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arXiv 2009.01964cs.NI
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VANET中的聚类：算法与挑战

Clustering in VANET: Algorithms and Challenges

Mohammad Mukhtaruzzaman, Mohammed Atiquzzaman

发表信息：
Computers & Electrical Engineering, Volume 88, December 2020, 106851
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摘要：

在车辆自组织网络中，多个车辆根据共同的特征组成一个组，聚类是一个重要概念。基于移动性的分簇策略是VANET分簇中最常见的策略，然而，机器学习和模糊逻辑算法也是许多VANET分簇算法的基础。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聚类。移动性和其他一些聚类策略在现有的文献综述，然而，广泛的研究基于智能，移动性为基础的，和多跳为基础的策略仍然缺乏在VANET聚类评论。在本文中，我们提出了基于智能的聚类算法，基于移动性的算法，和基于多跳的算法的分类与分析的移动性指标，评价标准，挑战，和未来的发展方向的机器学习，模糊逻辑，移动性，NEMO和多跳聚类算法。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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