Simulation-Based Evaluation of Reinforcement Learning-Enhanced Location-Aware Routing in Urban Vehicular Ad-hoc Networks (VANETs)

Authors

  • Arvind Kumar Manav Rachna International Institute of Research Studies, Faridabad 121004, India
  • Shobha Tyagi Manav Rachna International Institute of Research Studies, Faridabad 121004, India
  • Prashant Dixit Galgotias University, Greater Noida 203201, India
  • S.S. Tyagi Gurugram University, Samaspur 122018, India

DOI:

https://doi.org/10.59796/jcst.V16N2.2026.180

Keywords:

location-aware routing, ns3 simulation, reinforcement learning, routing protocols, urban mobility, vehicular ad hoc networks

Abstract

Vehicular Ad Hoc Networks (VANETs) need robust routing protocols to ensure rapid and reliable data transmission in urban environments characterized by high mobility and highly dynamic topologies. Traditional routing protocols lead to excessive routing overhead, increased hop counts, prolonged end-to-end delays, and reduced packet delivery ratios (PDR), which collectively hinder reliable and efficient data dissemination within intelligent transportation systems (ITS). This research presents a simulation-based evaluation of a Reinforcement Learning (RL)-enhanced Location-Aware Routing (LAR) protocol. By integrating RL with the traditional LAR protocol, the proposed framework dynamically adapts to network fluctuations, thereby minimizing routing overhead, hop counts, and end-to-end delay. Compared against classical routing protocols such as AODV, DSR, and LAR across sparse (50 vehicles), moderate (150), and dense (300) urban traffic scenarios using NS-3 and SUMO, RL-LAR demonstrates superior performance. Improvements ranging from 3% to 12% were observed in PDR, while average end-to-end delay was reduced by 9.7% to 13.8%. Additionally, routing overhead decreased by 4.3% to 8.7%, hop counts were reduced by 15% to 23% and throughput increased by 15% to 31% relative to baseline protocols. These gains were also validated by ANOVA (p < 0.01) and found to be suitable for routing in smart cities for future intelligent transportation systems.

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Published

2026-03-25

How to Cite

Kumar, A., Tyagi, S., Dixit, P., & Tyagi, S. (2026). Simulation-Based Evaluation of Reinforcement Learning-Enhanced Location-Aware Routing in Urban Vehicular Ad-hoc Networks (VANETs). Journal of Current Science and Technology, 16(2), 180. https://doi.org/10.59796/jcst.V16N2.2026.180

Issue

Vol. 16 No. 2 (2026): April-June

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Research Article

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