The controller selection problem (CSP) in software-defined vehicular networks (SDVNs) causes a long delay and large overhead due to sporadic links, especially when vehicles are outside the coverage of the main controller (MC) or a local controller (LC) and when distribution of the load among LCs is unequal. We solve the CSP using a support vector machine (SVM) algorithm for heterogeneous communications. The MC runs the algorithm based on vehicle density to select an optimal controller (OC) in both highway and city scenarios. After this selection, the OC is responsible for selecting a stable path to deliver packets from source to destination. This machine-learning-based SDVN scheme selects the minimum link duration ( $LD$ ) for communication between nodes, but selects the path with the maximum path time between source and destination. The protocol has two phases: OC selection and path selection. The OC is also responsible for distributing the load equally to the other LCs. The two-phase selection scheme improves the network performance in terms of delivery ratio (maximum value of 94.2%), end-to-end delay (minimum value of 0.11 ms), and routing overhead ratio (maximum incurred is 10%), which is proved by our simulation results in comparison to an existing scheme.

To meet the growing needs of automobile users, and to provide services on demand with stable and efficient paths across different bands amidst this proliferation of users, an integrated approach to the software-defined vehicular network (SDVN) is proposed in this paper. Due to the significant increase in users, DSRC is already considered insufficient to fulfill modern needs. Hence, to enhance network performance and fulfill the growing needs of users in SDVN environments, we implement cognitive technology by integrating the DSRC, mmWave, and THz bands to find stable paths among different nodes. To manage these different technologies, an SDN controller is employed as the main controller (MC), recording the global state of all nodes within the network. Channel sensing is conducted individually for each technology, and sensing results—representing the number of available bands for secondary communications—are updated periodically in the MC. Consequently, the MC manages connections by switching between DSRC, mmWave, and THz bands, providing stable paths between the source and destination. The switching decision is taken by considering both the distance from the MC and the availability of channels among these three technologies. This cognitive integration of bands in SDVN improves performance in terms of network delay, packet delivery, and overhead ratio.

Road sparsity is a major cause of routing instability, leading to intermittent connectivity and increased packet loss. To enhance stability in 6G software-defined vehicular networks (SDVNs), we integrate unmanned aerial vehicles (UAVs) into a dynamic vehicular environment. We propose a novel ground-to-air communication protocol that integrates Dedicated Short Range Communication (DSRC), millimeter-wave (mmWave), and terahertz (THz) bands to address path stability, load balancing among controllers, and technology switching to accommodate diverse vehicular demands. Roadside Units (RSUs) and UAVs act as local controllers (LCs), with RSUs communicating via LTE and UAVs via DSRC. The optimal controller (OC) is selected using supervised learning algorithms, Support Vector Machine (SVM), and Random Forest (RF), to ensure balanced load distribution and to manage stable routing across hybrid wireless technologies. The proposed scheme evaluates multiple UAV roles, including LC, relay, and destination, under different communication interfaces. Simulation results in a highway scenario demonstrate that the hybrid approach outperforms the reference DSRC, mmWave, and THz schemes in terms of packet delivery ratio, end-to-end delay, and routing overhead. Specifically, SVM-based OC selection achieved a maximum packet delivery ratio of 97.94%, with an end-to-end delay as low as 0.089 ms and minimal routing overhead, validating its effectiveness in mitigating road sparsity, improving connectivity, and enhancing network performance.