Visible Light Communication (VLC) leverages the visible light spectrum to establish wireless communication, offering advantages such as broader bandwidth, and reduced energy consumption compared to traditional radio frequency methods. VLC offers two main approaches: passive and active. Passive VLC takes advantage of sunlight, which is pervasive and highly power-efficient. However, its reliability can be affected by weather conditions and the absence of sunlight at night. On the other hand, active VLC which uses artificial light sources like LEDs, provides more consistent performance but is not power-efficient when sunlight is available. For example, during the day when ample sunlight could be used for passive VLC, turning on a light bulb for active VLC is unnecessary and wasteful.

This thesis tackles these challenges by combining the best of active and passive systems to create an even more power-efficient and reliable system. It addresses two key problems in passive VLC: reducing the power consumption of passive VLC transmitters and enhancing the reliability of passive VLC links through a hybrid system. By replacing the FPGA-based controller with a low-power microcontroller, the power consumption of the Digital Micro-mirror Device used for sunlight modulation was significantly reduced from 1.3W to 36.85mW, while achieving a data rate of 25 kbps with a bit error rate (BER) of less than 1% at a distance of 25 cm. Its maximum range was determined to be 75 cm at 10 kbps. Additionally, integrating an LED component into the passive VLC communication link improved reliability in varying ambient light conditions. The hybrid system demonstrated enhanced performance in low ambient light scenarios, ensuring a BER below 1% regardless of ambient light conditions. In high ambient light scenarios, the LED can be dimmed or turned off, conserving power and making the system more efficient than a purely active VLC system. This thesis contributes to the advancement of energy-efficient and reliable VLC technologies, paving the way for their broader adoption.

Visible Light Communication (VLC) has been gaining interest in the industry and academia for the last decade. VLC enables a high-speed communication alternative to conventional radio such as Bluetooth or WiFi and presents a solution to the 'spectrum crunch'. More recently, the combination of energy harvesting and VLC has been explored to enable battery-less devices that can communicate bidirectionally using light. In parallel, drones are being used in the industry for tasks such as warehouse management. However, very little research has been done on the conjunction of VLC and drones, even though this offers interesting research opportunities and applications.

In this thesis, we perform the first evaluation of different types of modulation techniques between a drone and base station in the context of VLC. We present DynamicVLBC: a complete system consisting of a drone ('Reader') and base station ('Tag'). We optimize this system such that the Reader can fly and the Tag can operate battery-less at an ultra-low power level. We thoroughly evaluate the system in both indoor and outdoor conditions. Our evaluations show that when the Reader is static and the Tag is externally powered, that the system can communicate up to 200cm with a BER below 1%. Moreover, when the Tag is operating battery-less, the system can still effectively communicate up to 150cm. Finally, when the Reader is airborne as well, we show that the system can still communicate up to 85cm.

Solar cells are usually used as power source, but can be used for sensing as well. We propose a novel indoor tracking system that tracks people by using the change in output caused by their shadows. First, we develop a simulator to determine how the solar cells should be positioned in the tracking environment to get the best detection rate. This applies ray tracing in a model of the environment, and uses the standard deviation of solar cell output to compare different positions. Next, we apply changepoint detection methods to convert the solar cell output to a binary signal. One approach uses Bayesian online changepoint detection and another uses the change of gradient in the signal. Finally, the binary output from multiple solar cells is fused to track multiple targets in a real indoor environment in different scenarios. For this, we have implemented a particle filter based on the probability hypothesis density filter. We compare this with a tracking algorithm that uses a hidden Markov model. We have combined everything to show that it is possible to track up to two people in an indoor environment in different lighting conditions using a network of multiple solar cells.