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Data Mining Techniques Applied to Wireless Sensor Networks for Early Forest Fire Detection

Saoudi Massinissa Ahcène Bounceur 1, 2 Reinhardt Euler 3 Tahar Kechadi 4
1 Lab-STICC_UBO_CACS_MOCS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance, UBO - Université de Brest
3 Lab-STICC_UBS_CACS_MOCS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : Nowadays, forest fires are a serious threat to the environ- ment 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 cor- responding 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 ex- periments, we show that our approach can provide a fast reaction to forest fires while consuming energy efficiently.
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Conference papers
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https://hal.univ-brest.fr/hal-01294137
Contributor : Ahcène Bounceur <>
Submitted on : Sunday, March 27, 2016 - 4:15:37 PM
Last modification on : Wednesday, June 24, 2020 - 4:19:24 PM

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Saoudi Massinissa, Ahcène Bounceur, Reinhardt Euler, Tahar Kechadi. Data Mining Techniques Applied to Wireless Sensor Networks for Early Forest Fire Detection. International Conference on Internet of Things and Cloud Computing, Mar 2016, Cambridge, United Kingdom. ⟨10.1145/2896387.2900323⟩. ⟨hal-01294137⟩

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