Clustering in VANET: Algorithms and Challenges

TL;DR

This paper reviews various clustering algorithms in VANET, focusing on mobility, machine learning, and multi-hop strategies, analyzing their metrics, challenges, and future directions to enhance network stability and efficiency.

Contribution

It provides a comprehensive classification and analysis of intelligence-based, mobility-based, and multi-hop clustering algorithms in VANETs, highlighting gaps in existing literature.

Findings

Mobility and multi-hop strategies are crucial for VANET clustering.

Machine learning and fuzzy logic improve cluster stability and efficiency.

The paper identifies challenges and future research directions in VANET clustering.

Abstract

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.