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.
Mots clés
machine learning
learning (artificial intelligence)
regression analysis
sensor placement
support vector machines
sensors localization
radio-location fingerprinting
received signal strength indicators
ridge regression
support vector regression
Optimization
Sensors
kernel-based machine-learning techniques
WSNS
telecommunication computing
vector-output regularized least squares
Wireless sensor networks
Kernel
Databases
Computational modeling
Mathematical model