In the modern day, there is a serious spectrum crunch in the legacy radio frequency (RF) band, for which visible light communication (VLC) can be a promising option. VLC is a short-range wireless communication variant which uses the visible light spectrum. In this thesis, we are using a VLC-based architecture for providing scalable communications to Internet-of-Things (IoT) devices where a multi-element hemispherical bulb is used that can transmit data streams from multiple light emitting diode (LED) boards. The essence of this architecture is that it uses a Line-of-Sight (LoS) alignment protocol that handles the hand-off issue created by the movement of receivers inside a room. We start by proposing an optimization problem aiming to minimize the total consumed energy emitted by each LED taking into consideration the LEDs' power budget, users' perceived quality-of-service, LED-user associations, and illumination uniformity constraints. Then, because of the non-convexity of the problem, we propose to solve it in two stages: (1) We design an efficient algorithm for LED-user association for fixed LED powers, and (2) using the LED-user association, we find an approximate solution based on Taylor series to optimize the LEDs' power. We devise two heuristic solutions based on this approach. The first heuristic solution, called the Low Complexity Two Stages Solution (TSS), optimizes the association between the LEDs and the mobile users before and then the power of each LED is optimized. In the second heuristic, named the Maximum Uniformity Approach, we try to improve the illumination uniformity first and then adjust the power values for each LED so that they do not go above a certain value. Finally, we illustrate the performance of our method via simulations.
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Master of Science in Electrical Engineering (M.S.E.E.)
College of Engineering and Computer Science
Electrical Engineering and Computer Engineering
Length of Campus-only Access
Masters Thesis (Open Access)
Dey, Akash, "Resource Optimization in Visible Light Communication Using Internet of Things" (2019). Electronic Theses and Dissertations, 2004-2019. 6315.