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Fog in the Clouds: UAVs to Provide Edge Computing to IoT Devices

Published: 26 August 2020 Publication History

Abstract

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.

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cover image ACM Transactions on Internet Technology
ACM Transactions on Internet Technology  Volume 20, Issue 3
SI: Evolution of IoT Networking Architectures papers
August 2020
259 pages
ISSN:1533-5399
EISSN:1557-6051
DOI:10.1145/3408328
  • Editor:
  • Ling Liu
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 26 August 2020
Online AM: 07 May 2020
Accepted: 01 February 2020
Revised: 01 December 2019
Received: 01 July 2019
Published in TOIT Volume 20, Issue 3

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Author Tags

  1. Internet of Things
  2. Reinforcement Learning
  3. energy efficiency
  4. fog computing
  5. performance evaluation

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  • PRIN project Liquid_Edge

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