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Reliable and Scalable Routing Under Hybrid SDVN Architecture: A Graph Learning Based Method

Li, Zhuhui; Zhao, Liang; Min, Geyong; Al-Dubai, Ahmed Y.; Hawbani, Ammar; Zomaya, Albert Y.; Luo, Chunbo

Authors

Zhuhui Li

Liang Zhao

Geyong Min

Ammar Hawbani

Albert Y. Zomaya

Chunbo Luo



Abstract

Greedy routing efficiently achieves routing solutions for vehicular networks due to its simplicity and reliability. However, the existing greedy routing schemes have mainly considered simple routing metrics only, e.g., distance based on the local view of an individual vehicle. This consideration is insufficient for analysing dynamic and complicated vehicular communication scenarios. This shortcoming inevitably degrades the overall routing performance. Software-Defined Vehicular Network (SDVN) and Graph Convolutional Network (GCN) could break these limitations. Thus, this paper presents a novel GCN-based greedy routing algorithm (NGGRA) in the hybrid SDVN. The SDVN control plane trains the GCN decision model based on the globally collected data. The vehicle with transmission requirements can adopt this model for inferring and making the routing decision. The new proposed nodeimportance-based graph convolutional network (NiGCN) model analyses multiple metrics in the dynamic vehicular network scenario. Meanwhile, SDVN architecture offers a global view for model training. Extensive simulation results demonstrate that NiGCN outperforms most popular GCN models in training efficiency and accuracy. In addition, NGGRA can improve the packet delivery ratio and latency substantially compared with its counterparts.

Journal Article Type Article
Acceptance Date Jul 27, 2023
Online Publication Date Aug 9, 2023
Publication Date 2023-12
Deposit Date Jul 28, 2023
Publicly Available Date Aug 9, 2023
Print ISSN 1524-9050
Electronic ISSN 1558-0016
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 24
Issue 12
Pages 14022 - 14036
DOI https://doi.org/10.1109/tits.2023.3300082
Keywords Software-defined vehicular networks, vehicular ad-hoc networks, routing, deep learning, graph convolutional networks
Public URL http://researchrepository.napier.ac.uk/Output/3155788

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Reliable And Scalable Routing Under Hybrid SDVN Architecture: A Graph Learning Based Method (accepted manuscript) (1.1 Mb)
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