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Clustering in vehicular ad hoc network: Algorithms and challenges
Computers & Electrical Engineering ( IF 4.3 ) Pub Date : 2020-12-01 , DOI: 10.1016/j.compeleceng.2020.106851
Mohammad Mukhtaruzzaman , Mohammed Atiquzzaman

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.

中文翻译:

车载自组织网络中的聚类:算法和挑战

摘要 聚类是车辆自组织网络(VANET)中的一个重要概念,在该网络中,多辆车辆基于共同特征组成一个组。基于移动性的集群策略是 VANET 集群中最常见的;然而,机器学习和模糊逻辑算法也是许多 VANET 聚类算法的基础。一些VANET聚类算法集成了机器学习和模糊逻辑算法,使聚类更加稳定和高效。网络移动性 (NEMO) 和基于多跳的策略也用于 VANET 集群。现有文献综述中介绍了移动性和其他一些聚类策略;然而,VANET 聚类评论中仍然缺少对基于智能、基于移动性和基于多跳的策略的广泛研究。在本文中,
更新日期:2020-12-01
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