Gas Sources Parameters Estimation Using Machine Learning in WSNs. Mahfouz, S., Mourad-Chehade, F., Honeine, P., Farah, J., & Snoussi, H. IEEE sensors journal, 16(14):5795 - 5804, July, 2016.
Gas Sources Parameters Estimation Using Machine Learning in WSNs [link]Link  Gas Sources Parameters Estimation Using Machine Learning in WSNs [pdf]Paper  doi  abstract   bibtex   
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.
@ARTICLE{16.wsn.diffusion,
   author =  "Sandy Mahfouz and Farah Mourad-Chehade and Paul Honeine and Joumana Farah and Hichem Snoussi",
   title =  "Gas Sources Parameters Estimation Using Machine Learning in {WSNs}",
   journal =  "IEEE sensors journal",
   year  =  "2016",
   volume =  "16",
   number =  "14",
   pages =  "5795 - 5804",
   month =  jul,
   doi = "10.1109/JSEN.2016.2569559",
   url_link= "https://ieeexplore.ieee.org/document/7470596", 
   url_paper   =  "http://honeine.fr/paul/publi/16.wsn.diffusion.pdf",
   keywords  =  "machine learning, wireless sensor networks, gas diffusion,
machine learning, one-class classification, ridge regression, source parameter estimation, gas sensors, pollution measurement, explosions, multiple gas source parameter estimation",
   abstract = "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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