Learning Automata-Based AODV to Improve V2V Communication in A Dynamic Traffic Simulation

Abstract

The study introduces the learning automata-based AODV (LA-AODV) protocol to enhance vehicle-to-vehicle (V2V) communication in dynamic vehicular Ad-hoc networks (VANETs). Existing routing protocols, such as Ad-hoc on-demand distance vector (AODV) protocols, face significant challenges, including low data transfer rates, higher delay times, lower throughput, and data congestion resulting from rapidly changing network topologies. LA-AODV addresses these issues by optimizing the quality of service (QoS) through the real-time selection of relay nodes based on vehicle speed, distance, and actual position parameters. Simulations were conducted at the Gadjah Mada university (UGM) roundabout in Yogyakarta, Indonesia, using SUMO and NS3 simulators. LA-AODV outperforms AODV with Packet Delivery Ratios ranging from 95% to 99% and Average Throughputs between 36.90 Kbps and 56.50 Kbps. Although LA-AODV exhibits slightly higher End-to-End Delays, it effectively mitigates Packet Loss Ratios ranging from 1% to 4%. These enhancements optimize routing decisions, reduce communication overhead, and enhance network resource utilization.