Kernel-based machine learning using radio-fingerprints for localization in WSNs. Mahfouz, S.; Mourad-Chehade, F.; Honeine, P.; Farah, J.; and Snoussi, H. IEEE Transactions on Aerospace and Electronic Systems, 51(2):1324 - 1336, April, 2015.
Kernel-based machine learning using radio-fingerprints for localization in WSNs [pdf]Paper  doi  abstract   bibtex   
This paper introduces an original method for sensors localization in WSNs. Based on radio-location fingerprinting and machine learning, the method consists of defining a model whose inputs and outputs are, respectively, the received signal strength indicators and the sensors locations. To define this model, several kernel-based machine-learning techniques are investigated, such as the ridge regression, support vector regression, and vector-output regularized least squares. The performance of the method is illustrated using both simulated and real data.
@ARTICLE{15.wsn_loc,
   author =  "Sandy Mahfouz and Farah Mourad-Chehade and Paul Honeine and Joumana Farah and Hichem Snoussi",
   title =  "Kernel-based machine learning using radio-fingerprints for localization in {WSNs}",
   journal =  "IEEE Transactions on Aerospace and Electronic Systems",
   year  =  "2015",
   volume =  "51",
   number =  "2",
   pages =  "1324 - 1336",
   month =  apr,
   doi="10.1109/TAES.2015.140061", 
   url_paper  =  "http://www.honeine.fr/paul/publi/15.wsn_loc.pdf",
   keywords  =  "machine learning, wireless sensor networks, learning (artificial intelligence), regression analysis, sensor placement, support vector machines, telecommunication computing, wireless sensor networks, vector-output regularized least squares, support vector regression, ridge regression, received signal strength indicators, radio-location fingerprinting, sensors localization, WSNS, kernel-based machine-learning techniques, Optimization, Sensors, Wireless sensor networks, Kernel, Databases, Computational modeling, Mathematical model",
   abstract={This paper introduces an original method for sensors localization in WSNs. Based on radio-location fingerprinting and machine learning, the method consists of defining a model whose inputs and outputs are, respectively, the received signal strength indicators and the sensors locations. To define this model, several kernel-based machine-learning techniques are investigated, such as the ridge regression, support vector regression, and vector-output regularized least squares. The performance of the method is illustrated using both simulated and real data.}, 
}
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