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<p>Vehicular Ad-Hoc Networks (VANETs) play a critical role in enabling Intelligent Transportation Systems (ITS), particularly in urban environments characterized by high mobility, dynamic topology, and frequent link disruptions. Efficient routing in such conditions remains a significant challenge due to congestion, unstable links, and increased communication overhead. To address these issues, this paper proposes an intelligent hybrid optimization framework that integrates Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) for enhanced routing efficiency in urban VANETs. The proposed model formulates routing as a multi-objective optimization problem, aiming to minimize end-to-end delay and routing overhead while maximizing packet delivery ratio (PDR) and throughput. A mobility-aware system model incorporating link stability, traffic density, and connectivity constraints is developed. The hybrid PSO–ACO algorithm combines global exploration and local exploitation to identify optimal routing paths under dynamic conditions. Additionally, an adaptive fitness function is employed to balance multiple QoS parameters effectively. Experiment results using ns-3 demonstrate that the proposed framework significantly outperforms conventional protocols such as AODV, DSR, and GPSR, as well as recent state-of-the-art methods. The model achieves a PDR of 92.6%, reduces end-to-end delay to 110 ms, improves throughput to 520 kbps, and lowers routing overhead to 0.27.</p>

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Enhancing routing efficiency in urban VANETs through intelligent optimization techniques

  • 期刊论文

  • 发表日期：

    2026年5月2日

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  • Sajithunisa Hussain,
  • S. Jacophine Susmi,
  • Remya P. George,
  • A. Althaf Ali,
  • Nazia Ahmad,
  • Rubina Liyakat Khan

摘要

Vehicular Ad-Hoc Networks (VANETs) play a critical role in enabling Intelligent Transportation Systems (ITS), particularly in urban environments characterized by high mobility, dynamic topology, and frequent link disruptions. Efficient routing in such conditions remains a significant challenge due to congestion, unstable links, and increased communication overhead. To address these issues, this paper proposes an intelligent hybrid optimization framework that integrates Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) for enhanced routing efficiency in urban VANETs. The proposed model formulates routing as a multi-objective optimization problem, aiming to minimize end-to-end delay and routing overhead while maximizing packet delivery ratio (PDR) and throughput. A mobility-aware system model incorporating link stability, traffic density, and connectivity constraints is developed. The hybrid PSO–ACO algorithm combines global exploration and local exploitation to identify optimal routing paths under dynamic conditions. Additionally, an adaptive fitness function is employed to balance multiple QoS parameters effectively. Experiment results using ns-3 demonstrate that the proposed framework significantly outperforms conventional protocols such as AODV, DSR, and GPSR, as well as recent state-of-the-art methods. The model achieves a PDR of 92.6%, reduces end-to-end delay to 110 ms, improves throughput to 520 kbps, and lowers routing overhead to 0.27.