Multi-source multi-destination hybrid infrastructure-aided traffic aware routing in V2V/I networks

Article


Ivanescu, T., Yetgin, H., Merrett, G.V. and El-Hajjar, M. 2022. Multi-source multi-destination hybrid infrastructure-aided traffic aware routing in V2V/I networks. IEEE Access. 10, pp. 119956-119969. https://doi.org/10.1109/access.2022.3221446
TypeArticle
TitleMulti-source multi-destination hybrid infrastructure-aided traffic aware routing in V2V/I networks
AuthorsIvanescu, T., Yetgin, H., Merrett, G.V. and El-Hajjar, M.
Abstract

The concept of the “connected car” offers the potential for safer, more enjoyable and more efficient driving and eventually autonomous driving. However, in urban Vehicular Networks (VNs), the high mobility of vehicles along roads poses major challenges to the routing protocols needed for a reliable and flexible vehicular communications system. Thus, urban VNs rely on static Road-Side-Units (RSUs) to forward data and to extend coverage across the network. In this paper, we first propose a new Q-learning-based routing algorithm, namely Infrastructure-aided Traffic-Aware Routing (I-TAR), which leverages the static wired RSU infrastructure for packet forwarding. Then, we focus on the multi-source, multi-destination problem and the effect this imposes on node availability, as nodes also participate in other communications paths. This motivates our new hybrid approach, namely Hybrid Infrastructure-aided Traffic Aware Routing (HI-TAR) that aims to select the best Vehicle-to-Vehicle/Infrastructure (V2V/I) route. Our findings demonstrate that I-TAR can achieve up to 19% higher average packet-delivery-ratio (APDR) compared to the state-of-the-art. Under a more realistic scenario, where node availability is considered, a decline of up to 51% in APDR performance is observed, whereas the proposed HI-TAR in turn can increase the APDR performance by up to 50% compared to both I-TAR and the state-of-the-art. Finally, when multiple source-destination vehicle pairs are considered, all the schemes that model and consider node availability, i.e. limited-availability, achieve from 72.2% to 82.3% lower APDR, when compared to those that do not, i.e. assuming full-availability. However, HI-TAR still provides 34.6% better APDR performance than I-TAR, and ~40% more than the state-of-the-art.

KeywordsVehicular networks; V2V/I; q-learning; traffic aware routing; quality of service
Sustainable Development Goals11 Sustainable cities and communities
Middlesex University ThemeSustainability
PublisherIEEE
JournalIEEE Access
ISSN
Electronic2169-3536
Publication dates
Online10 Nov 2022
Print17 Nov 2022
Publication process dates
Submitted06 Oct 2022
Accepted06 Nov 2022
Deposited05 Apr 2024
Output statusPublished
Publisher's version
License
File Access Level
Open
Copyright Statement

This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/

Digital Object Identifier (DOI)https://doi.org/10.1109/access.2022.3221446
Web of Science identifierWOS:000888953500001
LanguageEnglish
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