Intelligent Data Mining Techniques for Emergency Detection in Wireless Sensor Networks

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Title: Intelligent Data Mining Techniques for Emergency Detection in Wireless Sensor Networks
Authors: Saoudi, MassinissaBounceur, AhcèneEuler, ReinhardtKechadi, Taharet al.
Permanent link: http://hdl.handle.net/10197/8206
Date: 21-Jul-2016
Online since: 2016-12-09T16:38:51Z
Abstract: Event detection is an important part in many Wireless Sensor Network (WSN) applications such as forest fire and environmental pollution. In this kind of applications, the event must be detected early in order to reduce the threats and 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 and 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.
Funding Details: French National Research Agency (ANR)
Type of material: Conference Publication
Publisher: IEEE
Copyright (published version): 2016 IEEE
Keywords: OptimisationDecision analytics
Other versions: https://cbdcom2016.sciencesconf.org/
Language: en
Status of Item: Peer reviewed
Conference Details: 2nd IEEE International Conference on Cloud and Big Data Computing (CBDCom 2016), Toulouse, France, 18-21 July
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:Computer Science Research Collection
Insight Research Collection

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