@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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