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, Massinissa
Bounceur, Ahcene
Euler, Reinhardt
Kechadi, Tahar
et al.
Permanent link: http://hdl.handle.net/10197/8206
Date: 21-Jul-2016
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.
Type of material: Conference Publication
Publisher: IEEE
Copyright (published version): 2016 IEEE
Keywords: Optimisation;Decision analytics
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
Appears in Collections:Computer Science Research Collection
Insight Research Collection

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