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

The study introduces the learning automata-based AODV (LA-AODV) protocol to enhance vehicle-tovehicle
(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. LAAODV
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.