QoS-Aware Reliable Traffic Prediction Model under Wireless Vehicular Networks
Author | Zhang, Qin |
Author | Guo, Zhiwei |
Author | Yu, Keping |
Author | Al-Dulaimi, Anwer |
Author | Wei, Wei |
Author | Guizani, Mohsen |
Available date | 2022-11-09T20:49:19Z |
Publication Date | 2021-01-01 |
Publication Name | 2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings |
Identifier | http://dx.doi.org/10.1109/GLOBECOM46510.2021.9685115 |
Citation | Zhang, Q., Guo, Z., Yu, K., Al-Dulaimi, A., Wei, W., & Guizani, M. (2021, December). QoS-Aware Reliable Traffic Prediction Model Under Wireless Vehicular Networks. In 2021 IEEE Global Communications Conference (GLOBECOM) (pp. 1-6). IEEE. |
ISBN | 9781728181042 |
Abstract | With the continuous progress of communication quality, the wireless vehicular networks (WVN) will surely be-come an inevitable part of future smart cities. Inside WVN where context is complicated and stochastic, quality of service (QoS) acts as the core concern for broad users. And reliable prediction towards traffic in WVN is essentially an important demand to ensure QoS. Conventionally, related methods mainly focus one side to establish robust prediction models, possessing some limitations. To bridge such gap, model integration may be an intuitive and promising solution. This paper proposes QoS-aware reliable traffic prediction model under WVN (TP-WVN). Firstly, two typical prediction models are used as fundamental learners, which can capture the spatial correlations from different angles. Then, regression model is selected as the integrator to combine base models together. Simulative experiments on a real-world dataset are conducted to evaluate the proposal, and results show that the TP-WVN is able to realize reliable QoS-aware prediction compared with baseline methods. |
Sponsor | This work was supported in part by the National Natural Science Foundation of China under grant 62106029, in part by the Humanities and social Science Research Project of the Ministry of Education under grant 21YJC630036, in part by the National Language Commission Research Program of China under grant YB135-121, in part by the Chongqing Natural Science Foundation of China under grant cstc2019jcyj-msxmX0747, in part by the Japan Society for the Promotion of Science (JSPS) Grants-in-Aid for Scientific Research (KAK-ENHI) under Grant JP18K18044 and JP21K17736. |
Language | en |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Subject | machine intelligence QoS reliable pre-diction simulative modeling Wireless vehicular networks |
Type | Conference Paper |
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