Optimized vehicular connectivity and data exchange in a tree-structured VLC communication network based on optical codewords

Effective communication solutions are crucial in the dynamic transportation landscape. The rise of autonomous vehicles and sophisticated transportation systems has shaped urban mobility, underscoring the importance of safety considerations and data-driven decision making. This study examines the significance of rapid, low-latency communication in advanced intelligent transportation systems, focusing on the use of Visible Light Communication (VLC) in vehicle ad hoc networks (VANETs). This study introduces a tree-structured communication architecture utilizing hierarchical optical codewords to enhance data routing efficiency and establish a vehicle identification system. The proposed system employs dynamic attachment and reattachment protocols in conjunction with adaptive quality-of-service mechanisms to effectively mitigate variability in traffic dynamics, thus enhancing network stability and data aggregation. Simulation results contrasting the Intelligent Driver Model, Gipps, and Krauss mobility models indicate that, while more complex network trees may lead to increased delay and lower effective signal-to-noise ratios, models characterized by greater vehicular spacing generally result in reduced delay and enhanced SNR, though this improvement comes at the cost of connectivity. This document provides a detailed examination of mobility-aware performance and the incorporation of tree-structured VLC VANETs that employ hierarchical optical codewords for distinct node identification. The performance insights reveal significant improvements in scalability, latency, and throughput, which support the advancement of smart city infrastructures that are more sustainable, efficient, and secure.