端云协同的车载自组网分簇算法

马玲; 杨晓春; 王斌; 宋晓诗; 李发明

doi:10.12263/DZXB.20240686

您当前的位置：

首页 >

文章列表页 >

端云协同的车载自组网分簇算法

云边端协同数据管理、分析与系统 | 更新时间：2025-12-08

- 端云协同的车载自组网分簇算法
- Clustering Algorithms for Vehicular Ad Hoc Networks Based on End-Cloud Collaboration
- 电子学报 2025年53卷第2期页码：354-370
- 作者机构：
  
  东北大学计算机科学与工程学院，辽宁沈阳 110169
- 作者简介：
  
  [ "马玲女，1999年3月出生于河南省.现为东北大学计算机科学与工程学院博士研究生.主要研究方向为无线通信网络、车联网. E-mail: 2410822@stu.neu.edu.cn" ]
  [ "杨晓春女，1973年5月出生于辽宁省.现为东北大学计算机科学与工程学院教授、博士生导师.主要研究方向为大数据管理和知识工程、数据质量管理、数据隐私保护和推荐系统.中国电子学会会员编号：E190035815M. E-mail: yangxc@mail.neu.edu.cn" ]
  [ "王斌男，1972年11月出生于辽宁省.现为东北大学计算机科学与工程学院教授、博士生导师.主要研究方向为算法的设计和分析、流数据查询处理和分布式系统. E-mail: binwang@mail.neu.edu.cn" ]
  [ "宋晓诗男，1983年12月出生于辽宁省.现为东北大学计算机科学与工程学院副教授、博士生导师.主要研究方向为5G及B5G无线通信网络、无线数据缓存网络、D2D无线通信网络、认知无线网络、无线蜂窝网络.中国电子学会会员编号：E190158661M. E-mail: songxiaoshi@cse.neu.edu.cn" ]
  [ "李发明男，1988年1月出生于黑龙江省.现为东北大学计算机科学与工程学院讲师.主要研究方向为数据管理、大数据计算. E-mail: lifaming@mail.neu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(U22A2025;62232007;U23A20309);辽宁省自然科学基金(2023-BSBA-132)
- DOI：10.12263/DZXB.20240686
  中图分类号： TP393;
- 收稿：2024-07-22，
  
  修回：2025-03-06，
  
  纸质出版：2025-02-25
- 稿件说明：
移动端阅览
马玲, 杨晓春, 王斌, 等. 端云协同的车载自组网分簇算法[J]. 电子学报, 2025, 53(02): 354-370.

MA Ling, YANG Xiao-chun, WANG Bin, et al. Clustering Algorithms for Vehicular Ad Hoc Networks Based on End-Cloud Collaboration[J]. Acta Electronica Sinica, 2025, 53(02): 354-370.
马玲, 杨晓春, 王斌, 等. 端云协同的车载自组网分簇算法[J]. 电子学报, 2025, 53(02): 354-370. DOI：10.12263/DZXB.20240686

MA Ling, YANG Xiao-chun, WANG Bin, et al. Clustering Algorithms for Vehicular Ad Hoc Networks Based on End-Cloud Collaboration[J]. Acta Electronica Sinica, 2025, 53(02): 354-370. DOI：10.12263/DZXB.20240686

摘要

随着现代通信和信息技术的飞速发展，智能交通系统（Intelligent Transportation System，ITS）逐渐成为热门研究领域，车载自组网（Vehicular Ad Hoc Network，VANET）作为其关键技术，在实时道路信息共享和车辆间通信中起重要作用.然而，现有VANET分簇算法仍存在簇稳定性低、分簇开销大等问题.为解决这些问题，本文提出了一种端云协同的VANET分簇算法，在端云协同阶段，车辆通过路边单元（Road Side Unit，RSU）将自身特征数据上传至云，云侧根据特征变化，对车辆进行动态稳定性分类.稳定的端节点具有更高的可靠性和更长的连接持续时间.在端端协同阶段，考虑了稳定节点的相对移动性和覆盖节点数量等因素，进行簇头选举，简化簇头选举过程，提高了簇的稳定性.此外，针对控制开销大的问题，本文提出了一种邻居发现和更新机制，限制HELLO消息的转发操作，降低开销并优化资源使用.实验结果表明：本文提出的算法在簇稳定性、簇数量及分簇开销等关键性能指标上均优于基线算法，展示了其在实际交通场景中的应用潜力.

