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dc.contributor.authorIftikhar, Sundas
dc.contributor.authorGill, Sukhpal Singh
dc.contributor.authorSong, Chenghao
dc.contributor.authorXu, Minxian
dc.contributor.authorAslanpour, Mohammad Sadegh
dc.contributor.authorToosi, Adel N.
dc.contributor.authorDu, Junhui
dc.contributor.authorWu, Huaming
dc.contributor.authorGhosh, Shreya
dc.contributor.authorChowdhury, Deepraj
dc.contributor.authorGolec, Muhammed
dc.contributor.authorKumar, Mohit
dc.contributor.authorAbdelmoniem, Ahmed M.
dc.contributor.authorCuadrado, Felix
dc.contributor.authorVarghese, Blesson
dc.contributor.authorRana, Omer
dc.contributor.authorDustdar, Schahram
dc.contributor.authorUhlig, Steve
dc.identifier.citationIftikhar , S , Gill , S S , Song , C , Xu , M , Aslanpour , M S , Toosi , A N , Du , J , Wu , H , Ghosh , S , Chowdhury , D , Golec , M , Kumar , M , Abdelmoniem , A M , Cuadrado , F , Varghese , B , Rana , O , Dustdar , S & Uhlig , S 2023 , ' AI-based fog and edge computing : a systematic review, taxonomy and future directions ' , Internet of Things , vol. 21 , 100674 .
dc.identifier.otherRIS: urn:43B843418C60625785776B15BE8E6123
dc.descriptionFunding: Sundas Iftikhar would like thank the Higher Education Commission (HEC) Pakistan for their support and funding (Grant No. 2-5/FDPOS/HRD/UoK/QMUL/2020/1). This work is partially funded by Chinese Academy of Sciences President’s International Fellowship Initiative (Grant No. 2023VTC0006), and Shenzhen Science and Technology Program (Grant No. RCBS20210609104609044).en
dc.description.abstractResource management in computing is a very challenging problem that involves making sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse nature of workload, and the unpredictability of fog/edge computing environments have made resource management even more challenging to be considered in the fog landscape. Recently Artificial Intelligence (AI) and Machine Learning (ML) based solutions are adopted to solve this problem. AI/ML methods with the capability to make sequential decisions like reinforcement learning seem most promising for these type of problems. But these algorithms come with their own challenges such as high variance, explainability, and online training. The continuously changing fog/edge environment dynamics require solutions that learn online, adopting changing computing environment. In this paper, we used standard review methodology to conduct this Systematic Literature Review (SLR) to analyze the role of AI/ML algorithms and the challenges in the applicability of these algorithms for resource management in fog/edge computing environments. Further, various machine learning, deep learning and reinforcement learning techniques for edge AI management have been discussed. Furthermore, we have presented the background and current status of AI/ML-based Fog/Edge Computing. Moreover, a taxonomy of AI/ML-based resource management techniques for fog/edge computing has been proposed and compared the existing techniques based on the proposed taxonomy. Finally, open challenges and promising future research directions have been identified and discussed in the area of AI/ML-based fog/edge computing.
dc.relation.ispartofInternet of Thingsen
dc.subjectArtificial Intelligenceen
dc.subjectCloud computingen
dc.subjectFog computingen
dc.subjectEdge computingen
dc.subjectMachine learningen
dc.subjectInternet of Thingsen
dc.subjectSystematic literature reviewen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.titleAI-based fog and edge computing : a systematic review, taxonomy and future directionsen
dc.typeJournal itemen
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.description.statusPeer revieweden

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