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Machine learning for smart environments in B5G networks: Connectivity and QoS

journal contribution
posted on 11.10.2021, 23:36 by Saeed H Alsamhi, Faris A Almalki, Hatem Al-Dois, Soufiene Ben Othman, Jahan HassanJahan Hassan, Ammar Hawbani, Radyah Sahal, Brian Lee, Hager Saleh
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.

History

Volume

2021

Start Page

1

End Page

23

Number of Pages

23

eISSN

1687-5273

ISSN

1687-5265

Publisher

Hindawi

Additional Rights

CC BY 4.0

Language

en

Peer Reviewed

Yes

Open Access

Yes

Acceptance Date

25/08/2021

External Author Affiliations

South Valley University, Egypt; University of Science and Technology of China; University of Sousse, Tunisia; Taif University, Saudi Arabia; University College Cork, Athlone Institute of Technology, Ireland; Ibb University, Yemen

Era Eligible

Yes

Journal

Computational Intelligence and Neuroscience

Article Number

6805151