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Modelling the Diagnosis of Industry Internet of Things

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Smart Cities, Green Technologies, and Intelligent Transport Systems (VEHITS 2016, SMARTGREENS 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 738))

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Abstract

We consider necessary to discuss on a scientific article about the diagnosis of Internet of Things (IoT) for industry applications, e.g. controlled flexible manufacturing systems (FMS). In order to analyse and diagnose the main characteristics of these systems we focus on models realized with Markov chains of FMS with stochastic and not equal throughput rates. Discrete-event models assume that FMS is decomposed, and we study the following events: an Internet server fails, an Internet server is repaired, an Internet server memory buffer fills up, an Internet server memory buffer empties. The IoT diagnosis is performed with by calculating the time to absorption in Markov model of the IoT controlled FMS. Future developments of IoT diagnosis of FMS are also discussed in this work.

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Authors and Affiliations

  1. Stefan cel Mare University, 13 University, Suceava, Romania

    Calin Ciufudean & Corneliu Buzduga

Authors
  1. Calin Ciufudean
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  2. Corneliu Buzduga
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Corresponding author

Correspondence to Calin Ciufudean .

Editor information

Editors and Affiliations

  1. Dublin City University, Dublin 9, Ireland

    Markus Helfert

  2. Corporate Technology, Software and Systems, Siemens AG, Munich, Germany

    Cornel Klein

  3. Innovation Value Institute, Maynooth University, Maynooth, Kildare, Ireland

    Brian Donnellan

  4. Ford Research and Advanced Engineering, Dearborn, Michigan, USA

    Oleg Gusikhin

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Ciufudean, C., Buzduga, C. (2017). Modelling the Diagnosis of Industry Internet of Things. In: Helfert, M., Klein, C., Donnellan, B., Gusikhin, O. (eds) Smart Cities, Green Technologies, and Intelligent Transport Systems. VEHITS SMARTGREENS 2016 2016. Communications in Computer and Information Science, vol 738. Springer, Cham. https://doi.org/10.1007/978-3-319-63712-9_7

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  • DOI: https://doi.org/10.1007/978-3-319-63712-9_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63711-2

  • Online ISBN: 978-3-319-63712-9

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