Clustering in vehicular ad hoc network: Algorithms and challenges

•A comprehensive classification of VANET clustering algorithms: strength and weakness.•Intelligence-based VANET clustering including machine learning and fuzzy logic.•The distinction between vehicle mobility as well as network mobility strategies.•A detail study of VANET multi-hop clustering strateg...

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Bibliographic Details
Published in:Computers & electrical engineering Vol. 88; p. 106851
Main Authors: Mukhtaruzzaman, Mohammad, Atiquzzaman, Mohammed
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.12.2020
Subjects:
Clustering
Fuzzy logic
Machine learning
Mobility
Multi-hop
NEMO
Review
Vanet
Fuzzy logic
Machine learning
Mobility
Multi-hop
Vanet
Review
Clustering
NEMO
ISSN:0045-7906, 1879-0755
Online Access:Get full text
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Summary:•A comprehensive classification of VANET clustering algorithms: strength and weakness.•Intelligence-based VANET clustering including machine learning and fuzzy logic.•The distinction between vehicle mobility as well as network mobility strategies.•A detail study of VANET multi-hop clustering strategies. Clustering is an important concept in vehicular ad hoc network (VANET) where several vehicles join to form a group based on common features. Mobility-based clustering strategies are the most common in VANET clustering; however, machine learning and fuzzy logic algorithms are also the basis of many VANET clustering algorithms. Some VANET clustering algorithms integrate machine learning and fuzzy logic algorithms to make the cluster more stable and efficient. Network mobility (NEMO) and multi-hop-based strategies are also used for VANET clustering. Mobility and some other clustering strategies are presented in the existing literature reviews; however, extensive study of intelligence-based, mobility-based, and multi-hop-based strategies still missing in the VANET clustering reviews. In this paper, we presented a classification of intelligence-based clustering algorithms, mobility-based algorithms, and multi-hop-based algorithms with an analysis on the mobility metrics, evaluation criteria, challenges, and future directions of machine learning, fuzzy logic, mobility, NEMO, and multi-hop clustering algorithms. [Display omitted]
ISSN:0045-7906
1879-0755
DOI:10.1016/j.compeleceng.2020.106851