Combining a physical model with a nonlinear fluctuation for signal propagation modeling in WSNs
Résumé
In this paper, we propose a semiparametric regression model that relates the received signal strength indicators (RSSIs) to the distances separating stationary sensors and moving sensors in a wireless sensor network. This model combines the well-known log-distance theoretical propagation model with a nonlinear fluctuation term, estimated within the framework of kernel-based machines. This leads to a more robust propagation model. A fully comprehensive study of the choices of parameters is provided, and a comparison to state-of-the-art models using real and simulated data is given as well.
Mots clés
multikernel learning
distance measurement
regression analysis
radiowave propagation
RSSI
wireless sensor networks
semiparametric regression model
received signal strength indicators
stationary sensors
moving sensors
wireless sensor network
Polynomials
Kernel
Training
Data models
Mathematical model
kernel functions
Distance estimation
Computational modeling
log-distance theoretical propagation model
nonlinear fluctuation term
kernel-based machines
WSN
Sensors
semiparametric regression
Index Terms-Distance estimation
multi-kernel learning
| Origine | Fichiers produits par l'(les) auteur(s) |
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