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.

The purpose of this paper is to suggest an approach for increasing the convergence speed of Halley’s method to solve a non-linear equation. This approach is based on the second order Taylor polynomial and on Halley’s formula. By applying it a certain number of times, we obtain a new family of methods. The originality of this family is manifested in the fact that all its sequences are generated from one exceptional formula that depends on a natural integer parameter $p$. In addition, under certain conditions, the convergence speed of its sequences increases with $p$. The convergence analysis shows that the order of convergence of all proposed methods is three. A study on their global convergence is carried out. To illustrate the performance of this family, several numerical comparisons are made with other third and higher order methods.

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.

The Internet of Vehicles (IoV) enables intelligent transportation services through real-time communication between vehicles and infrastructure. However, high mobility and frequent topology changes often reduce cluster stability and communication reliability. Traditional clustering approaches commonly rely on static decision criteria and periodic maintenance mechanisms, leading to frequent reclustering operations and increased communication overhead. This paper proposes an Adaptive Mobility- and Connectivity-Aware Clustering (A-MCAC) framework for IoV environments. The proposed approach combines predictive mobility analysis, link lifetime estimation, adaptive weight optimization, and event-driven cluster maintenance to improve Cluster Head (CH) selection and cluster stability. By dynamically adapting clustering decisions according to network conditions, A-MCAC aims to reduce reclustering operations and enhance communication efficiency. The proposed framework is evaluated through simulations and compared with CARAC, MetaLearn, and CPB using cluster lifetime, CH lifetime, reclustering rate, link stability, clustering overhead, packet delivery ratio, end-to-end delay, and throughput. Results demonstrate that A-MCAC improves cluster stability, increases communication reliability, maintains a favorable overhead-performance trade-off, and achieves better network performance across varying vehicle densities. These findings demonstrate that A-MCAC improves both clustering stability and communication efficiency in highly dynamic IoV environments.