基于邻域跟随的车联网多跳分簇稳定性研究

林志坚; 吴文浩; 陈小培; 曾泽雄; 林永航; 陈平平

doi:10.12263/DZXB.20231099

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基于邻域跟随的车联网多跳分簇稳定性研究

学术论文 | 更新时间：2025-12-08

- 基于邻域跟随的车联网多跳分簇稳定性研究
- Research on the Stability of Multi-Hop Clustering Based on Neighbor Following Method in Vehicular Networks
- 电子学报 2024年52卷第4期页码：1260-1268
- 作者机构：
  
  1.福州大学物理与信息工程学院，福建福州 350108
  2.华南理工大学未来技术学院，广东广州 511442
- 作者简介：
  
  [ "林志坚男，1984年出生于福建省漳州市.现为福州大学电子信息工程系副教授，硕士生导师.主要研究方向为车联网、边缘缓存与计算卸载.中国电子学会会员编号：E190050609M.E-mail: zlin@fzu.edu.cn" ]
  [ "吴文浩男，2000年3月出生于福建省宁德市.福州大学物理与信息工程学院研究生.主要研究方向为车联网、边缘缓存与计算卸载. E-mail: 221127202@fzu.edu.cn" ]
  [ "陈平平男，1986年出生于福建省泉州市.现为福州大学电子信息工程系教授，博士生导师.主要研究方向为无线通信、信息传输与计算卸载.中国电子学会会员编号：E190021215M. E-mail: ppchen.xm@gmail.com" ]
- 基金信息：
  
  国家自然科学基金(62171135;62001117);福建省杰出青年科学基金(2022J06010)
- DOI：10.12263/DZXB.20231099
  中图分类号： TP393.0
- 收稿：2023-11-24，
  
  修回：2024-01-29，
  
  纸质出版：2024-04-25
- 稿件说明：
移动端阅览
林志坚,吴文浩,陈小培,等. 基于邻域跟随的车联网多跳分簇稳定性研究[J]. 电子学报,2024,52(04):1260-1268.

LIN Zhi-jian, WU Wen-hao, CHEN Xiao-pei, et al. Research on the Stability of Multi-Hop Clustering Based on Neighbor Following Method in Vehicular Networks[J]. Acta Electronica Sinica, 2024, 52(04): 1260-1268.
林志坚,吴文浩,陈小培,等. 基于邻域跟随的车联网多跳分簇稳定性研究[J]. 电子学报,2024,52(04):1260-1268. DOI：10.12263/DZXB.20231099

LIN Zhi-jian, WU Wen-hao, CHEN Xiao-pei, et al. Research on the Stability of Multi-Hop Clustering Based on Neighbor Following Method in Vehicular Networks[J]. Acta Electronica Sinica, 2024, 52(04): 1260-1268. DOI：10.12263/DZXB.20231099

摘要

针对车联网计算卸载需求量大及车辆网络拓扑变化随机性强的问题，本文提出一种基于邻域跟随的车辆多跳分簇方法，并采取多指标综合加权的方式研究其稳定性特征.现有针对车辆分簇的研究方案普遍存在指标单一化、分簇机制局限于单跳或固定跳数、分簇效率低及缺少簇头替换机制等问题.鉴于邻域跟随策略稳定性较优且分簇效率较高等特性，本文提出车辆基于邻居节点内综合指标最优准则选择稳定性最好的节点跟随方案，通过去环和剪枝算法使得车辆的跟随结构规范且扁平化，进而形成一个稳定性更高的车辆簇，并利用主从簇头和簇维护机制增强车辆簇的鲁棒性.仿真结果表明，与现有的车辆分簇方法相比，所提算法在簇稳定性和分簇效率上均具有较好的性能.

Abstract

In view of the problems of high demand of computation offloading in vehicular networks and the randomness of topology variation of vehicular networks

this paper proposes a vehicular multi-hop clustering scheme based on neighbor following and investigates its stability by multiple indicators. Existing research offers various vehicular clustering schemes

but they suffer from issues such as single indicator

being limited to a single hop or fixed number of hops

low clustering efficiency

and lacking a mechanism for cluster head replacement. As the neighbor following strategy has better stability and higher clustering efficiency

in the scheme designed in this paper

vehicles should follow the most stable vehicle based on the comprehensive indicators within neighboring nodes. The de-ringing and pruning algorithms are used to standardize and flatten the following structure of vehicles

forming a more stable vehicular cluster. The master-slave cluster header and the cluster maintenance mechanism are employed to enhance the robustness of vehicular cluster. Simulation results show that the proposed algorithm outperforms existing methods in terms of cluster stability and clustering efficiency.

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references

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