, UK, 22-23 March 2016
ISBN
9781450340632
This item is made available under a Creative Commons License
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Data Mining Techniques applied to wireless sensor networks to Early forest fire detection.pdf
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Format
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