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Enhancing OLSR protocol in VANETs with multi-objective particle swarm optimization

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  • Yang, Hanlin
  • Pu, Cunlai
  • Wu, Jiexin
  • Wu, Yanqing
  • Xia, Yongxiang

Abstract

The Optimized Link State Routing Protocol (OLSR) is envisioned as one of the key components to the operation of future vehicular ad hoc networks (VANETs). Its performance is however fairly constrained in the vehicular communication environment due to the limited coverage of wireless transmission and high-speed movement of nodes. In this brief, we propose a multi-objective particle swarm optimization (MOPSO) framework to enhance the performance of OLSR in VANETs. Specifically, we formulate a multi-objective optimization problem (MOP) considering not only the quality of service (QoS), including throughput, delay and packet loss rate, but also the cost of service, i.e., routing load. We solve this MOP with MOPSO and obtain the Pareto front corresponding to the optimal equilibrium of performance and cost. In the experiment, our optimization framework is used in a general setting of VANETs to obtain the optimal parameters of OLSR, i.e., Hello and TC intervals, and these parameters are further validated in realistic VANET scenarios. Our optimization framework can be naturally extend to other dynamic routing protocols in networks.

Suggested Citation

  • Yang, Hanlin & Pu, Cunlai & Wu, Jiexin & Wu, Yanqing & Xia, Yongxiang, 2023. "Enhancing OLSR protocol in VANETs with multi-objective particle swarm optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 614(C).
    • Handle: RePEc:eee:phsmap:v:614:y:2023:i:c:s0378437123001255
    DOI: 10.1016/j.physa.2023.128570
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    References listed on IDEAS

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    1. Cui, Nan & Chen, Bokui & Zhang, Kai & Zhang, Yi & Liu, Xiaotong & Zhou, Jun, 2019. "Effects of route guidance strategies on traffic emissions in intelligent transportation systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 32-44.
    2. Liang, Yuanyuan & Xia, Yongxiang & Yang, Xu-Hua, 2022. "Hybrid-radius spatial network model and its robustness analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    3. Zhang, Xuejun & Xu, Guoqiang & Xia, Yongxiang, 2018. "Optimal defense resource allocation in scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 2198-2204.
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    Cited by:

    1. Ma, Changxi & Zhao, Mingxi & Huang, Xiaoting & Zhao, Yongpeng, 2024. "Optimized deep extreme learning machine for traffic prediction and autonomous vehicle lane change decision-making," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).

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