基于车联网的中心导航云性能分析模型

吉林大学学报(理学版) ›› 2025, Vol. 63 ›› Issue (4): 1150-1156.

基于车联网的中心导航云性能分析模型

冷嵩¹, 李志强², 高金梁¹, 刘衍珩^1,2

1. 珠海科技学院计算机学院, 广东珠海 519041; 2. 吉林大学计算机科学与技术学院, 长春 130012

收稿日期:2024-11-14 出版日期:2025-07-26 发布日期:2025-07-26
通讯作者: 冷嵩 E-mail:lengsong2022@zcst.edu.cn

Performance Analysis Model of Central Navigation Cloud Based on Internet of Vehicles

LENG Song¹, LI Zhiqiang², GAO Jinliang¹, LIU Yanheng^1,2

1. School of Computer Science, Zhuhai College of Science and Technology, Zhuhai 519041, Guangdong Province, China； 2. College of Computer Science and Technology, Jilin University, Changchun 130012, China

Received:2024-11-14 Online:2025-07-26 Published:2025-07-26

摘要/Abstract

摘要： 为在给定道路节点数和车辆数的条件下, 使用最小的交通导航云规模, 提高道路的通行效率, 提出一种基于目标地图和云中心相关参数的性能分析模型. 首先, 分析影响导航性能的目标地图和云平台的各相关参数；其次, 考虑道路节点数和车辆数以及不同规模和频率的虚拟机, 并进行3组实验；最后, 使用真实数据集对模型的有效性进行验证. 实验结果表明, 该性能分析模型能准确计算出满足约束条件的导航云最小规模.

关键词: 车联网, 导航云, 性能分析, 动态路径, 排队论

Abstract: In order to improve efficiency of road traffic, by using the minimum traffic navigation cloud sacle given number of road nodes and vehicles, we proposed a performance analysis model based on target map and cloud center related parameters. Firstly, we analyzed the target map and the relevant parameters of the cloud platform that affected navigation performance. Secondly, we considered the number of road nodes and vehicles, as well as virtual machines of different scales and frequencies, and conducted three sets of experiments. Finally, we verified the effectiveness of the model by using real datasets. The experimental results show that the proposed performance analysis model can accurately calculate the minimum scale of navigation cloud that satisfies the constraint conditions.

Key words: Internet of vehicle, navigation cloud, performance analysis, dynamic route, queuing theory

中图分类号:

TP309

冷嵩, 李志强, 高金梁, 刘衍珩. 基于车联网的中心导航云性能分析模型[J]. 吉林大学学报(理学版), 2025, 63(4): 1150-1156.

LENG Song, LI Zhiqiang, GAO Jinliang, LIU Yanheng. Performance Analysis Model of Central Navigation Cloud Based on Internet of Vehicles[J]. Journal of Jilin University Science Edition, 2025, 63(4): 1150-1156.