论文标题
Vanet中的聚类：算法和挑战
Clustering in VANET: Algorithms and Challenges
论文作者
论文摘要
聚类是车辆临时网络（VANET）中的一个重要概念，其中几辆车辆与共同特征组成一个组。基于移动性的聚类策略在VANET聚类中最常见。但是，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，以使群集更加稳定和高效。网络移动性（NEMO）和基于多跳的策略也用于Vanet聚类。现有文献评论中提出了流动性和其他一些聚类策略；但是，在Vanet聚类评论中仍然缺少对基于情报，基于移动性和基于多跳的策略的广泛研究。在本文中，我们介绍了基于智能的集群算法，基于移动性的算法和基于多跳的算法的分类，并分析了机器学习标准，挑战，挑战和机器学习，模糊逻辑，逻辑，移动性，Nemo，Nemo，多互惠跳线聚集算法的机器学习，挑战和未来方向。
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.
