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.

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

Faraci G.;Grasso C.;Schembra G.
2020-01-01

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.
2020
energy efficiency
fog computing
Internet of Things
performance evaluation
Reinforcement Learning
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/496987
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