IoV Scenario: Implementation of a Bandwidth Aware Algorithm in Wireless Network Communication Mode

Zhang, Peiying; Wang, Chao; Aujla, Gagangeet Singh; Kumar, Neeraj; Guizani, Mohsen

Abstract

Wireless network communication has developed rapidly in recent years, especially in the field of Internet of vehicles (IoV). However, due to the limitations of traditional network architecture, resource scheduling in wireless network environment is still facing great challenges. We focus on the urgent need of users for bandwidth resources in the IoV scenario under virtual network environment. This paper proposes a bandwidth aware multi domain virtual network embedding (BA-VNE) algorithm. The algorithm is mainly aimed at the problem that users need a lot of bandwidth in wireless communication mode, and solves the problem of bandwidth resource allocation from the perspective of virtual network embedding (VNE). In order to improve the performance of the algorithm, we introduce particle swarm optimization (PSO) algorithm to optimize the performance of the algorithm. In order to verify the effectiveness of the algorithm, we have carried out simulation experiments from link bandwidth, mapping cost and virtual network request (VNR) acceptance rate. The final results show that the proposed algorithm is better than other representative algorithms in the above indicators.

Citation

Zhang, P., Wang, C., Aujla, G. S., Kumar, N., & Guizani, M. (2020). IoV Scenario: Implementation of a Bandwidth Aware Algorithm in Wireless Network Communication Mode. IEEE Transactions on Vehicular Technology, 69(12), https://doi.org/10.1109/tvt.2020.3035341

• Journal Article Type: Article
• Online Publication Date: Nov 3, 2020
• Publication Date: 2020-12
• Deposit Date: Apr 27, 2021
• Journal: IEEE Transactions on Vehicular Technology
• Print ISSN: 0018-9545
• Electronic ISSN: 1939-9359
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 69
• Issue: 12
• DOI: https://doi.org/10.1109/tvt.2020.3035341
• Public URL: https://durham-repository.worktribe.com/output/1243898