Vehicular Ad Hoc Networks (VANETs) play a crucial role in Intelligent Transportation Systems (ITS) by facilitating communication between vehicles and infrastructure. This communication aims to enhance road safety, improve traffic efficiency, and enhance passenger comfort. The secure and reliable exchange of information is paramount to ensure the integrity and confidentiality of data, while the authentication of vehicles and messages is essential to prevent unauthorized access and malicious activities. This survey paper presents a comprehensive analysis of existing authentication mechanisms proposed for cluster-based VANETs. The strengths, weaknesses, and suitability of these mechanisms for various scenarios are carefully examined. Additionally, the integration of secure key management techniques is discussed to enhance the overall authentication process. Cluster-based VANETs are formed by dividing the network into smaller groups or clusters, with designated cluster heads comprising one or more vehicles. Furthermore, this paper identifies gaps in the existing literature through an exploration of previous surveys. Several schemes based on different methods are critically evaluated, considering factors such as throughput, detection rate, security, packet delivery ratio, and end-to-end delay. To provide optimal solutions for authentication in cluster-based VANETs, this paper highlights AI- and ML-based routing-based schemes. These approaches leverage artificial intelligence and machine learning techniques to enhance authentication within the cluster-based VANET network. Finally, this paper explores the open research challenges that exist in the realm of authentication for cluster-based Vehicular Adhoc Networks, shedding light on areas that require further investigation and development.

翻译：车载自组织网络（VANET）在智能交通系统（ITS）中扮演着关键角色，通过促进车辆与基础设施之间的通信，旨在提升道路安全、改善交通效率并增强乘客舒适度。为确保数据的完整性与机密性，信息的可靠安全交换至关重要；同时，车辆与消息的认证对于防止未经授权访问和恶意活动不可或缺。本综述论文对现有面向集群式VANET的认证机制进行了全面分析，细致考察了这些机制的优势、劣势及其在不同场景下的适用性。此外，本文还探讨了安全密钥管理技术的集成，以增强整体认证流程。集群式VANET通过将网络划分为若干较小的群组或簇形成，并指定由一个或多个车辆组成的簇头。进一步地，本文通过梳理先前综述，识别了现有文献中的空白。基于不同方法的多种方案被批判性评估，考量指标包括吞吐量、检测率、安全性、数据包投递率及端到端延迟。为提供集群式VANET认证的最优解决方案，本文重点介绍了基于人工智能（AI）与机器学习（ML）的路由方案。这些方法利用人工智能与机器学习技术，增强了集群式VANET网络内的认证能力。最后，本文探讨了集群式车载自组织网络认证领域中存在的开放式研究挑战，揭示了需要进一步探索与发展的方向。