This thesis presents mobility-aware handover mechanisms for hybrid radio frequency (RF) and narrow-beam optical wireless communication (NB-OWC) networks in indoor environments. Reactive handover mechanisms, commonly used in existing system such as Wi-Fi or hybrid Li-Fi and Wi-Fi networks HLWNets, are shown to be inadequate in ultra-dense NB-OWC deployments due to the limited coverage area per beam. To address these limitations, three novel handover mechanisms—one reactive and two predictive—were developed and evaluated against a baseline reactive mechanism from the literature. A simulation framework assessed performance across a range of user speeds, using average throughput, lower-bound throughput, and handover rate as key metrics. The predictive mechanisms demonstrated significant improvements over reactive approaches, with the predictive throughput mechanism achieving a 157% increase in average throughput compared to the baseline. A trade-off between average and lower-bound throughput was observed, and a strong correlation between handover rate and throughput was identified. These results demonstrate that prediction-based handover strategies can substantially enhance user experience and network efficiency in hybrid RF/NB-OWC systems, particularly under moderate mobility, and provide a foundation for exploring multi-user and more realistic deployment scenarios.

This study introduces a novel system that leverages three photodiodes and ambient light to identify air-written characters on a resource-constrained device. Through experimentation, suitable methods of data preprocessing, machine learning and model compression were selected to recognize the first 10 characters of the Latin alphabet. The final system was able to recognize these characters with a between-participant accuracy of 50.80% and a within-participant accuracy of 67.82%.