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

Title: Data Mining Techniques Applied to Wireless Sensor Networks for Early Forest Fire Detection
Authors: Saoudi, Massinissa
Bounceur, Ahcene
Euler, Reinhardt
Kechadi, Tahar
Permanent link: http://hdl.handle.net/10197/8483
Date: 23-Mar-2016
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.
Type of material: Conference Publication
Publisher: ACM
Copyright (published version): 2016 ACM
Keywords: Machine learningStatisticsFire detectionWireless sensor networksData miningIntelligent decision making
DOI: 10.1145/2896387.2900323
Language: en
Status of Item: Peer reviewed
Is 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
Appears in Collections:Computer Science Research Collection
Insight Research Collection

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