Recurrent Neural Network Based Collaborative Filtering for QoS Prediction in IoV
As the emerging paradigm that is believed to be conducive to the development of intelligent transportation systems (ITS), Internet of Vehicles (IoV) is constructed with a number of connected heterogeneous vehicle devices which provide a variety of services. As the number of vehicle devices in IoV is growing fast, selecting the appropriate service from candidate services which are functionally equivalent is becoming an imperative task. Predicting the non-functional attribute of service invocation, namely quality of service (QoS), to ensure the optimal service selection is the mainstream direction. Considering that most of the conventional prediction methods neglect the fact that QoS values change dynamically with some objective factors, this paper proposes a recurrent neural network based collaborative filtering method called RNCF for QoS prediction. Specifically, a multi-layer GRU structure is incorporated in the framework of neural collaborative filtering to model the dynamic state of physical environments or network conditions and share the invocation records across different time slices. The authors conduct extensive experiments on the WSDream dataset to demonstrate the effectiveness of the proposed QoS prediction model RNCF.
- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
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Supplemental Notes:
- Copyright © 2022, IEEE.
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Authors:
- Liang, Tingting
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0000-0003-1607-5771
- Chen, Manman
- Yin, Yuyu
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0000-0001-7565-4111
- Zhou, Li
- Ying, H
- Ying, Haochao
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0000-0001-7832-2518
- Publication Date: 2022-3
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References;
- Pagination: pp 2400-2410
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Serial:
- IEEE Transactions on Intelligent Transportation Systems
- Volume: 23
- Issue Number: 3
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 1524-9050
- Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979
Subject/Index Terms
- TRT Terms: Cooperation; Neural networks; Predictive models; Quality of service
- Identifier Terms: Internet of Moving Things
- Subject Areas: Data and Information Technology; Operations and Traffic Management; Transportation (General);
Filing Info
- Accession Number: 01845906
- Record Type: Publication
- Files: TRIS
- Created Date: May 20 2022 9:32AM