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<p>In traditional Vehicle-to-Vehicle-to-Infrastructure (V2V2I) routing algorithms, the beacon power of vehicles remains fixed, lacking adaptability to dynamic adjustments based on events or on-demand operational modes, leading to poor dynamic adaptability of vehicles and significant energy wastage. To enhance the flexibility of beacon power adjustment for vehicles in dynamic environments, while considering practical factors such as the signal coverage status, selfishness, and motion characteristics of routing vehicles, a self-adaptive V2V2I routing algorithm named Adaptive Beacon Power Routing algorithm(ABPR) is proposed. In the algorithm, in order to enhance the stability of the vehicle’s connection to RSU, vehicles first collect neighbor beacons and analyze the current signal coverage status of the vehicle. Subsequently, they adaptively adjust the beacon power of the vehicle, and then analyze available neighbor vehicle nodes through improved Q-learning, searching for the optimal neighbor node and selecting it as the next-hop route. Simulation experiments conducted on the Cologne dataset demonstrate that compared to RSAR and GPSR algorithms, the ABPR algorithm achieves an average improvement of 15% in residual energy consumption, 13.79% in selfish vehicle count, 38.58% in routing hop count, and 51.37% in routing link lifetime proportion under ± 30% standard beacon power conditions. This algorithm enhances the flexibility of beacon power adjustment in VANET environments while optimizing the overall data offloading link quality.</p>

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A V2V2I routing algorithm for Internet of Vehicles based on adaptive variable beacon power

  • 期刊论文

  • 发表日期：

    2025年4月2日

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  • Tao Wu,
  • Yang Ou,
  • Qizhao Zhou,
  • Xi Chen

摘要

In traditional Vehicle-to-Vehicle-to-Infrastructure (V2V2I) routing algorithms, the beacon power of vehicles remains fixed, lacking adaptability to dynamic adjustments based on events or on-demand operational modes, leading to poor dynamic adaptability of vehicles and significant energy wastage. To enhance the flexibility of beacon power adjustment for vehicles in dynamic environments, while considering practical factors such as the signal coverage status, selfishness, and motion characteristics of routing vehicles, a self-adaptive V2V2I routing algorithm named Adaptive Beacon Power Routing algorithm(ABPR) is proposed. In the algorithm, in order to enhance the stability of the vehicle’s connection to RSU, vehicles first collect neighbor beacons and analyze the current signal coverage status of the vehicle. Subsequently, they adaptively adjust the beacon power of the vehicle, and then analyze available neighbor vehicle nodes through improved Q-learning, searching for the optimal neighbor node and selecting it as the next-hop route. Simulation experiments conducted on the Cologne dataset demonstrate that compared to RSAR and GPSR algorithms, the ABPR algorithm achieves an average improvement of 15% in residual energy consumption, 13.79% in selfish vehicle count, 38.58% in routing hop count, and 51.37% in routing link lifetime proportion under ± 30% standard beacon power conditions. This algorithm enhances the flexibility of beacon power adjustment in VANET environments while optimizing the overall data offloading link quality.