基于四象限分类的大规模自组织网络状态自适应快速感知方法研究

白晶晶; 朱晓荣; 崔涛

doi:10.11959/j.issn.2096-3750.2024.00353

您当前的位置：

首页 >

文章列表页 >

基于四象限分类的大规模自组织网络状态自适应快速感知方法研究

理论与技术 | 更新时间：2024-08-16

- 基于四象限分类的大规模自组织网络状态自适应快速感知方法研究
- Research on adaptive fast sensing method of large-scale Ad Hoc network states based on four-quadrant classification
- 物联网学报 2024年8卷第2期页码：127-137
- 作者机构：
  
  1.南京邮电大学江苏省无线通信重点实验室，江苏南京 210003
  2.深圳友讯达科技股份有限公司，广东深圳 518100
- 作者简介：
  
  [ "白晶晶（2000‒ ），女，南京邮电大学江苏省无线通信重点实验室硕士生，主要研究方向为移动边缘计算、通感算一体化技术。" ]
  [ "朱晓荣（1977‒ ），女，博士，南京邮电大学教授、博士生导师，主要研究方向为5G/6G通信系统、物联网、区块链等关键技术及系统。" ]
  [ "崔涛（1965‒ ），男，深圳友讯达科技股份有限公司董事长、总裁，主要研究方向为无线电通信。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(92067101);江苏省重点研发计划基金资助项目(BE2021013-3;BE2020084-3)
- DOI：10.11959/j.issn.2096-3750.2024.00353
  中图分类号：
- 纸质出版日期：2024-06-10，
  
  收稿日期：2023-03-15，
  
  修回日期：2023-09-11，
- 稿件说明：
移动端阅览
白晶晶,朱晓荣,崔涛.基于四象限分类的大规模自组织网络状态自适应快速感知方法研究[J].物联网学报,2024,08(02):127-137.

BAI Jingjing,ZHU Xiaorong,CUI Tao.Research on adaptive fast sensing method of large-scale Ad Hoc network states based on four-quadrant classification[J].Chinese Journal on Internet of Things,2024,08(02):127-137.
白晶晶,朱晓荣,崔涛.基于四象限分类的大规模自组织网络状态自适应快速感知方法研究[J].物联网学报,2024,08(02):127-137. DOI： 10.11959/j.issn.2096-3750.2024.00353.

BAI Jingjing,ZHU Xiaorong,CUI Tao.Research on adaptive fast sensing method of large-scale Ad Hoc network states based on four-quadrant classification[J].Chinese Journal on Internet of Things,2024,08(02):127-137. DOI： 10.11959/j.issn.2096-3750.2024.00353.

摘要

在大规模自组织网络中，状态感知是实现全局网络视图的先决条件，为网络故障排除、路由决策、网络拓扑动态规划等提供了数据支持。但现有的单一感知机制无法保证状态信息感知的时效性，同时会产生额外的网络开销，降低网络性能。针对上述问题，提出了一种基于四象限分类的大规模自组织网络状态自适应快速感知方法。首先，根据时延敏感度及网络请求频率差异化，将网络状态信息基于四象限图的思想进行分类。其次，针对不同象限的网络状态信息分别设计快速感知策略，同时将网络状态信息封装到管理帧中，以实现嵌入式传输，降低网络开销。最后，通过仿真实验验证了自适应快速感知方法在时效性和信息有效性方面均优于单一的主动上报和请求应答式策略。

Abstract

In large-scale Ad Hoc network

state sensing is a prerequisite for realizing global network view

which provides data support for network troubleshooting

routing decision and dynamic network topology planning. However

the existing single sensing mechanism cannot guarantee the timeliness of state information sensing

and will generate extra network overhead and reduce the network performance. To solve the above problems

an adaptive fast sensing strategy for large-scale Ad Hoc network states was proposed based on four-quadrant classification. Firstly

according to the difference of the delay sensitivity of network state information and network request frequency

it was classified based on the idea of four-quadrant graph. Secondly

fast sensing strategy was designed for network state information in different quadrants

and network state information was encapsulated into management frames to achieve embedded transmission and reduce network overhead. Finally

the simulation experiments show that the adaptive fast sensing method is superior to the single active reporting and request response strategy in terms of timeliness and information validity.

关键词

大规模自组织网络四象限分类自适应快速感知嵌入式传输

Keywords

large-scale Ad Hoc networkfour-quadrant classificationadaptive fast sensingembedded transmission

references

白瑞. 一种面向大规模节点的无线自组织网络接入架构研究[D]. 成都: 电子科技大学, 2022.

BAI R. Research on an access architecture of wireless ad hoc network for large-scale nodes[D]. Chengdu: University of Electronic Science and Technology of China, 2022.

TANG F X, MAO B M, KAWAMOTO Y, et al. Survey on machine learning for intelligent end-to-end communication toward 6G: from network access, routing to traffic control and streaming adaption[J]. IEEE Communications Surveys & Tutorials, 2021, 23(3): 1578-1598.

LIU Z, SUN J, SHEN F, et al. Topology sensing of wireless networks based on Hawkes process[J]. Mobile Networks and Applications,2020, 25(6): 2459-2470.

HONG J, ZHANG D H. TARCS: a topology change aware-based routing protocol choosing scheme of FANETs[J]. Electronics, 2019, 8(3): 274.

GONG M G, JI S F, XIE Y, et al. Exploring temporal information for dynamic network embedding[J]. IEEE Transactions on Knowledge and Data Engineering, 2022, 34(8): 3754-3764.

CHUA M Y K, YU F R, LIANG C C, et al. Software-defined device-to-device (D2D) communications in virtual wireless networks with imperfect network state information (NSI)[J]. IEEE Transactions on Vehicular Technology, 2016, 65(9): 7349-7360.

