Name
Abstract | ||
|---|---|---|
An extension of Software-Defined Networking (SDN) to the vehicular networks can improve the network performance effectively. However, with the explosive growth of vehicles, the architecture of Software-Defined Vehicular Networks (SDVNs) cannot well satisfy the stringent end-to-end latency requirement of vehicles. To cope with the problem, The 5G has been regarded as a promising technology to be integrated into SDVNs. However, there are still some technical challenges in 5G, such as coverage issues, which limit the development of vehicular networks. Beyond 5G (B5G) technology is envisioned as a promising solution to expand the capabilities of vehicular networks in terms of safety, reliability, etc. In this paper, we introduce B5G technology to improve the SDVNs to implement reliable routing. Firstly, we propose a new architecture of SDVNs based on B5G technology, and creat a protocol stack which integrates the 5G protocol into the WAVE protocol. In addition, we design the adaptive link performance monitoring mechanism which balances the control overhead and the accuracy. Finally, we improve the Genetic Algorithm (GA) through cross-generation selection and small-environment strategy and design an reliable QoS routing algorithm. The simulation results demonstrate that the monitoring mechanism can measure the performance of the link more accurately, and the improved GA is superior to standard GA and Dijkstra. In particular, compared with the standard GA, the improved GA can improve bandwidth utilization, delay, and packet loss rate by 34.3%, 30.6%, and 20.9%, respectively. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s12083-021-01231-1 | PEER-TO-PEER NETWORKING AND APPLICATIONS |
Keywords | DocType | Volume |
Software-defined networking (SDN), software-defined vehicular networks (SDVNs), B5G technology, reliable QoS routing, genetic algorithm (GA) | Journal | 15 |
Issue | ISSN | Citations |
1 | 1936-6442 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yaoguang Lu | 1 | 1 | 1.36 |
| Xingwei Wang | 2 | 0 | 0.34 |
| Bo Yi | 3 | 0 | 0.34 |
| Min Huang | 4 | 0 | 0.34 |