The University of Southampton
University of Southampton Institutional Repository

Modularity-based dynamic clustering for energy efficient UAVs aided communications

Modularity-based dynamic clustering for energy efficient UAVs aided communications
Modularity-based dynamic clustering for energy efficient UAVs aided communications

In this letter, we propose a novel modularity-based dynamic clustering relying on modified Louvain method for UAVs aided mobile communications. Our aim is to save the transmit power of the mobile devices, by locating the UAVs vertically projected on the centroids of the user clusters. We further propose two types of operation for the modularity-based dynamic clustering, namely the recurring operation and the differential operation. We show that the proposed method requires substantially lower transmit power of the mobile devises and lower energy consumption of the UAVs than that required by the K-means based solution. We also show that the differential operation is more suitable for networks with lower proportion of moving users, since it consumes significantly less energy than that required by the recurring operation at the cost of requiring slightly higher transmit power of mobile devices.

Clustering algorithms, Complexity theory, Mobile communication, Mobile handsets, Symmetric matrices, Unmanned aerial vehicles, Vehicle dynamics
2162-2337
1-5
Yu, Jiadong
bb359051-0c31-4164-8371-922d7b1e0f72
Zhang, Rong
e54ae375-3495-4e40-aff1-4b8635ee2ede
Gao, Yue
4ecd3333-926c-47a5-943a-dc57716b8f25
Yang, Lie Liang
ae425648-d9a3-4b7d-8abd-b3cfea375bc7
Yu, Jiadong
bb359051-0c31-4164-8371-922d7b1e0f72
Zhang, Rong
e54ae375-3495-4e40-aff1-4b8635ee2ede
Gao, Yue
4ecd3333-926c-47a5-943a-dc57716b8f25
Yang, Lie Liang
ae425648-d9a3-4b7d-8abd-b3cfea375bc7

Yu, Jiadong, Zhang, Rong, Gao, Yue and Yang, Lie Liang (2018) Modularity-based dynamic clustering for energy efficient UAVs aided communications. IEEE Wireless Communications Letters, 1-5. (doi:10.1109/LWC.2018.2816649).

Record type: Article

Abstract

In this letter, we propose a novel modularity-based dynamic clustering relying on modified Louvain method for UAVs aided mobile communications. Our aim is to save the transmit power of the mobile devices, by locating the UAVs vertically projected on the centroids of the user clusters. We further propose two types of operation for the modularity-based dynamic clustering, namely the recurring operation and the differential operation. We show that the proposed method requires substantially lower transmit power of the mobile devises and lower energy consumption of the UAVs than that required by the K-means based solution. We also show that the differential operation is more suitable for networks with lower proportion of moving users, since it consumes significantly less energy than that required by the recurring operation at the cost of requiring slightly higher transmit power of mobile devices.

Text
Modularity-based Dynamic Clustering for Energy Efficient UAVs aided Communications - Accepted Manuscript
Available under License Creative Commons Attribution.
Download (685kB)

More information

Accepted/In Press date: 15 March 2018
e-pub ahead of print date: 16 March 2018
Additional Information: AM added 24/4/18
Keywords: Clustering algorithms, Complexity theory, Mobile communication, Mobile handsets, Symmetric matrices, Unmanned aerial vehicles, Vehicle dynamics

Identifiers

Local EPrints ID: 419961
URI: http://eprints.soton.ac.uk/id/eprint/419961
ISSN: 2162-2337
PURE UUID: d4ee28e3-96bd-464b-b6ce-aec3882e9c12
ORCID for Lie Liang Yang: ORCID iD orcid.org/0000-0002-2032-9327

Catalogue record

Date deposited: 24 Apr 2018 16:30
Last modified: 16 Mar 2024 03:00

Export record

Altmetrics

Contributors

Author: Jiadong Yu
Author: Rong Zhang
Author: Yue Gao
Author: Lie Liang Yang ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×