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

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Óscar Belmonte-Fernández & Emilio Sansano-Sansano & Sergio Trilles & Antonio Caballer-Miedes, 2022. "A Reactive Architectural Proposal for Fog/Edge Computing in the Internet of Things Paradigm with Application in Deep Learning," Springer Optimization and Its Applications, in: Panos M. Pardalos & Stamatina Th. Rassia & Arsenios Tsokas (ed.), Artificial Intelligence, Machine Learning, and Optimization Tools for Smart Cities, pages 155-175, Springer.
    • Handle: RePEc:spr:spochp:978-3-030-84459-2_9
    DOI: 10.1007/978-3-030-84459-2_9
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