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.
Kernel-based machine learning using radio-fingerprints for localization in wsns
IEEE Transactions on Aerospace and Electronic Systems ; 51 , 2 ; 1324-1336
2015-04-01
948260 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch