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.

中文翻译：

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智慧城市中无人机的车辆辅助计算卸载

智慧城市成为提高城市运营效率和人们舒适度的有前途的范例。借助嵌入式多传感器，无人飞行器（UAV）在智慧城市中收集传感数据和提供社会服务方面具有巨大潜力。然而，由于无人飞行器的电池寿命和处理能力有限，在智慧城市中迫切需要有效的无人飞行器卸载方案。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中的移动车辆来提高卸载效率。我们首先为无人机建立卸载模型，以确定卸载策略。接下来，要选择最适合卸载的车辆，我们根据无人机和车辆的偏好列表制定了一个匹配方案，以得出无人机和车辆之间的最佳匹配。此后，为了提高卸载效率并最大化无人机和车辆的效用，将无人机和车辆之间的数据计算交易过程建模为讨价还价游戏。此外，提出了一种用于无人机和车辆的卸载算法，以获得最优策略。最后，进行仿真以验证所提出的卸载方案的效率。结果表明，提出的卸载方案可以大大节省资源，提高无人机和车辆的实用性。无人机和车辆之间计算数据的交易过程被建模为讨价还价游戏。此外，提出了一种用于无人机和车辆的卸载算法，以获得最优策略。最后，进行仿真以验证所提出的卸载方案的效率。结果表明，提出的卸载方案可以大大节省资源，提高无人机和车辆的实用性。无人机和车辆之间计算数据的交易过程被建模为讨价还价游戏。此外，提出了一种用于无人机和车辆的卸载算法，以获得最优策略。最后，进行仿真以验证所提出的卸载方案的效率。结果表明，提出的卸载方案可以大大节省资源，提高无人机和车辆的实用性。