Survey and taxonomy of clustering algorithms in 5G

Khan, M. F., Yau, K.-L. A., Noor, R. M. and Imran, M. A. (2020) Survey and taxonomy of clustering algorithms in 5G. Journal of Network and Computer Applications, 154, 102539. (doi: 10.1016/j.jnca.2020.102539)

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Abstract

The large-scale deployment of fifth generation (5G) is expected to produce a massive amount of data with high variability due to ultra-densification and the rapid increase in a heterogeneous range of applications and services (e.g., virtual reality, augmented reality, and driver-less vehicles), and network devices (e.g., smart gadgets and sensors). Clustering organizes network topology by segregating nodes with similar interests or behaviors in a network into logical groups in order to achieve network-level and cluster-level enhancements, particularly cluster stability, load balancing, social awareness, fairness, and quality of service. Clustering has been investigated to support mobile user equipment (UE) in access networks, whereby UEs form clusters themselves and may connect to BSs. In this paper, we present a comprehensive survey of the research work of clustering schemes proposed for various scenarios in 5G networks and highlight various aspects of clustering schemes, including objectives, challenges, metrics, characteristics, performance measures. Furthermore, we present open issues of clustering in 5G.

Item Type:Articles
Additional Information:This work was part of the project entitled “A Novel Clustering Algorithm based on Reinforcement Learning for the Optimization of Global and Local Network Performances in Mobile Networks” funded by the Malaysian Ministry of Education under Fundamental Research Grant Scheme FRGS/1/2019/ICT03/SYUC/01/1, and the Partnership Grant CR-UMSSTDCIS-2018-01 and RK004-2017 between Sunway University and University of Malaya.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Imran, Professor Muhammad
Authors: Khan, M. F., Yau, K.-L. A., Noor, R. M., and Imran, M. A.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:Journal of Network and Computer Applications
Publisher:Elsevier
ISSN:1084-8045
ISSN (Online):1095-8592
Published Online:14 January 2020
Copyright Holders:Copyright © 2020 Elsevier Ltd.
First Published:First published in Journal of Network and Computer Applications 154: 102539
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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