Secure Authentication Mechanism for Cluster based Vehicular Adhoc Network (VANET): A Survey

TL;DR

This survey reviews existing authentication mechanisms in cluster-based VANETs, analyzing their strengths and weaknesses, and discusses AI/ML-based approaches and open challenges for secure vehicular communication.

Contribution

It provides a comprehensive analysis of current authentication schemes in cluster-based VANETs, including integration of AI/ML techniques and identification of research gaps.

Findings

Evaluation of various authentication schemes based on security and performance metrics

Identification of the potential of AI/ML techniques in enhancing VANET authentication

Highlighting open research challenges in secure vehicular communication

Abstract

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.