Abstract

With the advancement of modern communication and information technology

intelligent transportation systems(ITS) have emerged as a prominent area of research. The vehicular ad hoc network(VANET)

serving as its pivotal technology

plays a crucial role in facilitating real-time road information sharing and inter-vehicle communication. However

existing clustering algorithms for VANET are plagued by issues such as low stability and high overhead. To address these challenges

this paper proposes a VANET clustering algorithm that leverages end-cloud collaboration. In the end-cloud collaboration phase

vehicles upload their feature data to the cloud via road side units(RSU)

where the cloud performs dynamic stability classification based on changes in vehicle features. Nodes exhibiting stable behavior demonstrate higher reliability and longer connection durations. In the end-to-end coordination phase

factors including relative node mobility and cluster coverage are taken into account during cluster-head election to streamline the process while enhancing cluster stability. Furthermore

this paper introduces a neighbor discovery and update mechanism aimed at restricting HELLO message forwarding operations to reduce overhead and optimize resource utilization. Experimental results demonstrate that the proposed algorithm surpasses baseline algorithms across key performance metrics such as cluster stability

quantity of clusters formed

and clustering costs—highlighting its potential applicability in real-world traffic scenarios.

关键词

Keywords

references

MILES J C , 陈干 . 智能交通系统手册 [M ] . 王笑京译. 北京 : 人民交通出版社 , 2007 : 201 - 203 .

MILES J C , CHEN G . Intelligent Transportation Systems Handbook [M ] . WANG X J, trans. Beijing : China Communications Press , 2007 : 201 - 203 . (in Chinese)

LIMBASIYA T , DAS D . Secure message transmission algorithm for Vehicle to Vehicle (V 2 V) communication [C ] // 2016 IEEE Region 10 Conference (TENCON) . Piscataway : IEEE , 2016 : 2507 - 2512 .

ALI KARABULUT M , SHAH A F M S , ILHAN H , et al . Inspecting VANET with various critical aspects - A systematic review [J ] . Ad Hoc Networks , 2023 , 150 : 103281 .

ZANJIREH M M , LARIJANI H . A survey on centralised and distributed clustering routing algorithms for WSNs [C ] // 2015 IEEE 81st Vehicular Technology Conference (VTC Spring) . Piscataway : IEEE , 2015 : 1 - 6 .

CAMPOLO C , MOLINARO A . On vehicle-to-roadside communications in 802.11p/WAVE VANETs [C ] // 2011 IEEE Wireless Communications and Networking Conference . Piscataway : IEEE , 2011 : 1010 - 1015 .

ANWER M S , GUY C . A survey of VANET technologies [J ] . Journal of Emerging Trends in Computing and Information Sciences , 2014 , 5 ( 9 ): 661 - 671 .

MUKHTARUZZAMAN M , ATIQUZZAMAN M . Clustering in vehicular ad hoc network: Algorithms and challenges [J ] . Computers & Electrical Engineering , 2020 , 88 : 106851 .

AYYUB M , ORACEVIC A , HUSSAIN R , et al . A comprehensive survey on clustering in vehicular networks: Current solutions and future challenges [J ] . Ad Hoc Networks , 2022 , 124 : 102729 .

KATIYAR A , SINGH D , YADAV R S . State-of-the-art approach to clustering protocols in VANET: A survey [J ] . Wireless Networks , 2020 , 26 ( 7 ): 5307 - 5336 .

