Performance Optimization of V2V Communication Reactive Routing Protocol Through Simulation Analysis

This study was conducted to explore and find the optimum parameters related to the Quality of Service (QoS) in Vehicle-to- Vehicle (V2V) communication within Vehicular Ad Hoc Networks (VANETs) by optimizing the Learning Automata-based Ad Hoc On-Demand Distance Vector protocol (LA-AODV). The study compared three variants of LA-AODV (LA-AODV(A), LA-AODV(B), and LA-AODV(C)) with the standard AODV. The simulation result evaluated their performance based on crucial QoS metrics such as FLOD ID, Packet Loss Ratio (PLR), Packet Delivery Ratio (PDR), Average Throughput, End-to-End Delay, and End-to-End Jitter. The results indicated that LA-AODV(B) consistently outperformed the other variants, particularly in FLOD ID. The improvements of 9.14%, 40.29%, and 22.79% in 50, 100, and 200-trial scenarios compared to LA-AODV(A) were significant. However, LA-AODV(C) showed suboptimal performance in the exact scenarios. Nevertheless, practical parameter tuning of LA-AODV(C) led to a remarkable improvement in protocol performance, with a 74% reduction in FLOD ID compared to AODV in various simulation scenarios. Parameter tuning is crucial for consistent efficiency in V2V communication, as LA-AODV's adaptability under different traffic conditions provides valuable insights. Our focus is on evaluating LA-AODV's performance in realistic scenarios. While we plan to compare it with established methods in the future, our current research allows us to understand its effectiveness in real-world V2V communication compared to standard AODV. We aim to expand our scope by comparing LA-AODV with other established methods in future studies.