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首页> 外文期刊>ACM Transactions on Internet Technology >Fog in the Clouds: UAVs to Provide Edge Computing to IoT Devices
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Fog in the Clouds: UAVs to Provide Edge Computing to IoT Devices

机译：云层中的雾：无人机提供到IoT设备的边缘计算

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摘要

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 loT 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 loT network to a fog platform with appropriate links, especially if loT 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.
机译：事物互联网（物联网）通过将多功能和大规模的智能物体集合连接到互联网，在我们日常生活中发挥重要作用，成为了一个巨大的范式转变。由IoT设备产生的数据可以生成许多无法在Lot设备上本地执行的计算任务。最常见的解决方案正在将这些任务卸载到具有更高计算和存储功能的外部设备，通常由远程云中的集中式服务器提供，或者使用雾计算范例在边缘中提供。尽管如此，在某些IOT方案中，存在具有适当链接的雾化平台的远程或具有挑战性的区域，特别是如果Lot设备产生需要实时处理的大量数据。为此目的，在本文中，我们建议使用无人驾驶飞行器（无人机）作为雾节点。虽然这个想法并不是新的，这是考虑在船上无人机上安装的计算元件的功耗的第一项工作，这是至关重要的，因为它可能影响飞行任务持续时间。系统控制器（SC）负责通过最大化电力消耗，作业损耗概率和处理延迟来决定运行时在运行时确定有源CPU的数量。强化学习（RL）用于支持其决策中的SC。在一个用例中执行数值分析，以展示如何使用文章中介绍的模型来决定计算元素的计算能力，以便在可用的CPU和CPU时钟速度的数量方面确定计算能力，并评估实现的性能增益提出的框架。

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