@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.}, 
}

{"_id":"DdEokajF6YzWSGQ9d","bibbaseid":"mahfouz-mouradchehade-honeine-farah-snoussi-kernelbasedmachinelearningusingradiofingerprintsforlocalizationinwsns-2015","author_short":["Mahfouz, S.","Mourad-Chehade, F.","Honeine, P.","Farah, J.","Snoussi, H."],"bibdata":{"bibtype":"article","type":"article","author":[{"firstnames":["Sandy"],"propositions":[],"lastnames":["Mahfouz"],"suffixes":[]},{"firstnames":["Farah"],"propositions":[],"lastnames":["Mourad-Chehade"],"suffixes":[]},{"firstnames":["Paul"],"propositions":[],"lastnames":["Honeine"],"suffixes":[]},{"firstnames":["Joumana"],"propositions":[],"lastnames":["Farah"],"suffixes":[]},{"firstnames":["Hichem"],"propositions":[],"lastnames":["Snoussi"],"suffixes":[]}],"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":"April","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.","bibtex":"@ARTICLE{15.wsn_loc,\n author = \"Sandy Mahfouz and Farah Mourad-Chehade and Paul Honeine and Joumana Farah and Hichem Snoussi\",\n title = \"Kernel-based machine learning using radio-fingerprints for localization in {WSNs}\",\n journal = \"IEEE Transactions on Aerospace and Electronic Systems\",\n year = \"2015\",\n volume = \"51\",\n number = \"2\",\n pages = \"1324 - 1336\",\n month = apr,\n doi=\"10.1109/TAES.2015.140061\", \n url_paper = \"http://www.honeine.fr/paul/publi/15.wsn_loc.pdf\",\n 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\",\n 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.}, \n}\n","author_short":["Mahfouz, S.","Mourad-Chehade, F.","Honeine, P.","Farah, J.","Snoussi, H."],"key":"15.wsn_loc","id":"15.wsn_loc","bibbaseid":"mahfouz-mouradchehade-honeine-farah-snoussi-kernelbasedmachinelearningusingradiofingerprintsforlocalizationinwsns-2015","role":"author","urls":{" paper":"http://www.honeine.fr/paul/publi/15.wsn_loc.pdf"},"keyword":["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"],"metadata":{"authorlinks":{}},"html":""},"bibtype":"article","biburl":"http://honeine.fr/paul/biblio_ph.bib","dataSources":["DsERGQxgYm5hGq3CY"],"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"],"search_terms":["kernel","based","machine","learning","using","radio","fingerprints","localization","wsns","mahfouz","mourad-chehade","honeine","farah","snoussi"],"title":"Kernel-based machine learning using radio-fingerprints for localization in WSNs","year":2015}