Gas Sources Parameters Estimation Using Machine Learning in WSNs

Sandy Mahfouz (1) , Farah Mourad-Chehade (1) , Paul Honeine (2, 1) , Joumana Farah (3) , Hichem Snoussi (1)

1 LM2S - Laboratoire Modélisation et Sûreté des Systèmes
2 DocApp - LITIS - Equipe Apprentissage
3 ULFG2 - Faculté de Génie en électricité et électronique, Branche 2 (Université Libanaise)

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.

Mots clés: ridge regression; one-class classification; machine learning; gas diffusion; pollution measurement; gas sensors; wireless sensor networks; source parameter estimation; explosions; multiple gas source parameter estimation; Index Terms-gas diffusion

Domaines: Apprentissage [cs.LG]; Traitement du signal et de l'image [eess.SP]

Sandy Mahfouz, Farah Mourad-Chehade, Paul Honeine, Joumana Farah, Hichem Snoussi. Gas Sources Parameters Estimation Using Machine Learning in WSNs. IEEE Sensors Journal, 2016, 16 (14), pp.5795 - 5804. ⟨10.1109/JSEN.2016.2569559⟩. ⟨hal-01965049⟩