Enhancing_RFID_System_Configuration_through_Semantic_Modelling_KER__latest__ (1).pdf (3.51 MB)
Download fileEnhancing RFID system configuration through semantic modelling
journal contribution
posted on 2021-06-18, 13:35 authored by Eleni Tsalapati, James Tribe, Paul GoodallPaul Goodall, Robert I.M. Young, Tom JacksonTom Jackson, Andrew WestAndrew WestRadio-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
Find out more...Embedded Integrated Intelligent Systems for Manufacturing
Engineering and Physical Sciences Research Council
Find out more...History
School
- Mechanical, Electrical and Manufacturing Engineering
- Business and Economics
Department
- Business
Published in
The Knowledge Engineering ReviewVolume
36Publisher
Cambridge University PressVersion
- AM (Accepted Manuscript)
Rights holder
© The AuthorsPublisher 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
2021-05-18Publication date
2021-07-27Copyright date
2021ISSN
0269-8889eISSN
1469-8005Publisher version
Language
- en