Gas Source Parameter Estimation Using Machine Learning in WSNs

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 conce...

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Bibliographic Details
Published inIEEE sensors journal Vol. 16; no. 14; pp. 5795 - 5804
Main Authors Mahfouz, Sandy, Mourad-Chehade, Farah, Honeine, Paul, Farah, Joumana, Snoussi, Hichem
Format Journal Article
LanguageEnglish
Published New York IEEE 15.07.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Area measurement
Clusters
Detectors
Estimates
Estimation
Explosions
gas diffusion
Machine learning
Mathematical models
Networks
one-class classification
Pollution measurement
Remote sensors
ridge regression
Sensors
source parameter estimation
Wireless networks
Wireless sensor networks
one-class classification
source parameter estimation
machine learning
Gas diffusion
ridge regression
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Summary: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.
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2016.2569559