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

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Artificial Intelligence, Machine Learning, and Optimization Tools for Smart Cities

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 186))

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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.

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Acknowledgements

This work has been partially funded by the Spanish Ministry of Science, Innovation and Universities through the “Retos investigación” programme (RTI2018-095168-B-C53), by Jaume I University “Pla de promoció de la investigació 2020” programme (UJI-B2020-36) and by Generalitat Valenciana “Promoció de la investigació 2020” programme (AICO/2020/046). Óscar Belmonte-Fernández had a grant by the Spanish Ministry of Science, Innovation and Universities (PRX18/00123) for developing part of this work. Sergio Trilles has been funded by the Juan de la Cierva - Incorporación postdoctoral programme of the Ministry of Science and Innovation - Spanish government (IJC2018-035017-I).

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The authors declare that there are no conflicts of interest regarding the publication of this chapter.

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  1. Institute of New Imaging Technologies, Castellón de La Plana, Spain

    Óscar Belmonte-Fernández, Emilio Sansano-Sansano, Sergio Trilles & Antonio Caballer-Miedes

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  1. Óscar Belmonte-Fernández
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Correspondence to Óscar Belmonte-Fernández .

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  1. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  2. University of Macedonia & Le.D.R.A. Group, Thessaloniki, Greece

    Stamatina Th. Rassia

  3. Center for Applied Optimization, University of Florida, Gainesville, FL, USA

    Arsenios Tsokas

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Belmonte-Fernández, Ó., Sansano-Sansano, E., Trilles, S., Caballer-Miedes, A. (2022). A Reactive Architectural Proposal for Fog/Edge Computing in the Internet of Things Paradigm with Application in Deep Learning. In: Pardalos, P.M., Rassia, S.T., Tsokas, A. (eds) Artificial Intelligence, Machine Learning, and Optimization Tools for Smart Cities. Springer Optimization and Its Applications, vol 186. Springer, Cham. https://doi.org/10.1007/978-3-030-84459-2_9

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