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

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