Internet of Things (IoT) has emerged as a huge paradigm shift by connecting a versatile and massive collection of smart objects to the Internet, coming to play an important role in our daily lives. Data produced by IoT devices can generate a number of computational tasks that cannot be executed locally on the IoT devices. The most common solution is offloading these tasks to external devices with higher computational and storage capabilities, usually provided by centralized servers in remote clouds or on the edge by using the fog computing paradigm. Nevertheless, in some IoT scenarios there are remote or challenging areas where it is difficult to connect an IoT network to a fog platform with appropriate links, especially if IoT devices produce a lot of data that require processing in real-time. To this purpose, in this article, we propose to use unmanned aerial vehicles (UAVs) as fog nodes. Although this idea is not new, this is the first work that considers power consumption of the computing element installed on board UAVs, which is crucial, since it may influence flight mission duration. A System Controller (SC) is in charge of deciding the number of active CPUs at runtime by maximizing an objective function weighing power consumption, job loss probability, and processing latency. Reinforcement Learning (RL) is used to support SC in its decisions. A numerical analysis is carried out in a use case to show how to use the model introduced in the article to decide the computation power of the computing element in terms of number of available CPUs and CPU clock speed, and evaluate the achieved performance gain of the proposed framework.

中文翻译：

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云中的雾

物联网 (IoT) 已经成为一种巨大的范式转变，通过将多用途和海量的智能对象集合连接到互联网，在我们的日常生活中发挥重要作用。物联网设备产生的数据可以生成许多无法在物联网设备上本地执行的计算任务。最常见的解决方案是将这些任务卸载到具有更高计算和存储能力的外部设备，通常由远程云或边缘的集中式服务器使用雾计算范式提供。然而，在某些物联网场景中，在一些偏远或具有挑战性的区域，很难通过适当的链接将物联网网络连接到雾平台，尤其是当物联网设备产生大量需要实时处理的数据时。为此，在本文中，我们建议使用无人机（UAV）作为雾节点。虽然这个想法并不新鲜，但这是第一项考虑安装在无人机上的计算元件的功耗的工作，这一点至关重要，因为它可能会影响飞行任务的持续时间。系统控制器 (SC) 负责通过最大化衡量功耗、工作丢失概率和处理延迟的目标函数来决定运行时活动 CPU 的数量。强化学习 (RL) 用于支持 SC 的决策。在一个用例中进行了数值分析，展示了如何使用文章中介绍的模型来确定计算单元的计算能力，包括可用 CPU 的数量和 CPU 时钟速度，并评估实现的性能增益。提议的框架。