Delay-Effective Task Offloading Technology in Internet of Vehicles: From the Perspective of the Vehicle Platooning
<|reference_start|>Delay-Effective Task Offloading Technology in Internet of Vehicles: From the Perspective of the Vehicle Platooning: The task offloading technology plays a crucial vital role in the Internet of Vehicle (IoV) with the demands of delay minimum, by jointly optimizing the heterogeneous computing resources supported by the vehicles, roadside units (RSUs), and macro base stations (MBSs). In previous works, on the one hand, they ignored the wireless interference among the exchange and sharing of the task data. On the other hand, the available resources supported by the vehicles that have similar driving behaviors, which can form a vehicle platooning (VEH-PLA) and effectively integrate the resources of individual vehicle, has not been addressed. In addition, as a novel resource management paradigm, the VEH-PLA should consider the task categorization, since vehicles in VEH-PLA may have the same task offloading requests, which also has not attracted enough attention. In this paper, considering the wireless interference, mobility, VEH-PLA, and task categorization, we propose four kinds of task offloading models for the purpose of the processing delay minimum. Furthermore, by utilizing centralized training and decentralized execution (CTDE) based on multi-agent deep reinforcement learning (MADRL), we present a task offloading decision-making method to find the global optimal offloading decision, resulting in a significant enhancement in the load balancing of resources and processing delay. Finally, the simulations demonstrate that the proposed method significantly outperforms traditional task offloading methods in terms of the processing delay minimum while keeping the resource load balancing.<|reference_end|>
@article{yu2024delay-effective,
    title={Delay-Effective Task Offloading Technology in Internet of Vehicles: From
  the Perspective of the Vehicle Platooning},
    author={Kan Yu, Fuze Zhu, Xiaowu Liu, Zhiyong Feng, Dong Li},
    journal={arXiv preprint arXiv:2405.16060},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.16060},
    primaryClass={cs.IT math.IT}
}