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5G and MEC Based Data Streaming Architecture for Industrial AI

  • Conference paper
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Innovative Intelligent Industrial Production and Logistics (IN4PL 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1886))

  • 131 Accesses

Abstract

Availability of computation capabilities and real-time machine data is one key requirement of smart manufacturing systems. Latency, privacy and security issues of cloud computing for Industrial artificial intelligence (AI) led to the edge computing paradigm, where computation is performed close to the data source. As on-premise edge deployments require companies to allocate budget and human resources to acquire and maintain the required information technologies (IT) infrastructure and equipment, they are not feasible for several companies. However, 5G can merge advantages of previous alternatives. Multi-Access Edge Computing (MEC) servers deployed at the edge of the 5G network close to the final user, offer security, privacy, scalability, high throughput and low latency advantages. MECs are suitable for industrial AI, while industrial companies do not face the burden of acquiring and maintaining servers and communication infrastructures. This paper proposes a real-time high-frequency data streaming architecture to deploy Industrial AI applications at MECs. The architecture has been successfully validated with data sent through a 5G network to a Kafka broker at the MEC, where different microservices are deployed in a Kubernetes cluster. The performance of the architecture has been investigated to analyze the capabilities of 5G and MEC to cope with the requirements of Industrial AI applications.

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Authors and Affiliations

  1. Faculty of Engineering, University of Deusto, Mundaitz Kalea, 50, 20012, Donostia-San Sebastian, Spain

    Telmo Fernández De Barrena Sarasola & Ander García

  2. Department of Data Intelligence for Energy and Industrial Processes, Fundación Vicomtech, Basque Research and Technology Alliance (BRTA), Mikeletegi 57, 20009, Donostia-San Sebastian, Spain

    Telmo Fernández De Barrena Sarasola, Juan Luis Ferrando Chacón & Ander García

  3. Department of Signal Theory and Communications, Universitat Politècnica de Catalunya (UPC), Campus Nord, Carrer de Jordi Girona, 1, 3, 08034, Barcelona, Spain

    Michail Dalgitsis

Authors
  1. Telmo Fernández De Barrena Sarasola
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  2. Juan Luis Ferrando Chacón
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  3. Ander García
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  4. Michail Dalgitsis
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Corresponding author

Correspondence to Telmo Fernández De Barrena Sarasola .

Editor information

Editors and Affiliations

  1. Politecnico di Milano, Milan, Italy

    Sergio Terzi

  2. University of Paris - EST Créteil, Créteil, France

    Kurosh Madani

  3. Ford Motor Company, Dearborn, MI, USA

    Oleg Gusikhin

  4. University of Lorraine, Nancy, France

    Hervé Panetto

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Fernández De Barrena Sarasola, T., Chacón, J.L.F., García, A., Dalgitsis, M. (2023). 5G and MEC Based Data Streaming Architecture for Industrial AI. In: Terzi, S., Madani, K., Gusikhin, O., Panetto, H. (eds) Innovative Intelligent Industrial Production and Logistics. IN4PL 2023. Communications in Computer and Information Science, vol 1886. Springer, Cham. https://doi.org/10.1007/978-3-031-49339-3_3

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  • DOI: https://doi.org/10.1007/978-3-031-49339-3_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-49338-6

  • Online ISBN: 978-3-031-49339-3

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