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Intelligent Data Mining Techniques for Emergency Detection in Wireless Sensor Networks
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| File | Description | Size | Format | |
|---|---|---|---|---|
| insight_publication.pdf | 460.34 kB | Adobe PDF | Download |
| 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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