Combining A Physical Model With A Nonlinear Fluctuation For Signal Propagation Modeling In Wsns
Computer Systems and Applications(2014)
Abstract
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.
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Key words
Distance estimation,kernel functions,multi-kernel learning,RSSI,semiparametric regression
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