LEI F Y, LIU X, LI Z M, et al. Multihop neighbor information fusion graph convolutional network for text classification[J]. Mathematical Problems in Engineering, 2021, 2021: 1-9.

CHAI A Y, MA Y, YIN Z Y, et al. Dynamic control model based on state perception[C]//Proceedings of 2019 IEEE 5th International Conference on Computer and Communications (ICCC). Piscataway: IEEE Press, 2020: 1406-1411.

梁佳健. 协同网络中多链路状态动态感知机制的研究与实现[D]. 北京: 北京交通大学, 2018.

LIANG J J. Research and implementation of dynamic sensing mechanism of multi-link state in collaborative network[D]. Beijing: Beijing Jiaotong University, 2018.

AMENTO B, BALASUBRAMANIAN B, HALL R J, et al. FocusStack: orchestrating edge clouds using location-based focus of attention[C]//Proceedings of 2016 IEEE/ACM Symposium on Edge Computing (SEC). Piscataway: IEEE Press, 2016: 179-191.

戴锦友.网络感知技术的标准化研究[J].标准科学, 2022(S1): 86-90.

DAI J Y. Standardization of network awareness technology [J]. Standard Science, 2022 (S1): 86-90.

章广梅. 基于AI的无线网络感知技术研究综述[J]. 电讯技术, 2022, 62(5): 686-694.

ZHANG G M. Researches on wireless network sensing technology based on AI: an overview[J]. Telecommunication Engineering, 2022, 62(5): 686-694.

MIAO W W, WEI L, WU H Y, et al. Fault Processing Algorithm of Power backbone Communication networks Based on Artificial Intelligence and State Perception[C]//Proceedings of 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia). Piscataway: IEEE Press, 2019: 1045-1050.

任育峰. 基于机器学习的网络状态感知分析方法[J]. 移动通信, 2022, 46(9): 30-34.

REN Y F. A network state-aware analysis method based on machine learning[J]. Mobile Communications, 2022, 46(9): 30-34.

赵妙颖. 配电变压器数据感知与智能维护决策研究[D]. 北京: 华北电力大学(北京), 2019.

ZHAO M Y. Research on data perception and intelligent maintenance decision of distribution transformer[D]. Beijing: North China Electric Power University, 2019.

ZHOU D H, YAN Z, LIU G, et al. An adaptive network data collection system in SDN[J]. IEEE Transactions on Cognitive Communications and Networking, 2020, 6(2): 562-574.

胡蓉蓉. 边缘节点服务能力快速感知机制的研究与实现[D]. 南京: 东南大学, 2021.

HU R R. Research and implementation of edge node’s fast sensing mechanism of service capability[D]. Nanjing: Southeast University, 2021.

常立众. 软件定义移动自组织网络状态感知和异常检测[D]. 西安: 西安电子科技大学, 2021.

CHANG L Z. State awareness and anomaly detection in software-defined mobile ad hoc networks[D]. Xi'an: Xidian University, 2021.

毛珍建. 面向多播的带内网络遥测系统设计与实现[D]. 北京: 北京邮电大学, 2021.

MAO Z J. Design and implementation of multicast-oriented in-band network telemetry system[D]. Beijing: Beijing University of Posts and Telecommunications, 2021.

AKIN E, KORKMAZ T. Link-prioritized network state information collection in SDN[C]//Proceedings of ICC 2019 - 2019 IEEE International Conference on Communications (ICC). Piscataway: IEEE Press, 2019: 1-7.

陈浩澜. 基于P4的带内网络遥测技术研究[D]. 成都: 电子科技大学, 2022.

CHEN H L. Research on in-band network telemetry technology based on P4[D]. Chengdu: University of Electronic Science and Technology of China, 2022.

邓晓平, 马路娟. 面向智慧家居的异构自组织网络分簇算法[J].计算机时代, 2023(3):11-16.

DENG X P, MA L J. Clustering algorithm for the heterogeneous adhoc network in smart home[J]. The Computer Age, 2023(3):11-16.

QIONG W. Research on intelligent perception and sensor network technology of urban traffic based on energy tree model[C]//Proceedings of 2022 International Conference on Information System, Computing and Educational Technology (ICISCET). Piscataway: IEEE Press, 2022: 336-341.

WU H Y, TANG Z, XUN S C, et al. Intelligent sensing strategy of terminal equipment in power communication network based on intelligent optical connection box[C]//Proceedings of 2020 International Conference on Computer Communication and Network Security (CCNS). Piscataway: IEEE Press, 2020: 170-173.

LIU J J, PENG B, LIN S J. Research on centralized control architecture deployment of power optical network based on network perception[C]//Proceedings of 2018 IEEE 4th International Conference on Computer and Communications (ICCC). Piscataway: IEEE Press, 2019: 2645-2651.

SUN H T, ZHANG P F, ZHANG Z Q, et al. State perception event-triggered communication scheme for networked control systems[C]//Proceedings of 2020 7th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS). Piscataway: IEEE Press, 2021: 831-836.

YUN K, HUANG Q, MA Y X. Construction of network security perception system using Elman neural network[C]//Proceedings of 2021 2nd International Conference on Computer Communication and Network Security (CCNS). Piscataway: IEEE Press, 2021: 187-190.

ZHANG C, YUAN J N, LUO F, et al. Research on PLC and wireless heterogeneous network based on link awareness[C]//Proceedings of 2018 IEEE International Conference on Computer and Communication Engineering Technology (CCET). Piscataway: IEEE Press, 2018: 91-95.

LIU Z L, GAO S Y, LI P, et al. Distributed state estimation in digital distribution networks based on proximal atomic coordination[J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 1-11.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

暂无数据