  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.
