Digital Twin Based Network Latency Prediction in Vehicular Networks

Fu, Yanfang, Guo, Dengdeng, Li, Qiang, Liu, Liangxin, Qu, Shaochun, and Xiang, Wei (2022) Digital Twin Based Network Latency Prediction in Vehicular Networks. Electronics, 11 (14). 2217.

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Network latency is a crucial factor affecting the quality of communications networks due to the irregularity of vehicular traffic. To address the problem of performance degradation or instability caused by latency in vehicular networks, this paper proposes a time delay prediction algorithm, in which digital twin technology is employed to obtain a large quantity of actual time delay data for vehicular networks and to verify autocorrelation. Subsequently, to meet the prediction conditions of the ARMA time series model, two neural networks, i.e., Radial basis function (RBF) and Elman networks, were employed to construct a time delay prediction model. The experimental results show that the average relative error of the RBF is 7.6%, whereas that of the Elman-NN is 14.2%. This indicates that the RBF has a better prediction performance, and a better real-time performance than the Elman-NN.

Item ID: 76479
Item Type: Article (Research - C1)
ISSN: 2079-9292
Keywords: ARMA, digital twin, Elman, network latency, RBF, time delay, vehicle network
Copyright Information: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
Date Deposited: 27 Feb 2023 07:36
FoR Codes: 40 ENGINEERING > 4002 Automotive engineering > 400203 Automotive mechatronics and autonomous systems @ 50%
46 INFORMATION AND COMPUTING SCIENCES > 4606 Distributed computing and systems software > 460609 Networking and communications @ 50%
SEO Codes: 22 INFORMATION AND COMMUNICATION SERVICES > 2201 Communication technologies, systems and services > 220105 Network systems and services @ 50%
27 TRANSPORT > 2703 Ground transport > 270301 Active ground transport @ 50%
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