LEARNING AUTOMATA-BASED AODV ROUTING PROTOCOL TO IMPROVE V2V COMMUNICATION IN URBAN TRAFFIC SIMULATION
Abstract
Network congestion, packet loss, and high latency in the AODV routing protocol are significant obstacles to achieving reliable vehicle-to-vehicle (V2V) communication. Consequently, an update to the AODV protocol is necessary. This research proposes the Learning Automata-based AODV (LA-AODV) routing protocol to address these issues. The LA-AODV protocol incorporates learning automata into the routing protocol by considering speed, acceleration, and x and y coordinates. The communication quality index with the nearest vehicles is measured before selecting a set of relay nodes until the maximum estimated time is reached. The primary objective of this study is to enhance the performance of V2V communications by reducing network congestion, packet loss, and latency. The results demonstrate that LA-AODV achieves a maximum packet delivery ratio (PDR) improvement of 4.0% and a throughput of up to 56.50 kbps, surpassing the performance of both AODV and DSDV protocols. These findings indicate the potential of LA-AODV to optimize V2V communications, thereby significantly improving transportation safety and efficiency. The research contributes to the field by providing a novel solution to enhance V2V communication quality in urban traffic scenarios, offering significant benefits in reduced latency, increased reliability, and overall better network performance.
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