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.
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.},
}