JABBAR M K , TRABELSI H . A review on clustering in VANET: Algorithms, phases, and comparisons [C ] // 2022 19th International Multi-Conference on Systems , Signals & Devices (SSD) . Piscataway : IEEE , 2022 : 444 - 451 .

LATIF S , MAHFOOZ S , JAN B , et al . A comparative study of scenario-driven multi-hop broadcast protocols for VANETs [J ] . Vehicular Communications , 2018 , 12 : 88 - 109 .

GERLA M , TZU-CHIEH TSAI J . Multicluster, mobile, multimedia radio network [J ] . Wireless Networks , 1995 , 1 ( 3 ): 255 - 265 .

CHEN G , NOCETTI F G , GONZALEZ J S , et al . Connectivity based k-hop clustering in wireless networks [C ] // Proceedings of the 35th Annual Hawaii International Conference on System Sciences . Piscataway : IEEE , 2002 : 2450 - 2459 .

BASU P , KHAN N , LITTLE T D C . A mobility based metric for clustering in mobile ad hoc networks [C ] // Proceedings of the 21st International Conference on Distributed Computing Systems Workshops . Piscataway : IEEE , 2001 : 413 - 418 .

ZHANG Z X , BOUKERCHE A , PAZZI R . A novel multi-hop clustering scheme for vehicular ad-hoc networks [C ] // Proceedings of the 9th ACM International Symposium on Mobility Management and Wireless Access . New York : ACM , 2011 : 19 - 26 .

UCAR S , ERGEN S C , OZKASAP O . VMaSC: Vehicular multi-hop algorithm for stable clustering in vehicular ad hoc networks [C ] // 2013 IEEE Wireless Communications and Networking Conference (WCNC) . Piscataway : IEEE , 2013 : 2381 - 2386 .

BASAGNI S . Distributed clustering for ad hoc networks [C ] // Proceedings of the 4th International Symposium on Parallel Architectures, Algorithms, and Networks (I-SPAN’99) . Piscataway : IEEE , 2002 : 310 - 315 .

WOLNY G . Modified DMAC clustering algorithm for VANETs [C ] // Proceedings of the 3rd International Conference on Systems and Networks Communications . Piscataway : IEEE , 2008 : 268 - 273 .

CHATTERJEE M , DAS S K , TURGUT D . An on-demand weighted clustering algorithm (WCA) for ad hoc networks [C ] // Global Telecommunications Conference (Globecom’00 - IEEE) . Piscataway : IEEE , 2002 : 1697 - 1701 .

DHURANDHER S K , SINGH G V . Weight based adaptive clustering in wireless ad hoc networks [C ] // 2005 IEEE International Conference on Personal Wireless Communications (ICPWC 2005) . Piscataway : IEEE , 2005 : 95 - 100 .

SENOUCI O , HAROUS S , ALIOUAT Z . An efficient weight-based clustering algorithm using mobility report for IoV [C ] // Proceedings of the 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) . Piscataway : IEEE , 2018 : 614 - 620 .

ZHAO H T , TANG J W , ADEBISI B , et al . An adaptive vehicle clustering algorithm based on power minimization in vehicular ad-hoc networks [J ] . IEEE Transactions on Vehicular Technology , 2022 , 71 ( 3 ): 2939 - 2948 .

KENNEDY J , EBERHART R . Particle swarm optimization [C ] // Proceedings of ICNN’95 - International Conference on Neural Networks . Piscataway : IEEE , 1995 : 1942 - 1948 .

HEIDARI A A , MIRJALILI S , FARIS H , et al . Harris Hawks optimization: Algorithm and applications [J ] . Future Generation Computer Systems , 2019 , 97 : 849 - 872 .

MIRJALILI S , MIRJALILI S M , LEWIS A . Grey wolf optimizer [J ] . Advances in Engineering Software , 2014 , 69 : 46 - 61 .

GOLBERG D E . Genetic algorithms in search, optimization, and machine learning [J ] . Machine Learning , 1988 , 3 : 95 - 99 .

