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dc.contributor.authorAlsamhi, Saeed H.
dc.contributor.authorAlmalki, Faris, A.
dc.contributor.authorAl-Dois, Hatem
dc.contributor.authorOthman, Soufiene Ben
dc.contributor.authorHassan, Jahan
dc.contributor.authorHawbani, Anmar
dc.contributor.authorSahal, Radyah
dc.contributor.authorLee, Brian
dc.contributor.authorSaleh, Hager
dc.identifier.citation68Alsamhi, S.H. et al (2021). Machine learning for smart environments in B5G networks: connectivity and QoS. Computational Intelligence and Neuroscience. Article ID 6805151.
dc.description.abstractThe number of Internet of Things (IoT) devices to be connected via the Internet is overgrowing. The heterogeneity and complexity of the IoT in terms of dynamism and uncertainty complicate this landscape dramatically and introduce vulnerabilities. Intelligent management of IoT is required to maintain connectivity, improve Quality of Service (QoS), and reduce energy consumption in real time within dynamic environments. Machine Learning (ML) plays a pivotal role in QoS enhancement, connectivity, and provisioning of smart applications. Therefore, this survey focuses on the use of ML for enhancing IoT applications. We also provide an in-depth overview of the variety of IoT applications that can be enhanced using ML, such as smart cities, smart homes, and smart healthcare. For each application, we introduce the advantages of using ML. Finally, we shed light on ML challenges for future IoT research, and we review the current literature based on existing works.en_US
dc.relation.ispartofComputational Intelligence and Neuroscienceen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.subjectMachine learningen_US
dc.subjectSmart environmentsen_US
dc.subjectB5G Networksen_US
dc.titleMachine learning for smart environments in B5G networks: connectivity and QoSen_US
dc.contributor.affiliationAthlone Institute of Technologyen_US
dc.contributor.sponsorScience Foundation Ireland (SFI) under grant no. SFI/16/RC/3918 (CONFIRM) and Marie Skłodowska-Curie grant agreement no. 847577 co-funded by the European Regional Development Fund. Taif University Researchers Supporting Project (no. TURSP-2020/265).en_US
dc.identifier.orcid 0000-0003-2857-6979en_US
dc.subject.departmentSoftware Research Institute AITen_US

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Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International