Distributed regression in sensor networks with a reduced-order kernel model

Paul Honeine (1) , Mehdi Essoloh (1) , Cédric Richard (1) , Hichem Snoussi (1)

1 LM2S - Laboratoire Modélisation et Sûreté des Systèmes

Over the past few years, wireless sensor networks received tremendous attention for monitoring physical phenomena, such as the temperature field in a given region. Applying conventional kernel regression methods for functional learning such as support vector machines is inappropriate for sensor networks, since the order of the resulting model and its computational complexity scales badly with the number of available sensors, which tends to be large. In order to circumvent this drawback, we propose in this paper a reduced-order model approach. To this end, we take advantage of recent developments in sparse representation literature, and show the natural link between reducing the model order and the topology of the deployed sensors. To learn this model, we derive a gradient descent scheme and show its efficiency for wireless sensor networks. We illustrate the proposed approach through simulations involving the estimation of a spatial temperature distribution.

Mots clés: sparsity; adaptive filtering; gradient methods; regression analysis; distributed regression; reduced-order kernel model; kernel regression methods; gradient descent scheme; Kernel; Sensor phenomena and characterization; Wireless sensor networks; Monitoring; Temperature sensors; Machine learning; Support vector machines; Computational modeling; Computational complexity; Reduced order systems

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]

Paul Honeine, Mehdi Essoloh, Cédric Richard, Hichem Snoussi. Distributed regression in sensor networks with a reduced-order kernel model. Proc. 51st IEEE GLOBECOM Global Communications Conference, 2008, New Orleans, LA, USA, United States. pp.1-5, ⟨10.1109/GLOCOM.2008.ECP.29⟩. ⟨hal-01966053⟩