A visible light communication (VLC) provides potential and effective communication paradigm due to the demand of high data-rate applications. VLC networks, consisting of multiple light emitting diodes (LEDs) and it provides the low-cost high data-rate transmission to multiple users simultaneously in indoor environments. VLC has been recently introduced as a secure directional data transmission in vehicle to vehicle to provide an intelligent vehicle control system. However, the performance of this system is mostly affected by the collision of data transmission between different users. In this paper, we introduce an optimal visible light communication (OVLC) network that allows vehicles which have provides collision aware data transmission to improve the chance of transmitting information successively according to the network condition. Firstly, the next forwarding node is selected by the chaotic fish swarm optimization (CFSO) algorithm with the help of vehicle information’s such as intensity of light, the distance and speed of neighboring vehicles. The second contribution is to illustrate the congestion control (CC) system for avoiding extra time due to the control packets exchange process. The optimal result is then forward to the source vehicle equipped device, which helps the driver to make a healthy to control vehicle and efficiently avoid or prevent road accidents under different circumstances. The results show that the proposed OVLC network performs very efficient than existing network in terms of quality metrics, such as throughput, delay, packet loss rate, energy consumption and fairness index.

Vehicular Ad-hoc Network (VANET) is a growing technology that utilizes moving vehicles as mobile nodes for exchanging essential information between users. Unlike the conventional radio frequency based VANET, the Visible Light Communication (VLC) is used in the VANET to improve the throughput. However, the road safety is considered as a significant issue for users of VANET. Therefore, congestion-aware routing is required to be developed for enhancing road safety, because it creates a collision between the vehicles that causes packet loss. In this paper, the Multi Objective Congestion Metric based Artificial Ecosystem Optimization (MOCMAEO) is proposed to enhance road safety. The MOCMAEO is used along with the Ad hoc On-Demand Distance Vector (AODV) routing protocol for generating the optimal routing path between the source node to the Road Side Unit (RSU). Specifically, the performance of the MOCMAEO is improved using the multi-objective fitness functions such as congestion metric, residual energy, distance, and some hops. The performance of the MOCMAEO is analyzed by means of Packet Delivery Ratio (PDR), throughput, delay, and Normalized Routing Load (NRL). The PSO based geocast routing protocols such as LARgeoOPT, DREAMgeoOPT, and ZRPgeoOPT are used to evaluate the performance of the MOCMAEO method. The PDR of the MOCMAEO method is 99.92 % for 80 nodes, which is high when compared to the existing methods.

Traffic congestion is one of the significant problems in every metropolitan city. Traffic congestion occurs, when large numbers of vehicles are all together and are not able to move or move slowly, it is also known as a traffic jam. The main aim of the proposed OIVC-VLC VANET system is to improve the data transmission rate to control traffic in high density loads for emergency vehicles. Traffic congestion leads to wasting of time, road accidents, delays of trips, and reduces regional economic health and fuel consumption. Moreover, the most critical concern of traffic congestion is a delay of emergency vehicles like ambulance and police vehicles, and firefighting trucks leading to an increase the human death and loss their essential things. In addition, this paper focuses to reduce the traffic congestion and traveling time of emergency vehicles. To overcome traffic congestion issues, an Optimal Intelligent Vehicle Control System for the emergency vehicle by intelligent traffic clearance (OIVC-VLC-VANET) is proposed. Firstly an improved whale optimization (IWO) algorithm is submitted for grouping the vehicle nodes based on their behaviors. Secondly, the differential search algorithm is used to select the next forwarding node using multiple constraints received from vehicles. Thirdly, the dragonfly algorithm is submitted for avoiding extra time due to the control packets exchange process. The results show that the proposed OIVC-VLC-VANET system can perform very efficiently in terms of quality constraints than existing systems.

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Vehicular ad hoc network (VANET) is a subdivision of the mobile ad hoc networks which uses the moving vehicles as mobile nodes to form the mobile network. In conventional vehicular communications, the restricted radio frequency bandwidth affects the network performances. Therefore, Visible Light Communication (VLC) is integrated with the growing vehicular ad hoc network to obtain high data rate and less energy consumption during the communication. In this paper, vehicular communication is integrated with visible light communication to avoid the issues caused by the restricted radio frequency bandwidth. Moreover, the Routing using Biogeography Based Optimization (RBBO) is proposed to develop an optimal route between the source vehicles to the destination. This research performs two different communications such as vehicle to vehicle and vehicle to the infrastructure. The performance of the RBBOVLC-VANET method is analyzed by means of throughput, packet delivery ratio, delay and routing overhead as well as these performances are compared with the existing method namely ant colony optimization based routing protocol. The throughput of the routing using the biogeography based optimization method is 589.763 kbps for 500 nodes which is high when compared to the existing method.