An Multi-resources Integration Empowered Task Offloading in Internet of Vehicles: From the Perspective of Wireless Interference
<|reference_start|>An Multi-resources Integration Empowered Task Offloading in Internet of Vehicles: From the Perspective of Wireless Interference: The task offloading technology plays a vital role in the Internet of Vehicles (IoV), by satisfying the diversified demands of the vehicles, such as the energy consumption and processing latency of the computing task. Different from the previous works, on the one hand, they ignored the wireless interference of communications among vehicle-to-vehicle (V2V), as well as between vehicles and roadside units (RSU); on the other hand, the available resources of parked vehicles on the roadside and other moving vehicles on the road are also ignored. In this paper, first of all, we adopt a truncated Gaussian distribution for modeling the vehicle moving speed, instead of the simplistic average speed models in prior studies. Then, with the consideration of wireless interference and effective communication duration existing in V2V and RSUs, we establish an analytical framework of the task offloading, characterized by the energy consumption and processing delay, by integrating computing resources of parked/moving vehicles and RSUs. Furthermore, inspired by the method of multi-agent deterministic policy gradient (MADDPG), we address a joint optimization of the energy consumption and processing delay of the computing task, while ensuring the load balancing of the resources. Finally, the simulations demonstrate the effectiveness and correctness of the proposed MADDPG. In particular, compared with the current popular methods of the task offloading, the MADDPG shows the best performance, in terms of convergence speed, energy consumption and processing delay.<|reference_end|>
@article{liu2024an,
    title={An Multi-resources Integration Empowered Task Offloading in Internet of
  Vehicles: From the Perspective of Wireless Interference},
    author={Xiaowu Liu, Yun Wang, Kan Yu, Dianxia Chen, Dong Li, Qixun Zhang,
  Zhiyong Feng},
    journal={arXiv preprint arXiv:2405.16078},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.16078},
    primaryClass={cs.IT math.IT}
}