Machine Learning for Smart Environments in B5G Networks: Connectivity and QoS

Saeed H Alsamhi; Faris A Almalki; Hatem Al-Dois; Soufiene Ben Othman; Jahan Hassan; Ammar Hawbani; Radyah Sahal; Brian Lee; Hager Saleh

doi:10.1155/2021/6805151

Machine Learning for Smart Environments in B5G Networks: Connectivity and QoS

Comput Intell Neurosci. 2021 Sep 18:2021:6805151. doi: 10.1155/2021/6805151. eCollection 2021.

Authors

Saeed H Alsamhi^{1

2}, Faris A Almalki³, Hatem Al-Dois⁴, Soufiene Ben Othman^{5

6}, Jahan Hassan⁷, Ammar Hawbani⁸, Radyah Sahal⁹, Brian Lee¹, Hager Saleh¹⁰

Affiliations

¹ Athlone Institute of Technology, Athlone, Ireland.
² Ibb University, Ibb, Yemen.
³ Department of Computer Engineering, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.
⁴ Department of Electrical Engineering, Ibb University, Ibb, Yemen.
⁵ PRINCE Laboratory Research, ISITCom, Hammam Sousse, University of Sousse, Sousse, Tunisia.
⁶ Tunisia and School of Engineering and Technology, Sharda University, Greater Noida, India.
⁷ Central Queensland University, Sydney, NSW 2000, Australia.
⁸ School of Computer Science and Technology, University of Science and Technology of China, Hefei, China.
⁹ School of Computer Science and Information Technology, University College Cork, Cork, Ireland.
¹⁰ Faculty of Computers and Artificial Intelligence, South Valley University, Hurghada, Egypt.

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.

Publication types

Review

MeSH terms

Cities
Delivery of Health Care
Internet of Things*
Machine Learning