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首页> 外文期刊>IEEE Transactions on Intelligent Transportation Systems >Vehicle Assisted Computing Offloading for Unmanned Aerial Vehicles in Smart City
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Vehicle Assisted Computing Offloading for Unmanned Aerial Vehicles in Smart City

机译:车辆辅助计算智能城市无人空中车辆的卸载

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摘要

Smart city emerges a promising paradigm for improving operational efficiency of city and comfort of people. With embedded multi-sensors, Unmanned Aerial Vehicles (UAVs) hold great potential for collecting sensing data and providing social services in smart city. However, due to the limited battery lifetime and processing capacities of UAVs, the efficient offloading scheme of UAVs is urgently needed in smart city. Therefore, in this article, a vehicle-assisted computing offloading architecture for UAVs is proposed to improve offloading efficiency by harnessing the moving vehicles in smart city. We first develop an offloading model for UAVs to determine the offloading strategy. Next, to select the optimal vehicles for offloading, we formulate a matching scheme based on the preference lists of UAVs and vehicles to derive the optimal matching between UAVs and vehicles. After that, to improve the offloading efficiency and maximize the utilities of UAVs and vehicles, the transaction process of computing data between UAVs and vehicles is modeled as a bargaining game. Moreover, an offloading algorithm for UAVs and vehicles is proposed to obtain the optimal strategy. Finally, simulations are performed to validate the efficiency of the proposed offloading scheme. The results demonstrate that the proposed offloading scheme can significantly save resource and improve the utilities of UAVs and vehicles.
机译:智能城市出现了提高城市的运营效率和舒适的人的有前途的范式。通过嵌入式多传感器,无人驾驶飞行器(无人机)占据感应数据并在智能城市提供社会服务的巨大潜力。但是,由于电池寿命有限和无人机的加工能力,智能城市迫切需要UAV的有效卸载方案。因此,在本文中,提出了一种用于无人机的车辆辅助计算卸载架构,以通过利用智能城市的移动车辆来提高卸载效率。我们首先为无人机开发卸载模型,以确定卸载策略。接下来,为了选择卸载的最佳车辆,我们根据无人机和车辆的偏好列表制定匹配方案,以导出UVS和车辆之间的最佳匹配。之后,为了提高卸载效率并最大限度地提高无人驾驶和车辆的实用程序,UAV和车辆之间的计算数据的交易过程被建模为讨价还价游戏。此外,提出了一种用于UAV和车辆的卸载算法以获得最佳策略。最后,执行仿真以验证所提出的卸载方案的效率。结果表明,所提出的卸载方案可以显着节省资源,并改善无人机和车辆的公用事业。

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