Vehicular Ad-hoc Networks (VANET) offer several user applications for passengers and drivers, as well as security and internet access applications. To ensure efficient data transmission between vehicles, a reliable routing protocol is considered a significant challenge. This paper suggests a new clustering-based routing protocol combining a modified K-Means algorithm with Continuous Hopfield Network and Maximum Stable Set Problem (KMRP) for VANET. In this way, the basic input parameters of the K-Means algorithm, such as the number of clusters and the initial cluster heads, will not be selected randomly, but using Maximum Stable Set Problem and Continuous Hopfield Network. Then the assignment of vehicles to clusters will be carried out according to Link Reliability Model as a metric that replaces the distance parameter in the K-Means algorithm. Finally, the cluster head is selected by weight function according to the amount of free buffer space, the speed, and the node degree. The simulation results have proved that the designed protocol performs better in a highway vehicular environment, compared to the most recent schemes designed for the same objective. In fact, KMRP reduces traffic congestion, and thus provides a significant increase in Throughput. In addition, KMRP decreases the transmission delay and guarantees the stability of the clusters in high density and mobility, which acts better in terms of the Packet Delivery Ratio.

Today, Vehicular Ad-hoc Networks (VANET) have become an interesting research topic for developing Intelligent Transport Systems. In urban environments, vehicles move continuously and at different speeds, which leads to frequent changes in the network topology. The main issue faced in an urban scenario is the performance of routing protocols when delivering data from one vehicle to another. This paper introduces ECRDP, an Efficient Clustering Routing approach using a new clustering algorithm based on Density Peaks Clustering (DPC) and Particle Swarm Optimization (PSO). First, the PSO algorithm is applied to determine the cluster heads, or a new fitness function for finding the best solutions is formulated using the DPC algorithm. Next, clustering is performed based on the reliability of links parameter between vehicles. Then, a maintenance phase is proposed to update the cluster heads and redistribute the vehicles in the clusters. Finally, the effectiveness of the suggested scheme is evaluated by a simulation operated by MATLAB on a real urban scenario. The results achieved show an overall increase in stability, proven by a reduction in change rate by 74%, and an improvement in performance indicated by an increase in intra-cluster throughput by 34% and inter-cluster by 47%, as well as an overall reduction of average delay by 16%.

The convergence of Vehicular Ad Hoc Networks (VANETs) and the Internet of Things (IoT) is giving rise to the Internet of Vehicles (IoV), a key enabler of next-generation intelligent transportation systems. This survey provides a comprehensive analysis of the architectural, communication, and computing foundations that support VANET–IoT integration. We examine the roles of cloud, edge, and in-vehicle computing, and compare major V2X and IoT communication technologies, including DSRC, C-V2X, MQTT, and CoAP. The survey highlights how sensing, communication, and distributed intelligence interact to support applications such as collision avoidance, cooperative perception, and smart traffic management. We identify four central challenges—security, scalability, interoperability, and energy constraints—and discuss how these issues shape system design across the network stack. In addition, we review emerging directions including 6G-enabled joint communication and sensing, reconfigurable surfaces, digital twins, and quantum-assisted optimization. The survey concludes by outlining open research questions and providing guidance for the development of reliable, efficient, and secure VANET–IoT systems capable of supporting future transportation networks.

In this paper, we propose a routing protocol using an adaptive K-Harmonic Means (RPKHM) in order to improve the lifetime of links between vehicles and to increase the stability of the vehicular network. First, the number and initial positions of the centroids are determined using a mathematical model that takes into account the total number of vehicles and the network topology. Whereas, the clustering is done using a similarity value based on the Euclidean distance, the difference in speed and direction of the vehicles. Finally, the maintenance of the clusters and the selection of new cluster heads are based on a cost function, which takes into account the total size of the free buffer and expected transmission count (ETX). The simulation results show the efficiency of the proposed scheme in terms of Packet Delivery Ratio, average End-to-End Delay, and Throughput.

Considerable attention has recently been given to the routing issue in vehicular ad-hoc networks (VANET). Indeed, the repetitive communication failures and high velocity of vehicles reduce the efficacy of routing protocols in VANET. The clustering technique is considered an important solution to overcome these difficulties. In this paper, an efficient clustering approach using an adapted k-means algorithm for VANET has been introduced to enhance network stability in a highway environment. Our approach relies on a clustering scheme that accounts for the network characteristics and the number of connected vehicles. The simulation indicates that the proposed approach is more efficient than similar schemes. The results obtained appear an overall increase in constancy, proven by an increase in cluster head lifetime by 66%, and an improvement in robustness clear in the overall reduction of the end-to-end delay by 46% as well as an increase in throughput by 74%.