Distributed intelligent illumination control in the context of probabilistic graphical models

Cosovic, M. and Devaja, T. and Bajovic, D. and Machaj, J. and McCutcheon, G. and Stankovic, V. and Stankovic, L. and Vukobratovic, D.; Perkovic, Toni and Vukojevic, Katarina and Rodrigues, Joel J.P.C. and Rodrigues, Joel J.P.C. and Nizetic, Sandro and Patrono, Luigi and Solic, Petar, eds. (2019) Distributed intelligent illumination control in the context of probabilistic graphical models. In: 2019 4th International Conference on Smart and Sustainable Technologies, SpliTech 2019. IEEE, HRV. ISBN 978-953-290-091-0 (https://doi.org/10.23919/SpliTech.2019.8783018)

[thumbnail of Cosovic-etal-SST2019-Distributed-intelligent-illumination-control-in-the-context-of-probabilistic-graphical-models]
Preview
 Text. Filename: Cosovic_etal_SST2019_Distributed_intelligent_illumination_control_in_the_context_of_probabilistic_graphical_models.pdf
Accepted Author Manuscript
Download (1MB)| Preview

Abstract

Lighting systems based on light-emitting diodes (LEDs) possess many benefits over their incandescent counterparts including longer lifespans, lower energy costs, better quality of light and no toxic elements, all without sacrificing consumer satisfaction. Their lifespan is not affected by switching frequency allowing for better illumination control and system efficiency. In this paper, we present a fully distributed energy-saving illumination dimming control strategy for the system of a lighting network which consists of a group of LEDs and user-Associated devices. In order to solve the optimization problem, we are using a distributed approach that utilizes factor graphs and the belief propagation algorithm. Using probabilistic graphical models to represent and solve the system model provides for a natural description of the problem structure, where user devices and LED controllers exchange data via line-of-sight communication.

CORE (COnnecting REpositories)