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Two-hop connectivity to the roadside in a VANET under the random connection model
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Kartun-Giles, Alexander P., Koufos, Konstantinos, Lu, Xiao and Niyato, Dusit (2023) Two-hop connectivity to the roadside in a VANET under the random connection model. IEEE Transactions on Vehicular Technology, 72 (4). pp. 5508-5512. doi:10.1109/TVT.2022.3225517 ISSN 0018-9545.
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WRAP-Two-hop-connectivity-roadside-VANET-22.pdf - Accepted Version - Requires a PDF viewer. Download (932Kb) | Preview |
Official URL: https://doi.org/10.1109/TVT.2022.3225517
Abstract
In this paper, we compute the expected number of vehicles with at least one two-hop path to a fixed roadside unit (RSU) in a multi-hop, one-dimensional vehicular ad hoc network (VANET) where other cars can act as relays. The pairwise channels experience Rayleigh fading in the random connection model, and so exist, with probability function of the mutual distance between the cars, or between the cars and the RSU. We derive exact equivalents for the expected number of cars with a two-hop connection to the RSU when the car density ρ tends to zero and infinity, and determine its behaviour using an infinite oscillating power series in ρ, which is accurate for all regimes. We also corroborate those findings to a realistic situation, using snapshots of actual traffic data. Finally, a normal approximation is discussed for the probability mass function of the number of cars with a two-hop connection to the RSU.
| Item Type: | Journal Article | ||||||||
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| Subjects: | T Technology > TE Highway engineering. Roads and pavements | ||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||
| Library of Congress Subject Headings (LCSH): | Vehicular ad hoc networks (Computer networks), End-to-End Negotiation Protocol (Computer network protocol) | ||||||||
| Journal or Publication Title: | IEEE Transactions on Vehicular Technology | ||||||||
| Publisher: | IEEE | ||||||||
| ISSN: | 0018-9545 | ||||||||
| Official Date: | April 2023 | ||||||||
| Dates: |
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| Volume: | 72 | ||||||||
| Number: | 4 | ||||||||
| Page Range: | pp. 5508-5512 | ||||||||
| DOI: | 10.1109/TVT.2022.3225517 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Reuse Statement (publisher, data, author rights): | © 2022 Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
| Description: | This research is supported in part by the National Research Foundation (NRF), Singapore and Infocomm Media Development Authority under the Future Communications Research Development Programme (FCP), and DSO National Laboratories under the AI Singapore Programme (AISG Award No: AISG2-RP-2020-019). |
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| Date of first compliant deposit: | 24 November 2022 | ||||||||
| Date of first compliant Open Access: | 24 November 2022 | ||||||||
| RIOXX Funder/Project Grant: |
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