Skip to main content
DORAS
DCU Online Research Access Service
Login (DCU Staff Only)
Clustering and 5G-enabled smart cities: a survey of clustering schemes in VANETs

Tal, Irina and Muntean, Gabriel-Miro ORCID: 0000-0003-2958-7979 (2021) Clustering and 5G-enabled smart cities: a survey of clustering schemes in VANETs. In: Research Anthology on Developing and Optimizing 5G Networks and the Impact on Society. IGI Global, USA, pp. 1012-1050. ISBN 9781799877080

Full text available as:

[img]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
430kB

Abstract

This chapter highlights the importance of Vehicular Ad-hoc Networks (VANETs) in the context of the 5Genabled smarter cities and roads, a topic that attracts significant interest. In order for VANETs and its associated applications to become a reality, a very promising avenue is to bring together multiple wireless technologies in the architectural design. 5G is envisioned to have a heterogeneous network architecture. Clustering is employed in designing optimal VANET architectures that successfully use different technologies, therefore clustering has the potential to play an important role in the 5G-VANET enabled solutions. This chapter presents a survey of clustering approaches in the VANET research area. The survey provides a general classification of the clustering algorithms, presents some of the most advanced and latest algorithms in VANETs, and it is among the fewest works in the literature that reviews the performance assessment of clustering algorithms.

Item Type:Book Section
Refereed:Yes
Subjects:Computer Science > Algorithms
Engineering > Telecommunication
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Publisher:IGI Global
Official URL:https://dx.doi.org/10.4018/978-1-7998-7708-0.ch042
Copyright Information:© 2021 IGI
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:27077
Deposited On:27 Apr 2022 10:25 by Gabriel Muntean . Last Modified 27 Apr 2022 10:25

Downloads

Downloads per month over past year

Archive Staff Only: edit this record

  • Student Email
  • Staff Email
  • Student Apps
  • Staff Apps
  • Loop
  • Disclaimer
  • Privacy
  • Contact Us