Graph-enabled Intelligent Vehicular Network data processing

Zheng, Zhigao and Bashir, Ali Kashif ORCID: https://orcid.org/0000-0001-7595-2522 (2022) Graph-enabled Intelligent Vehicular Network data processing. IEEE Transactions on Intelligent Transportation Systems, 23 (5). pp. 4726-4735. ISSN 1524-9050

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Official URL: https://doi.org/10.1109/TITS.2022.3158045

Abstract

Intelligent vehicular network (IVN) is the underlying support for the connected vehicles and smart city, but there are several challenges for IVN data processing due to the dynamic structure of the vehicular network. Graph processing, as one of the essential machine learning and big data processing paradigm, which provide a set of big data processing scheme, is well-designed to processing the connected data. In this paper, we discussed the research challenges of IVN data processing and motivated us to address these challenges by using graph processing technologies. We explored the characteristics of the widely used graph algorithms and graph processing frameworks on GPU. Furthermore, we proposed several graph-based optimization technologies for IVN data processing. The experimental results show the graph processing technologies on GPU can archive excellent performance on IVN data.

Item Type:	Article
Peer-reviewed:	Yes
Date Deposited:	03 Jan 2023 13:49
Publisher:	Institute of Electrical and Electronics Engineers
Additional Information:	© 2022 IEEE. 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.
Divisions:	Faculties > Science and Engineering Research Centres > Centre for Advanced Computational Science
Subject terms:	Science & Technology, Technology, Engineering, Civil, Engineering, Electrical & Electronic, Transportation Science & Technology, Engineering, Transportation, Data processing, Quality of service, Vehicle dynamics, Sensors, Path planning, Job shop scheduling, Industries, Graph processing, vehicular networks, internet of intelligent vehicles, POWER-LAW DISTRIBUTIONS, TRANSPORTATION, 0801 Artificial Intelligence and Image Processing, 0905 Civil Engineering, 1507 Transportation and Freight Services, Logistics & Transportation
URI:	https://e-space.mmu.ac.uk/id/eprint/631074
DOI:	https://doi.org/10.1109/TITS.2022.3158045
ISSN	1524-9050
e-ISSN	1558-0016

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