Novel Online Sequential Learning-Based Adaptive Routing for Edge Software-Defined Vehicular Networks

Zhao, Liang; Zhao, Weiliang; Hawbani, Ammar; Al-Dubai, Ahmed Y.; Min, Geyong; Zomaya, Albert; Gong, Changqing

Abstract

To provide efficient networking services at the edge of Internet-of-Vehicles (IoV), Software-Defined Vehicular Network (SDVN) has been a promising technology to enable intelligent data exchange without giving additional duties to the resource constrained vehicles. Compared with conventional centralized SDVNs, hybrid SDVNs combine the centralized control of SDVNs and self-organized distributed routing of Vehicular Ad-hoc NETworks (VANETs) to mitigate the burden on the central controller caused by the frequent uplink and downlink transmissions. Although a wide variety of routing protocols have been developed, existing protocols are designed for specific scenarios without considering flexibility and adaptivity in dynamic vehicular networks. To address this problem, we propose an efficient online sequential learning-based adaptive routing scheme, namely, Penicillium reproduction-based Online Learning Adaptive Routing scheme (POLAR) for hybrid SDVNs. By utilizing the computational power of edge servers, this scheme can dynamically select a routing strategy for a specific traffic scenario by learning the pattern from network traffic. Firstly, this paper applies Geohash to divide the large geographical area into multiple grids, which facilitates the collection and processing of real-time traffic data for regional management in controller.

• Journal Article Type: Article
• Acceptance Date: Dec 12, 2020
• Online Publication Date: Dec 31, 2020
• Publication Date: 2021-05
• Deposit Date: Dec 18, 2020
• Publicly Available Date: Dec 18, 2020
• Journal: IEEE Transactions on Wireless Communications
• Print ISSN: 1536-1276
• Electronic ISSN: 1558-2248
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 20
• Issue: 5
• Pages: 2991-3004
• DOI: https://doi.org/10.1109/twc.2020.3046275
• Keywords: Routing, Particle swarm optimization, Optimization, Wireless communication, Adaptive systems, Decision making, Routing protocols
• Public URL: http://researchrepository.napier.ac.uk/Output/2711391