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A fuzzy logic recommendation system to support the design of cloud-edge data analysis in cyber-physical systems

dc.contributor.authorQueiroz, Jonas
dc.contributor.authorLeitão, Paulo
dc.contributor.authorOliveira, Eugénio
dc.date.accessioned2022-03-15T11:04:43Z
dc.date.available2022-03-15T11:04:43Z
dc.date.issued2022
dc.description.abstractThe ongoing 4th industrial revolution is characterized by the digitization of industrial environments, mainly based on the use of Internet of Things, Cloud Computing and Artificial Intelligence (AI). Regarding AI, although data analysis has shown to be a key enabler of industrial Cyber-Physical Systems (CPS) in the development of smart machines and products, the traditional Cloud-centric solutions are not suitable to attend the data and time-sensitive requirements. Complementary to Cloud, Edge Computing has been adopted to enable the data processing capabilities at or close to the physical components. However, defining which data analysis tasks should be deployed on Cloud and Edge computational layers is not straightforward. This work proposes a framework to guide engineers during the design phase to determine the best way to distribute the data analysis capabilities among computational layers, contributing for a lesser ad-hoc design of distributed data analysis in industrial CPS. Besides defining the guidelines to identify the data analysis requirements, the core contribution relies on a Fuzzy Logic recommendation system for suggesting the most suitable layer to deploy a given data analysis task. The proposed approach is validated in a smart machine testbed that requires the implementation of different data analysis tasks for its operation.pt_PT
dc.description.sponsorshipThis work was supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/05757/2020.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationQueiroz, Jonas; Leitao, Paulo; Oliveira, Eugenio (2022). A fuzzy logic recommendation system to support the design of cloud-edge data analysis in cyber-physical systems. IEEE Open Journal of the Industrial Electronics Society. ISSN 2644-1284. 3, p. 174-187pt_PT
dc.identifier.doi10.1109/OJIES.2022.3152725pt_PT
dc.identifier.urihttp://hdl.handle.net/10198/25230
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIEEEpt_PT
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectCyber-physical system designpt_PT
dc.subjectDistributed data analysispt_PT
dc.subjectFuzzy recommendation systempt_PT
dc.titleA fuzzy logic recommendation system to support the design of cloud-edge data analysis in cyber-physical systemspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.citation.endPage187pt_PT
oaire.citation.startPage174pt_PT
oaire.citation.titleIEEE Open Journal of the Industrial Electronics Societypt_PT
oaire.citation.volume3pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameQueiroz
person.familyNameLeitão
person.givenNameJonas
person.givenNamePaulo
person.identifierhttps://scholar.google.com/citations?user=UnhjE9gAAAAJ
person.identifierA-8390-2011
person.identifier.ciencia-idBF12-BBDD-CCC5
person.identifier.ciencia-id8316-8F13-DA71
person.identifier.orcid0000-0001-5416-4762
person.identifier.orcid0000-0002-2151-7944
person.identifier.scopus-author-id57188655139
person.identifier.scopus-author-id35584388900
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication49c3a549-2500-4d74-8657-9d9baafffea3
relation.isAuthorOfPublication68d9eb25-ad4f-439b-aeb2-35e8708644cc
relation.isAuthorOfPublication.latestForDiscovery68d9eb25-ad4f-439b-aeb2-35e8708644cc
relation.isProjectOfPublication6e01ddc8-6a82-4131-bca6-84789fa234bd
relation.isProjectOfPublication.latestForDiscovery6e01ddc8-6a82-4131-bca6-84789fa234bd

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