Publication:
A review of fog computing and machine learning: Concepts, applications, challenges, and open issues

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dc.citedby107
dc.contributor.authorAbdulkareem K.H.en_US
dc.contributor.authorMohammed M.A.en_US
dc.contributor.authorGunasekaran S.S.en_US
dc.contributor.authorAl-Mhiqani M.N.en_US
dc.contributor.authorMutlag A.A.en_US
dc.contributor.authorMostafa S.A.en_US
dc.contributor.authorAli N.S.en_US
dc.contributor.authorIbrahim D.A.en_US
dc.contributor.authorid57197854295en_US
dc.contributor.authorid57192089894en_US
dc.contributor.authorid55652730500en_US
dc.contributor.authorid57197853907en_US
dc.contributor.authorid57203180481en_US
dc.contributor.authorid37036085800en_US
dc.contributor.authorid56693765600en_US
dc.contributor.authorid57191545030en_US
dc.date.accessioned2023-05-29T07:28:50Z
dc.date.available2023-05-29T07:28:50Z
dc.date.issued2019
dc.descriptionApplications; Computer graphics; Decision making; Energy utilization; Fog; Green computing; Internet of things; Learning algorithms; Learning systems; Machine learning; Natural language processing systems; Speech recognition; Computing applications; Computing paradigm; Internet of Things (IOT); NAtural language processing; Neuromorphic computing; Research challenges; Research domains; Resource management; Fog computingen_US
dc.description.abstractSystems based on fog computing produce massive amounts of data; accordingly, an increasing number of fog computing apps and services are emerging. In addition, machine learning (ML), which is an essential area, has gained considerable progress in various research domains, including robotics, neuromorphic computing, computer graphics, natural language processing (NLP), decision-making, and speech recognition. Several researches have been proposed that study how to employ ML to settle fog computing problems. In recent years, an increasing trend has been observed in adopting ML to enhance fog computing applications and provide fog services, like efficient resource management, security, mitigating latency and energy consumption, and traffic modeling. Based on our understanding and knowledge, there is no study has yet investigated the role of ML in the fog computing paradigm. Accordingly, the current research shed light on presenting an overview of the ML functions in fog computing area. The ML application for fog computing become strong end-user and high layers services to gain profound analytics and more smart responses for needed tasks. We present a comprehensive review to underline the latest improvements in ML techniques that are associated with three aspects of fog computing: management of resource, accuracy, and security. The role of ML in edge computing is also highlighted. Moreover, other perspectives related to the ML domain, such as types of application support, technique, and dataset are provided. Lastly, research challenges and open issues are discussed. � 2013 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo8869895
dc.identifier.doi10.1109/ACCESS.2019.2947542
dc.identifier.epage153140
dc.identifier.scopus2-s2.0-85074636899
dc.identifier.spage153123
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85074636899&doi=10.1109%2fACCESS.2019.2947542&partnerID=40&md5=f6c93a8b97e9e87960d649efc7a14458
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24919
dc.identifier.volume7
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleIEEE Access
dc.titleA review of fog computing and machine learning: Concepts, applications, challenges, and open issuesen_US
dc.typeReviewen_US
dspace.entity.typePublication
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