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Title	Machine Learning For Smart Environments In B5G Networks: Connectivity And Qos
ID_Doc	35982
Authors	Alsamhi S.H.; Almalki F.A.; Al-Dois H.; Ben Othman S.; Hassan J.; Hawbani A.; Sahal R.; Lee B.; Saleh H.
Year	2021
Published	Computational Intelligence and Neuroscience, 2021
DOI	http://dx.doi.org/10.1155/2021/6805151
Abstract	The 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. © 2021 Saeed H. Alsamhi et al.
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