AI构建视距径地图的低时延数据路由方法

doi:10.19665/j.issn1001-2400.20250508

西安电子科技大学学报 ›› 2025, Vol. 52 ›› Issue (3): 188-201.doi: 10.19665/j.issn1001-2400.20250508

• 第二十七届中国科协年会——AI时代网络技术创新 • 上一篇下一篇

AI构建视距径地图的低时延数据路由方法

卞留念¹^,²^,³(), 周一青¹^,²^,³(), 刘玲¹^,²^,³(), 李达²(), 时宁哲¹^,²^,³(), 石晶林¹^,²^,³()

1.中国科学院计算技术研究所处理器芯片全国重点实验室,北京 100190
2.中国科学院大学计算机科学与技术学院,北京 100049
3.中国科学院计算技术研究所移动计算与新型终端北京市重点实验室,北京 100190

收稿日期:2025-03-18 出版日期:2025-06-20 发布日期:2025-05-27
通讯作者: 周一青(1975—),女,研究员,E-mail:zhouyiqing@ict.ac.cn
作者简介:卞留念(1997—),男,中国科学院计算技术研究所博士研究生,E-mail:bianliunian18s@ict.ac.cn
刘玲(1990—),女,副研究员,E-mail:liuling@ict.ac.cn
李达(1999—),男,中国科学院计算技术研究所博士研究生,E-mail:lida.ucas@gmail.com
时宁哲(1999—),男,中国科学院计算技术研究所博士研究生,E-mail:shiningzhe21b@ict.ac.cn
石晶林(1972—),男,研究员,E-mail:sjl@ict.ac.cn
基金资助:
国家自然科学基金(U21A20449)

Low-latency data routing method based on an AI-constructed line-of-sight map

BIAN Liunian¹^,²^,³(), ZHOU Yiqing¹^,²^,³(), LIU Ling¹^,²^,³(), LI Da²(), SHI Ningzhe¹^,²^,³(), SHI Jinglin¹^,²^,³()

1. State Key Lab of Processors,Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China
2. School of Computer Science and Technology,University of Chinese Academy of Sciences,Beijing 100049,China
3. Beijing Key Laboratory of Mobile Computing and Pervasive Device,Institute of Computing Technology,Beijing 100190,China

Received:2025-03-18 Online:2025-06-20 Published:2025-05-27

摘要/Abstract

摘要：

在存在爆炸性气体或液体的特殊工厂中,自动导引车需要以移动自组网的形式组网。当工厂中有遮挡物遮挡无线信号时,车辆移动会造成车辆间信道条件在视距和非视距之间变化,车辆节点的无线传输范围随之改变,现有基于固定传输范围的数据路由方法的丢包或时延增加。面向遮挡环境中的移动自组网,文中提出基于视距径地图的低时延数据路由方法。首先,提出了一种基于全连接神经网络构建视距径地图的方法,通过估计路由消息的信噪比生成原始数据,基于信噪比大小将原始数据转换为训练数据,训练全连接神经网络得到视距径地图。然后,提出了一种基于视距径地图的时延感知数据路由协议,可在视距径地图基础上,估计节点间无线链路期望传输次数,基于该次数计算和更新路由评估分数,节点选择路由评估分数大(对应时延小)的路径上的节点作为下一跳转发节点。最后,仿真结果表明,在存在遮挡物的场景中,与基于固定传输范围的数据路由方法相比,文中所提方法能够在不降低数据包交付率的同时将通信时延降低66%。

关键词: 移动自组织网络, 路由协议, 自动导引车, 视距径

Abstract:

In special factories where explosive gases or liquids exist,automated guided vehicles need to be networked in the form of mobile ad hoc networks.When there are obstacles in the factory to block the wireless signal,the vehicle movement will cause the channel conditions between the vehicles to change between line-of-sight and non-line-of-sight,and the wireless transmission range of the vehicle will change accordingly so that the packet loss or delay of the existing data routing methods based on fixed transmission range will increase.Oriented to mobile ad hoc networks in such environments,this paper proposes a low-latency data routing method based on the line-of-sight map.First,a method based on fully-connected neural network is proposed to construct a line-of-sight map,which generates raw data by estimating the signal-to-noise ratio of the routing messages,converts the raw data into training data based on the signal-to-noise ratio magnitude,and trains the fully-connected neural network to obtain the line-of-sight map.Then,a delay-aware data routing protocol is proposed based on the line-of-sight map,which can estimate the expected number of transmissions of wireless links between nodes based on the line-of-sight map,and calculate and update the route evaluation score based on the number of transmissions,with nodes selecting the node with a larger route evaluation score (corresponding to smaller delay) as the next-hop forwarding node.Finally,simulation results show that the proposed method in this paper is able to reduce the communication delay by 66% without decreasing the packet delivery rate compared to the data routing method based on a fixed transmission range.

Key words: mobile ad hoc network, routing protocol, automated guided vehicle, line of sight

中图分类号:

TN929.5

卞留念, 周一青, 刘玲, 李达, 时宁哲, 石晶林. AI构建视距径地图的低时延数据路由方法[J]. 西安电子科技大学学报, 2025, 52(3): 188-201.

BIAN Liunian, ZHOU Yiqing, LIU Ling, LI Da, SHI Ningzhe, SHI Jinglin. Low-latency data routing method based on an AI-constructed line-of-sight map[J]. Journal of Xidian University, 2025, 52(3): 188-201.

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参数名称	参数值	参数名称	参数值
EC消息广播周期/s	1	预测时域长度/s	3
节点数量	30	场地大小/(m×m)	400×400
遮挡物大小/(m×m)	10×10	发射功率/dBm	15
物理层传输速率/(Mb·s^-1)	54	噪声功率/dBm	-110
确认消息超时时间/μs	50	信号接收阈值/dB	20
数据包长度/B	1 000	路由评估分数系数	0.8
最大传输次数	4	移动预测采样周期/s	0.1
视距径判定阈值	0.5	概率分析阈值	0.01
莱斯衰落非中心参数	2	节点移动速度/(m·s^-1)	10
莱斯衰落尺度参数	1	瑞利衰落尺度参数	1

AI构建视距径地图的低时延数据路由方法

Low-latency data routing method based on an AI-constructed line-of-sight map

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