A Reactive Architectural Proposal for Fog/Edge Computing in the Internet of Things Paradigm with Application in Deep Learning

Óscar Belmonte-Fernández (), Emilio Sansano-Sansano (), Sergio Trilles () and Antonio Caballer-Miedes ()
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Óscar Belmonte-Fernández: Institute of New Imaging Technologies
Emilio Sansano-Sansano: Institute of New Imaging Technologies
Sergio Trilles: Institute of New Imaging Technologies
Antonio Caballer-Miedes: Institute of New Imaging Technologies

A chapter in Artificial Intelligence, Machine Learning, and Optimization Tools for Smart Cities, 2022, pp 155-175 from Springer

Abstract: Abstract The fog/edge computing paradigm has been proposed to tackle the challenges inherent to the Internet of Things realm. Timely response, bandwidth efficiency, context awareness, data privacy and safety, and mobility support are some of the requirements that are only partially covered by cloud computing. A collaboration of both paradigms when developing deep learning solutions for the Internet of Things can be seen as a win–win approach. Time-consuming and hardware demanding deep learning models are built in the cloud with data provided by the fog/edge, and then these models are returned to the fog/edge for use. This work proposes a new architecture, based on the principles of reactive systems, for building responsive, resilient and elastic systems, where all components interact with one another through asynchronous message passing. As a proof of concept, two particular applications of this architecture in the realms of e-health and precision agriculture are presented.

Date: 2022
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DOI: 10.1007/978-3-030-84459-2_9

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