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Enhancing RFID system configuration through semantic modelling

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journal contribution
posted on 18.06.2021, 13:35 authored by Eleni Tsalapati, James Tribe, Paul GoodallPaul Goodall, Robert I.M. Young, Tom JacksonTom Jackson, Andrew WestAndrew West
Radio-Frequency Identification System technology is a key element for the realisation of the Industry 4.0 vision, as it is vital for tasks such as entity tracking, identification and asset management. However, the plethora of RFID systems’ elements in combination with the wide range of factors that need to be taken under consideration along with the interrelations among them, make the problem of identification and design of the right RFID system, based on users’ needs particularly complex. The research outlined in this paper seeks to optimise this process by developing an integrating schema that will encapsulate this information in a form that is both human and machine processible. Human readability will allow a shared understanding of the RFID-technology domain; machine readability, automated reasoning engines to perform logical deduction techniques returning implicit information. For this purpose, the novel RFID System Configuration Ontology (RFID SCO) is developed. Hence, non-RFID experts are enabled to identify the most suitable RFID system according to their needs and RFID-experts to retrieve all the relevant information required for the efficient design of the corresponding RFID system. The RFID SCO is validated and tested successfully against real-world scenarios provided by domain experts.

Funding

Adaptive Informatics for Intelligent Manufacturing (AI2M)

Engineering and Physical Sciences Research Council

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Embedded Integrated Intelligent Systems for Manufacturing

Engineering and Physical Sciences Research Council

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History

School

  • Mechanical, Electrical and Manufacturing Engineering
  • Business and Economics

Department

  • Business

Published in

The Knowledge Engineering Review

Volume

36

Publisher

Cambridge University Press

Version

AM (Accepted Manuscript)

Rights holder

© The Authors

Publisher statement

This article has been published in a revised form in The Knowledge Engineering Review https://doi.org/10.1017/S0269888921000096. This version is published under a Creative Commons CC-BY-NC-ND. No commercial re-distribution or re-use allowed. Derivative works cannot be distributed. © The Authors.

Acceptance date

18/05/2021

Publication date

2021-07-27

Copyright date

2021

ISSN

0269-8889

eISSN

1469-8005

Language

en

Depositor

Prof Tom Jackson. Deposit date: 18 June 2021

Article number

e11

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