Enhancing Vehicle Communication on Highways through Modification of the On-Demand Distance Vector Routing Protocol Using Learning Automata Approach
DOI:
https://doi.org/10.30812/bite.v7i1.5131Keywords:
V2V Communication, Learning Automata-Based AODV, VANET Performance Optimization, Intelligent Transportation Systems, Highway Traffic SimulationAbstract
Backgroud: Vehicle-to-vehicle communication has become a crucial element in the development of intelligent transportation systems. However, conventional routing protocols face limitations in coping with dense and dynamic traffic conditions.
Objective: The objective of this study is to improve communication efficiency between vehicles by modifying an on-demand routing protocol using a learning automata approach.
Method: This study employed a simulation method with traffic modeling using traffic modeling software and network simulation tools, based on data from highways in the Soekarno-Hatta International Airport area.
Result: The results of this study show that the developed protocol increases the packet delivery ratio to 87.7% and reduces latency by 6.5%.
Conclusion: The conclusion of this study is that the application of learning automata in vehicle routing enhances communication reliability and supports the implementation of a more adaptive and efficient transportation system.
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Copyright (c) 2025 Bryan Jonathan Hutapea, Ketut Bayu Yogha Bintoro, Helna Wardhana
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