期刊
ACM TRANSACTIONS ON INTERNET TECHNOLOGY
卷 22, 期 4, 页码 -出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3433548
关键词
Internet of vehicles; traffic prediction; network security; deep learning
资金
- National Key R&D Program of China [2018YFE0206800]
- National Natural Science Foundation of China [61936001, 61971084, 62001073, 62025105, 62171378]
- National Natural Science Foundation of Chongqing [cstc2019jcyj-cxttX0002, cstc2021ycjh-bgzxm0013, cstc2021ycjh-bgzxm0039, cstc2021jcyj-msxmX0031]
- Support Program for Overseas Students to Return to China for Entrepreneurship and Innovation [cx2021003, cx2021053]
This article studies the problem of end-to-end network traffic prediction in the backbone networks of Internet of Vehicles (IoV), and proposes a deep learning-based method which considers the spatio-temporal feature and long-range dependence of network traffic. Furthermore, a threshold-based update mechanism is introduced to improve the real-time performance of the method.
Internet of Vehicles (IoV), as a special application of Internet of Things (IoT), has been widely used for Intelligent Transportation System (ITS), which leads to complex and heterogeneous IoV backbone networks. Network traffic prediction techniques are crucial for efficient and secure network management, such as routing algorithm, network planning, and anomaly and intrusion detection. This article studies the problem of end-to-end network traffic prediction in IoV backbone networks, and proposes a deep learning-based method. The constructed system considers the spatio-temporal feature of network traffic, and can capture the long-range dependence of network traffic. Furthermore, a threshold-based update mechanism is put forward to improve the real-time performance of the designed method by using Q-learning. The effectiveness of the proposed method is evaluated by a real network traffic dataset.
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