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Article Dans Une Revue IEEE Transactions on Aerospace and Electronic Systems Année : 2015

Kernel-based machine learning using radio-fingerprints for localization in WSNs

Résumé

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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Dates et versions

hal-01965564 , version 1 (04-01-2019)

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Citer

Sandy Mahfouz, Farah Mourad-Chehade, Paul Honeine, Joumana Farah, Hichem Snoussi. Kernel-based machine learning using radio-fingerprints for localization in WSNs. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51 (2), pp.1324 - 1336. ⟨10.1109/TAES.2015.140061⟩. ⟨hal-01965564⟩
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