<![CDATA[This research addresses optimizing communication efficiency in Vehicle-to-Vehicle (V2V) networks in urban and highway environments, focusing on the limitations of traditional routing protocols under varying traffic conditions. The study introduces an improved version of the AODV protocol, termed Learning Automata-based AODV (LA-AODV), designed to enhance data transmission reliability and reduce latency. In this approach, LA-AODV utilizes location and movement information to optimize communication paths, adaptively selecting the most reliable routes based on real-time traffic dynamics. The objective is to evaluate LA-AODV’s performance against AODV based on metrics such as packet delivery, jitter, and end-to-end delay. The study assesses protocols in dynamic urban and highway traffic settings through quantitative simulations. Results indicate that LA-AODV consistently outperforms AODV, reducing jitter by 15% and increasing packet delivery by 12% in urban scenarios while decreasing end-to-end delay by 10% on highways. These gains are achieved by LA-AODV’s enhanced route selection, which incorporates location-based decisions for optimal communication paths. The study’s findings substantiate the reliability of LA-AODV, which is a significant step forward in the field of V2V communication. This research provides a foundation for advancing next-generation V2V communication systems in urban and highway contexts, instilling confidence in the potential of LA-AODV to improve V2V communication efficiency.]]>
