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

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

Design Models for Trusted Communications in Vehicle-to-Everything (V2X) Networks

Intelligent transportation system is one of the main systems which has been developed to achieve safe traffic and efficient transportation. It enables the road entities to establish connections with other road entities and infrastructure units using Vehicle-to-Everything (V2X) communications. To improve the driving experience, various applications are implemented to allow for road entities to share the information among each other. Then, based on the received information, the road entity can make its own decision regarding road safety and guide the driver. However, when these packets are dropped for any reason, it could lead to inaccurate decisions due to lack of enough information. Therefore, the packets should be sent through a trusted communication. The trusted communication includes a trusted link and trusted road entity. Before sending packets, the road entity should assess the link quality and choose the trusted link to ensure the packet delivery. Also, evaluating the neighboring node behavior is essential to obtain trusted communications because some misbehavior nodes may drop the received packets. As a consequence, two main models are designed to achieve trusted V2X communications. First, a multi-metric Quality of Service (QoS)-balancing relay selection algorithm is proposed to elect the trusted link. Analytic Hierarchy Process (AHP) is applied to evaluate the link based on three metrics, which are channel capacity, link stability and end-to-end delay. Second, a recommendation-based trust model is designed for V2X communication to exclude misbehavior nodes. Based on a comparison between trust-based methods, weighted-sum is chosen in the proposed model. The proposed methods ensure trusted communications by reducing the Packet Dropping Rate (PDR) and increasing the end-to-end delivery packet ratio. In addition, the proposed trust model achieves a very low False Negative Rate (FNR) in comparison with an existing model

Survey and taxonomy of clustering algorithms in 5G

The large-scale deployment of fifth generation (5G) is expected to produce a massive amount of data with high variability due to ultra-densification and the rapid increase in a heterogeneous range of applications and services (e.g., virtual reality, augmented reality, and driver-less vehicles), and network devices (e.g., smart gadgets and sensors). Clustering organizes network topology by segregating nodes with similar interests or behaviors in a network into logical groups in order to achieve network-level and cluster-level enhancements, particularly cluster stability, load balancing, social awareness, fairness, and quality of service. Clustering has been investigated to support mobile user equipment (UE) in access networks, whereby UEs form clusters themselves and may connect to BSs. In this paper, we present a comprehensive survey of the research work of clustering schemes proposed for various scenarios in 5G networks and highlight various aspects of clustering schemes, including objectives, challenges, metrics, characteristics, performance measures. Furthermore, we present open issues of clustering in 5G

Clustering and 5G-enabled smart cities: a survey of clustering schemes in VANETs

This chapter highlights the importance of Vehicular Ad-hoc Networks (VANETs) in the context of the 5Genabled smarter cities and roads, a topic that attracts significant interest. In order for VANETs and its associated applications to become a reality, a very promising avenue is to bring together multiple wireless technologies in the architectural design. 5G is envisioned to have a heterogeneous network architecture. Clustering is employed in designing optimal VANET architectures that successfully use different technologies, therefore clustering has the potential to play an important role in the 5G-VANET enabled solutions. This chapter presents a survey of clustering approaches in the VANET research area. The survey provides a general classification of the clustering algorithms, presents some of the most advanced and latest algorithms in VANETs, and it is among the fewest works in the literature that reviews the performance assessment of clustering algorithms

New Gateway Selection Algorithm Based on Multi-Objective Integer Programming and Reinforcement Learning

Connecting vehicles to the infrastructure and benefiting from the services provided by the network is one of the main objectives to increase safety and provide well-being for passengers. Providing such services requires finding suitable gateways to connect the source vehicles to the infrastructure. The major feature of using gateways is to decrease the load of the network infrastructure resources so that each gateway is responsible for a group of vehicles. Unfortunately, the implementation of this goal is facing many challenges, including the highly dynamic topology of VANETs, which causes network instability, and the deployment of applications with high bandwidth demand that can cause network congestion, particularly in urban areas with a high-density vehicle. This work introduces a novel gateway selection algorithm for vehicular networks in urban areas, consisting of two phases. The first phase identifies the best gateways among the deployed vehicles using multi-objective integer programming. While in the second phase, reinforcement learning is employed to select a suitable gateway for any vehicular node in need to access the VANET infrastructure. The proposed model is evaluated and compared to other existing solutions. The obtained results show the efficiency of the proposed system in identifying and selecting the gateways

A Comparative Survey of VANET Clustering Techniques

© 2016 Crown. A vehicular ad hoc network (VANET) is a mobile ad hoc network in which network nodes are vehicles - most commonly road vehicles. VANETs present a unique range of challenges and opportunities for routing protocols due to the semi-organized nature of vehicular movements subject to the constraints of road geometry and rules, and the obstacles which limit physical connectivity in urban environments. In particular, the problems of routing protocol reliability and scalability across large urban VANETs are currently the subject of intense research. Clustering can be used to improve routing scalability and reliability in VANETs, as it results in the distributed formation of hierarchical network structures by grouping vehicles together based on correlated spatial distribution and relative velocity. In addition to the benefits to routing, these groups can serve as the foundation for accident or congestion detection, information dissemination and entertainment applications. This paper explores the design choices made in the development of clustering algorithms targeted at VANETs. It presents a taxonomy of the techniques applied to solve the problems of cluster head election, cluster affiliation, and cluster management, and identifies new directions and recent trends in the design of these algorithms. Additionally, methodologies for validating clustering performance are reviewed, and a key shortcoming - the lack of realistic vehicular channel modeling - is identified. The importance of a rigorous and standardized performance evaluation regime utilizing realistic vehicular channel models is demonstrated