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

Multi-source multi-destination hybrid infrastructure-aided traffic aware routing in V2V/I networks
Multi-source multi-destination hybrid infrastructure-aided traffic aware routing in V2V/I networks
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 (HITAR)
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.
AODV, Delays, Q-learning, Quality of Service, Quality of service, Roads, Routing, Routing protocols, Traffic Aware Routing, V2V/I, Vehicular Networks, quality of service, traffic aware routing, Vehicular networks, q-learning
2169-3536
119956-119969
Ivanescu, Teodor
98894ec7-417b-4460-84aa-a15fec69ff09
Yetgin, Halil
a036d86f-32ed-4c2e-9f7e-341b77972417
Merrett, Geoff
89b3a696-41de-44c3-89aa-b0aa29f54020
El-Hajjar, Mohammed
3a829028-a427-4123-b885-2bab81a44b6f
Ivanescu, Teodor
98894ec7-417b-4460-84aa-a15fec69ff09
Yetgin, Halil
a036d86f-32ed-4c2e-9f7e-341b77972417
Merrett, Geoff
89b3a696-41de-44c3-89aa-b0aa29f54020
El-Hajjar, Mohammed
3a829028-a427-4123-b885-2bab81a44b6f

Ivanescu, Teodor, Yetgin, Halil, Merrett, Geoff and El-Hajjar, Mohammed (2022) Multi-source multi-destination hybrid infrastructure-aided traffic aware routing in V2V/I networks. IEEE Access, 10, 119956-119969. (doi:10.1109/ACCESS.2022.3221446).

Record type: Article

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 (HITAR)
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.

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Accepted/In Press date: 8 November 2022
Published date: 10 November 2022
Additional Information: Publisher Copyright: © 2013 IEEE.
Keywords: AODV, Delays, Q-learning, Quality of Service, Quality of service, Roads, Routing, Routing protocols, Traffic Aware Routing, V2V/I, Vehicular Networks, quality of service, traffic aware routing, Vehicular networks, q-learning

Identifiers

Local EPrints ID: 472560
URI: http://eprints.soton.ac.uk/id/eprint/472560
ISSN: 2169-3536
PURE UUID: b3ee1cf6-adb1-48bc-94b7-a71b951d48ad
ORCID for Teodor Ivanescu: ORCID iD orcid.org/0000-0003-1490-8573
ORCID for Geoff Merrett: ORCID iD orcid.org/0000-0003-4980-3894
ORCID for Mohammed El-Hajjar: ORCID iD orcid.org/0000-0002-7987-1401

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Date deposited: 08 Dec 2022 17:33
Last modified: 06 Jun 2024 01:50

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Contributors

Author: Teodor Ivanescu ORCID iD
Author: Halil Yetgin
Author: Geoff Merrett ORCID iD
Author: Mohammed El-Hajjar ORCID iD

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