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Graph-Enabled Intelligent Vehicular Network Data Processing
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DOI：10.1109/TITS.2022.3158045.png)
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Abstract
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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.
Keywords:
Data processing
Quality of service
Vehicle dynamics
Sensors
Path planning
Job shop scheduling
Industries
Graph processing
vehicular networks
internet of intelligent vehicles
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