Vehicular visible light communication (V VLC) systems, when combined with reconfigurable intelligent surfaces (RIS), present promising opportunities for improving communication reliability and efficiency in vehicle to vehicle (V2V) environments. Nevertheless, safeguarding these systems at the physical layer remains a critical challenge, particularly given their exposure to jamming threats. In this study, we investigate the physical layer security performance of RIS assisted V2V VLC systems under jamming scenarios, employing realistic V2V VLC channel models. We develop a methodology to examine the impact of various security strategies in mitigating the adverse effects of jamming. Our analysis examines key parameters including the signal to noise ratio SNR, the secure communication rate and the count of RIS units. Simulation results confirm that the proposed security systems significantly enhance the resilience of V2V VLC networks in the presence of jamming attacks. These results offer useful perspectives for the reliable design structure and deployment of RIS based V2V VLC systems in practical vehicular communication settings.

Visible light communication (VLC) is a rapidly growing wireless communication technology for the Internet of Things (IoT) that offers high data rates and low latency, making it ideal for massive connectivity. Efficient resource allocation is essential in VLC networks to minimize inter-symbol and cochannel interferences, which can greatly improve network performance and user satisfaction. This paper focuses on an indoor IoT-based VLC system that utilizes photodetectors (PDs) on users’ cell phones as receivers, with the goal of maximizing system performances and reducing power consumption by selectively activating some PDs while deactivating others. However, this objective presents a challenge due to the inherent non-convex nature of the multi-objective optimization problem, which cannot be solved by analytical means. To address this, we propose an enhanced Aquila optimization (EAO) scheme that improves upon the Aquila Optimizer (AO) by incorporating a fitness distance balance (FDB) function. We evaluate our proposed EAO in various scenarios under different settings, considering both capacity and fairness metrics. Through simulations, we demonstrate the effectiveness of our approach and its superiority over classical algorithms such as Aquila Optimizer (AO), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO) in finding the optimal solution. Our results confirm that the proposed EAO algorithm can efficiently optimize the system capacity and ensure fairness among all users, providing a promising solution for indoor VLC systems.

The reliability of vehicle-to-vehicle (V2V) Visible Light Communication (VLC) systems is affected by several factors, such as car mobility and optics system design. Therefore, this paper focuses on the cars’ relative positions and the design of the optics on the receiving end. Instead of using the rectangle detector, commonly used in the literature, this paper proposes using the polar detector for V2V-VLC systems. We introduce using an imaging receiver with different kinds of optical lenses, such as Fresnel and Aspherical lenses to improve the performance of a V2V-VLC system. We perform a channel modeling study using the non-sequential ray-tracing approach, considering the possibility of horizontal and vertical movement between vehicles. A comprehensive performance comparison of these lenses assumes different vehicle positions on the road. We further investigate the impact of receiver type and lateral shift on the performance of the considered systems. The obtained results demonstrated that with a carefully chosen system and lens parameters, an enhancement of up to 7 dB in total received power could be achieved compared to the case without the lens.