In the Internet of Vehicles, vehicles lack reliable network access in scenarios such as unmanned areas. Satellite-Terrestrial Integrated Network (STIN) can provide reliable network access for vehicles in scenarios without cellular network coverage. However, the existing satellite-terrestrial traffic scheduling methods often overlook the differences in traffic types, leading to limited quality of service (QoS) for STIN vehicles. To address this issue, we first model the attributes such as satellite-terrestrial visibility, filtering satellites that meet the minimum threshold requirements. Secondly, we design a customized QoS evaluation function and integrate it with switching costs and load balancing indices to create a reinforcement learning reward function, aiming to optimize satellite-terrestrial handover decisions by maximizing the reward. Finally, we develop an intelligent traffic allocation strategy based on reinforcement learning to ensure successful handover rates and onboard load balancing. Simulation results indicate that the proposed algorithm maintains high vehicle QoS and onboard load balance while reducing handover failure rates.


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    Title :

    Traffic Scheduling Mechanism of Satellite-Terrestrial Integrated Networks for Internet of Vehicles


    Contributors:
    Pan, Jiehua (author) / Wu, Chao (author) / Gong, Chen (author) / Zhang, Zhiyong (author) / Ke, Xinqin (author) / Zhu, Zixiao (author)


    Publication date :

    2024-11-08


    Size :

    616183 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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