An Effective Data Dissemination Using Multi Objective Congestion Metric Based Artificial Ecosystem Optimization for Vehicular Ad-Hoc Network

Abstract

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.