| Autor: |
Rafid, Muhammad, Ghafoor, Huma, Koo, Insoo |
| Zdroj: |
IEEE Transactions on Intelligent Transportation Systems; 2024, Vol. 25 Issue: 5 p3828-3842, 15p |
| Abstrakt: |
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. |
| Databáze: |
Supplemental Index |
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