SHAHZAD W , KHAN F A , SIDDIQUI A B . Clustering in mobile ad hoc networks using comprehensive learning particle swarm optimization (CLPSO) [M ] // Communication and Networking . Berlin : Springer , 2009 : 342 - 349 .

FAHAD M , AADIL F , REHMAN Z U , et al . Grey wolf optimization based clustering algorithm for vehicular ad-hoc networks [J ] . Computers & Electrical Engineering , 2018 , 70 : 853 - 870 .

ALI A , AADIL F , KHAN M F , et al . Harris hawks optimization-based clustering algorithm for vehicular ad-hoc networks [J ] . IEEE Transactions on Intelligent Transportation Systems , 2023 , 24 ( 6 ): 5822 - 5841 .

CHAROENCHAI S , SIRIPONGWUTIKORN P . Genetic algorithm for multi-hop VANET clustering based on coalitional game [J ] . Journal of Network and Systems Management , 2024 , 32 ( 1 ): 9 .

MYERSON R B . Graphs and cooperation in games [J ] . Mathematics of Operations Research , 1977 , 2 ( 3 ): 225 - 229 .

QI G Q , DU Y M , WU J P , et al . Leveraging longitudinal driving behaviour data with data mining techniques for driving style analysis [J ] . IET Intelligent Transport Systems , 2015 , 9 ( 8 ): 792 - 801 .

ZHANG L R , LAKAS A , EL-SAYED H , et al . Mobility analysis in vehicular ad hoc network (VANET) [J ] . Journal of Network and Computer Applications , 2013 , 36 ( 3 ): 1050 - 1056 .

ZHENG J , TONG H , WU Y Y . A cluster-based delay tolerant routing algorithm for vehicular ad hoc networks [C ] // 2017 IEEE 85th Vehicular Technology Conference (VTC Spring) . Piscataway : IEEE , 2017 : 1 - 5 .

WENZEL T P , ROSS M . The effects of vehicle model and driver behavior on risk [J ] . Accident Analysis & Prevention , 2005 , 37 ( 3 ): 479 - 494 .

LI Z P , ZHANG R , XU S Z , et al . Study on the effects of driver’s lane-changing aggressiveness on traffic stability from an extended two-lane lattice model [J ] . Communications in Nonlinear Science and Numerical Simulation , 2015 , 24 ( 1/2/3 ): 52 - 63 .

CHEUNG E , BERA A , KUBIN E , et al . Identifying driver behaviors using trajectory features for vehicle navigation [C ] // 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . Piscataway : IEEE , 2018 : 3445 - 3452 .

SU Z Z , WOODMAN R , SMYTH J , et al . The relationship between aggressive driving and driver performance: A systematic review with meta-analysis [J ] . Accident Analysis & Prevention , 2023 , 183 : 106972 .

VAIANA R , IUELE T , ASTARITA V , et al . Driving behavior and traffic safety: An acceleration-based safety evaluation procedure for smartphones [J ] . Modern Applied Science , 2013 , 8 ( 1 ): 88 - 96 .

BREIMAN L . Random forests [J ] . Machine Learning , 2001 , 45 ( 1 ): 5 - 32 .

CAMPANILE L , GRIBAUDO M , IACONO M , et al . Computer network simulation with ns-3: A systematic literature review [J ] . Electronics , 2020 , 9 ( 2 ): 272 .

UCAR S , ERGEN S C , OZKASAP O . Multihop-cluster-based IEEE 802.11p and LTE hybrid architecture for VANET safety message dissemination [J ] . IEEE Transactions on Vehicular Technology , 2016 , 65 ( 4 ): 2621 - 2636 .

CHEN Y Z , FANG M Y , SHI S , et al . Distributed multi-hop clustering algorithm for VANETs based on neighborhood follow [J ] . EURASIP Journal on Wireless Communications and Networking , 2015 , 2015 ( 1 ): 98 .

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

基于邻域跟随的车联网多跳分簇稳定性研究

无线传感器网络一种基于聚合收益的动态成簇算法