A novel approach of tensor‐based data missing estimation for Internet of Vehicles,International Journal of Communication Systems

当前位置： X-MOL 学术 › Int. J. Commun. Syst. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

A novel approach of tensor‐based data missing estimation for Internet of Vehicles
International Journal of Communication Systems ( IF 1.7 ) Pub Date : 2020-05-06 , DOI: 10.1002/dac.4433
Ting Zhang _{1,

2} , Degan Zhang _{1,

2} , Jinxin Gao _{1,

2} , Jie Chen ₃ , Kaiwen Jiang _{1,

2}

Affiliation

In the face of the current huge amount of intelligent traffic data, collecting and statistical processing is a necessary and important process. But the inevitable data missing problem is the current research focus. In this paper, a novel approach of tensor‐based data missing estimation for Internet of Vehicles is proposed for the problem of missing the Internet of Vehicles data: Integrated Bayesian tensor decomposition (IBTD). In the data model construction stage, the random sampling principle is used to randomly extract the missing data to generate a subset of data. And the optimized Bayesian tensor decomposition algorithm is used for interpolation. Introduce the integration idea, analyze, and sort the error results after multiple interpolations, consider the space‐time complexity, and choose the optimal average to get the best result. The performance of the proposed model was evaluated by mean absolute percentage error (MAPE) and root mean square error (RMSE). The experimental results show that the proposed method can effectively interpolate the traffic data sets with different missing quantities and get good interpolation results.

中文翻译：

一种基于张量的车辆互联网数据丢失估计的新方法

面对当前大量的智能交通数据，收集和统计处理是必要且重要的过程。但是不可避免的数据丢失问题是当前的研究重点。本文针对车辆网络数据缺失的问题，提出了一种基于张量的车辆网络数据缺失估计的新方法：集成贝叶斯张量分解（IBTD）。在数据模型构建阶段，使用随机采样原理随机抽取丢失的数据以生成数据子集。并采用优化的贝叶斯张量分解算法进行插值。引入积分思想，对多次插值后的误差结果进行分析和排序，考虑时空复杂度，并选择最佳平均值以获得最佳结果。通过平均绝对百分比误差（MAPE）和均方根误差（RMSE）评估所提出模型的性能。实验结果表明，该方法能够有效地插补丢失量不同的交通数据集，并取得良好的插补效果。

更新日期：2020-05-06

点击分享查看原文

点击收藏

阅读更多本刊最新论文本刊介绍/投稿指南11