Gas Source Parameter Estimation Using Machine Learning in WSNs

被引：10

作者：

Mahfouz, Sandy ^{[1
]}

Mourad-Chehade, Farah ^{[1
]}

Honeine, Paul ^{[2
]}

Farah, Joumana ^{[3
]}

Snoussi, Hichem ^{[1
]}

机构：

[1] Univ Technol Troyes, Lab Modelisat & Surete Syst, Inst Charles Delaunay, F-10010 Troyes, France

[2] Univ Rouen, Lab Informat Traitement Informat & Syst, F-76800 Rouen, France

[3] Lebanese Univ, Fac Engn, Roumieh, Lebanon

来源：

IEEE SENSORS JOURNAL | 2016年 / 16卷 / 14期

关键词：

Gas diffusion; machine learning; one-class classification; ridge regression; source parameter estimation;

D O I：

10.1109/JSEN.2016.2569559

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper introduces an original clusterized framework for the detection and estimation of the parameters of multiple gas sources in wireless sensor networks. The proposed method consists of defining a kernel-based detector that can detect gas releases within the network's clusters using concentration measures collected regularly from the network. Then, we define two kernel-based models that accurately estimate the gas release parameters, such as the sources locations and their release rates, using the collected concentrations.

引用

页码：5795 / 5804

页数：10

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