Combining a physical model with a nonlinear fluctuation for signal propagation modeling in WSNs

Sandy Mahfouz (1) , Paul Honeine (1) , Farah Mourad-Chehade (1) , Joumana Farah (2) , Hichem Snoussi (1)

1 LM2S - Laboratoire Modélisation et Sûreté des Systèmes
2 Holy-Spirit University of Kaslik [Jounieh]

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

Domaines: Apprentissage [cs.LG]; Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Sandy Mahfouz, Paul Honeine, Farah Mourad-Chehade, Joumana Farah, Hichem Snoussi. Combining a physical model with a nonlinear fluctuation for signal propagation modeling in WSNs. Proc. 11th IEEE/ACS International Conference on Computer Systems and Applications, 2014, Doha, Qatar. pp.413-419, ⟨10.1109/AICCSA.2014.7073228⟩. ⟨hal-01965996⟩