Mathematical prediction model construction of network packet loss rate and nonlinear mapping user experience under the Internet of Things

IF 2.4 Q2 ENGINEERING, MECHANICAL
Bin Fan, B. Nagaraj
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引用次数: 0

Abstract

Abstract In order to further improve the prediction accuracy, the network packet loss rate (PLR) prediction mathematical model based on the Internet of Things (IoTs) was proposed. First, the network data transmission module was established, and the network PLR prediction process was developed based on IoTs; second, the prediction framework of PLR was designed to obtain more accurate prior information. The relationship between PLR and user experience quality QoE is univariate and nonlinear. The mapping between PLR and user experience quality QoE is established using univariate nonlinear regression analysis; finally, a mathematical model of network PLR prediction is constructed to further improve the prediction accuracy. Experimental results show that the delays of network nodes are all within 5 s, which can ensure the real-time nature of data transmission. When the total number of packets and the number of lost packets are the same, the PLR predicted by the mathematical model designed by the authors is consistent with the actual PLR. Conclusion: The prediction effect of the model is better and has higher promotion value.
物联网下网络丢包率的数学预测模型构建与用户体验的非线性映射
摘要为了进一步提高预测精度,提出了基于物联网(iot)的网络丢包率(PLR)预测数学模型。首先,建立了网络数据传输模块,开发了基于物联网的网络PLR预测流程;其次,设计PLR预测框架,获得更准确的先验信息;PLR与用户体验质量QoE之间存在单变量非线性关系。利用单变量非线性回归分析建立了用户体验质量QoE与PLR之间的映射关系;最后,建立了网络PLR预测的数学模型,进一步提高了网络PLR预测的精度。实验结果表明,网络节点时延均在5s以内,保证了数据传输的实时性。当总包数和丢包数相同时,所设计的数学模型预测的PLR与实际PLR一致。结论:该模型预测效果较好,具有较高的推广价值。
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来源期刊
CiteScore
6.20
自引率
3.60%
发文量
49
审稿时长
44 weeks
期刊介绍: The Journal of Nonlinear Engineering aims to be a platform for sharing original research results in theoretical, experimental, practical, and applied nonlinear phenomena within engineering. It serves as a forum to exchange ideas and applications of nonlinear problems across various engineering disciplines. Articles are considered for publication if they explore nonlinearities in engineering systems, offering realistic mathematical modeling, utilizing nonlinearity for new designs, stabilizing systems, understanding system behavior through nonlinearity, optimizing systems based on nonlinear interactions, and developing algorithms to harness and leverage nonlinear elements.
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