An Effective Cluster Based Routing Using Multi-Objective Prairie Dog Optimization in Vehicular Ad-hoc Network

A vehicular Ad-hoc network (VANET) is a kind of Mobile Ad-hoc network (MANET) which utilize moving vehicles to create communication with each other to exchange information to address a particular need. The usage of radio frequency bandwidth in VANET diminishes the network performance due to the high latency rate. So, the combination of Visible Light Communication (VLC) helps to achieve energy efficiency with minimized delay. In this research, the VLC-based VANET is used to perform effective data transmission using the proposed Multiobjective Prairie Dog Optimization Algorithm (MPDOA). The balance generated among the phases of exploration and exploitation helps to achieve improved results in terms of packet delivery ratio, delay and throughput. The efficiency of the proposed MPDOA-VLC-VANET is compared with the existing Traffic aware and Low-Latency Routing schema (TaLAR) using Harris Hawks Optimization (HHO). When the comparison is performed by considering the throughput, the proposed approach achieves a throughput of
$8.9\times 10^{3}$
kbps where TaLAR-HHO achieves a throughput of 5720 kbps.