Data mining techniques applied to wireless sensor networks for early forest fire detection

Massinissa Saoudi; Ahcène Bounceur; Reinhardt Euler; Tahar Kechadi

doi:10.1145/2896387.2900323

Data mining techniques applied to wireless sensor networks for early forest fire detection

Massinissa Saoudi, Ahcène Bounceur, Reinhardt Euler, Tahar Kechadi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

25 Scopus citations

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 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 corresponding 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.

Original language	English
Title of host publication	Proceedings of the International Conference on Internet of Things and Cloud Computing, ICC 2016
Editors	Lyamine Guezouli, Homero Toral Cruz, Faouzi Hidoussi, Djallel Eddine Boubiche, Ahcene Bounceur
Publisher	Association for Computing Machinery
ISBN (Electronic)	9781450340632
DOIs	https://doi.org/10.1145/2896387.2900323
State	Published - 22 Mar 2016
Externally published	Yes
Event	International Conference on Internet of Things and Cloud Computing, ICC 2016 - Cambridge, United Kingdom Duration: 22 Mar 2016 → 23 Mar 2016

Publication series

Name	ACM International Conference Proceeding Series
Volume	22-23-March-2016

Conference

Conference	International Conference on Internet of Things and Cloud Computing, ICC 2016
Country/Territory	United Kingdom
City	Cambridge
Period	22/03/16 → 23/03/16

Bibliographical note

Publisher Copyright:
© 2016 ACM.

Keywords

Data mining
Fire detection
Intelligent decision making
Wireless sensor networks

ASJC Scopus subject areas

Software
Human-Computer Interaction
Computer Vision and Pattern Recognition
Computer Networks and Communications

Access to Document

10.1145/2896387.2900323

Cite this

Saoudi, M., Bounceur, A., Euler, R., & Kechadi, T. (2016). Data mining techniques applied to wireless sensor networks for early forest fire detection. In L. Guezouli, H. T. Cruz, F. Hidoussi, D. E. Boubiche, & A. Bounceur (Eds.), Proceedings of the International Conference on Internet of Things and Cloud Computing, ICC 2016 Article a71 (ACM International Conference Proceeding Series; Vol. 22-23-March-2016). Association for Computing Machinery. https://doi.org/10.1145/2896387.2900323

Saoudi, Massinissa ; Bounceur, Ahcène ; Euler, Reinhardt et al. / Data mining techniques applied to wireless sensor networks for early forest fire detection. Proceedings of the International Conference on Internet of Things and Cloud Computing, ICC 2016. editor / Lyamine Guezouli ; Homero Toral Cruz ; Faouzi Hidoussi ; Djallel Eddine Boubiche ; Ahcene Bounceur. Association for Computing Machinery, 2016. (ACM International Conference Proceeding Series).

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title = "Data mining techniques applied to wireless sensor networks for early forest fire detection",

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 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 corresponding 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.",

keywords = "Data mining, Fire detection, Intelligent decision making, Wireless sensor networks",

author = "Massinissa Saoudi and Ahc{\`e}ne Bounceur and Reinhardt Euler and Tahar Kechadi",

note = "Publisher Copyright: {\textcopyright} 2016 ACM.; International Conference on Internet of Things and Cloud Computing, ICC 2016 ; Conference date: 22-03-2016 Through 23-03-2016",

year = "2016",

month = mar,

day = "22",

doi = "10.1145/2896387.2900323",

language = "English",

series = "ACM International Conference Proceeding Series",

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Saoudi, M, Bounceur, A, Euler, R & Kechadi, T 2016, Data mining techniques applied to wireless sensor networks for early forest fire detection. in L Guezouli, HT Cruz, F Hidoussi, DE Boubiche & A Bounceur (eds), Proceedings of the International Conference on Internet of Things and Cloud Computing, ICC 2016., a71, ACM International Conference Proceeding Series, vol. 22-23-March-2016, Association for Computing Machinery, International Conference on Internet of Things and Cloud Computing, ICC 2016, Cambridge, United Kingdom, 22/03/16. https://doi.org/10.1145/2896387.2900323

Data mining techniques applied to wireless sensor networks for early forest fire detection. / Saoudi, Massinissa; Bounceur, Ahcène; Euler, Reinhardt et al.
Proceedings of the International Conference on Internet of Things and Cloud Computing, ICC 2016. ed. / Lyamine Guezouli; Homero Toral Cruz; Faouzi Hidoussi; Djallel Eddine Boubiche; Ahcene Bounceur. Association for Computing Machinery, 2016. a71 (ACM International Conference Proceeding Series; Vol. 22-23-March-2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Saoudi, Massinissa

AU - Bounceur, Ahcène

AU - Euler, Reinhardt

AU - Kechadi, Tahar

PY - 2016/3/22

Y1 - 2016/3/22

N2 - 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 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 corresponding 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.

AB - 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 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 corresponding 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.

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Saoudi M, Bounceur A, Euler R, Kechadi T. Data mining techniques applied to wireless sensor networks for early forest fire detection. In Guezouli L, Cruz HT, Hidoussi F, Boubiche DE, Bounceur A, editors, Proceedings of the International Conference on Internet of Things and Cloud Computing, ICC 2016. Association for Computing Machinery. 2016. a71. (ACM International Conference Proceeding Series). doi: 10.1145/2896387.2900323

Data mining techniques applied to wireless sensor networks for early forest fire detection

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this