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浙江大学学报(工学版)
浙江大学学报(工学版)  2022, Vol. 56 Issue (5): 1044-1054    DOI: 10.3785/j.issn.1008-973X.2022.05.022
电子与通信工程     
活跃度感知的社交车辆分簇算法
张海波1,2(),刘子琪1,2,刘开健1,2,徐勇军1
1. 重庆邮电大学 通信与信息工程学院，重庆 400065
2. 移动通信教育部工程研究中心，重庆 400065
Activity-aware social vehicle clustering algorithm
Hai-bo ZHANG1,2(),Zi-qi LIU1,2,Kai-jian LIU1,2,Yong-jun XU1
1. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2. Engineering Research Center of Mobile Communications, Ministry of Education, Chongqing 400065, China
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摘要：

为了解决车联网(IoV)中因车辆高速移动和拓扑结构多变导致的车辆间数据传输链路不稳定甚至中断的问题，提出一种活跃度感知的社交车辆分簇算法. 在簇头(CH)筛选过程中，考虑由相对加速度、速度和相对距离构成的移动相似性分值以及由兴趣相似度定义的社交相似性分值，加权求和得到车辆相似性分值. 利用基数排序算法排序并筛选出分值最高者作为簇头候选者(CHc)，保证集群的稳定性. 引入由车辆历史数据处理量和车辆请求资源次数构成的活跃度的概念，通过对其进行判断，从簇头候选者中筛选出真正有社交意愿和能力的簇头，提升簇内亲密度. 使用OMNet++平台进行仿真，结果表明，与传统算法相比，采用所提算法，能使得集群在保持稳定性的同时，亲密度有所提升.
关键词： 车联网(IoV);  活跃度;  分簇算法;  社交相似性;  亲密度    
Abstract:

An activity-aware social vehicle clustering algorithm was proposed, in order to solve the problem of instability or interruption of data transmission link between vehicles due to the high mobility of vehicles and the changeable topology in the Internet of Vehicles (IoV). In the cluster head (CH) selection process, the mobility similarity score composed of relative acceleration, speed and distance and social similarity score defined by interest similarity were considered, then were weighted and summed to obtain similarity score. The radix sorting algorithm was used to sort and select cluster head candidates (CHc) with highest scores, which ensured the stability. Activity degree consisting of the amount of historical processed data and the number of requests for resources was introduced. By measuring it, the CH with real social willingness and ability was selected from CHcs, and this increases the intimacy within clusters. Simulation results on the OMNet++ platform show that compared with traditional algorithms, the proposed algorithm maintains the stability of the cluster while increasing the intimacy.
Key words: Internet of Vehicles (IoV)    activity degree    clustering algorithm    social similarity    intimacy
收稿日期: 2021-09-29 出版日期: 2022-05-31
CLC:  TN   
  92 (Hydrospheric and atmospheric geophysics)  
基金资助: 国家自然科学基金资助项目（61801065）；长江学者和创新团队发展计划基金资助项目（IRT16R72）；重庆市留创计划创新类资助项目（cx2020059）
作者简介: 张海波（1979—）男，副教授，博士，从事车联网研究. orcid.org/0000-0003-2719-9956. E-mail: zhanghb@cqupt.edu.cn
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引用本文:

张海波,刘子琪,刘开健,徐勇军. 活跃度感知的社交车辆分簇算法[J]. 浙江大学学报(工学版), 2022, 56(5): 1044-1054.

Hai-bo ZHANG,Zi-qi LIU,Kai-jian LIU,Yong-jun XU. Activity-aware social vehicle clustering algorithm. Journal of ZheJiang University (Engineering Science), 2022, 56(5): 1044-1054.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2022.05.022    或    https://www.zjujournals.com/eng/CN/Y2022/V56/I5/1044

图 1  区块链辅助的车载社交网络架构
图 2  活跃度感知的社交车辆集群形成图
状态 状态说明
未定义 (undefined, UD) 所有车辆的初始状态，此状态下车辆不属于任何簇
簇头(cluster head, CH) 簇内的唯一领导者，通过一跳成员列表查询簇员状态信息
簇员(cluster meber, CM) 与簇头有相同兴趣的
一跳邻居车辆
簇头候选者(cluster head candidate, CHc) 仅在分簇过程中暂时存在，簇头选定后就会消失
表 1  车辆状态说明表
图 3  分簇过程中车辆的状态转换图
图 4  簇头选举流程图
图 5  SUMO仿真道路场景图
参数名称 取值
仿真时间 ${T^{ {\text{sim} } } }$/s 500
MAC协议 802.11p
车辆数量 $ N $/辆 500
稳定通信范围 $D_{\rm{v}}^{ {\text{st} } }$/m 200~500
最大车速 ${V_{{\text{MAX}}}}$/(km?h?1) 60
道路长度 $ L $/km 5
车辆长度/m 5
加速度/(m?s?2) 2.6
减速度/(m?s?2) 4.5
BI/s 1
$q$ 0.2
路径损耗模型 2径模型
迭代次数 $ \xi $[21]/次 10
表 2  车辆分簇过程仿真设置参数表
图 6  车辆间稳定通信距离对成簇数的影响
图 7  稳定通信距离和成簇数对集群持续时间的影响
图 8  稳定通信距离和成簇数对集群车辆数量的影响
图 9  最大车速对集群稳定性的影响
图 10  不同分簇算法下最大车速对集群持续时间的影响
图 11  不同分簇算法下最大车速对集群不同状态车辆数量的影响
图 12  权重因子对车辆总相似性分值的影响
场景 ${V_{{\rm{MAX}}} }$/(km?h?1) $D_{\rm{v}}^{ {\text{st} } }$/m $q$
堵车道路 18[23] 200 (0.1, 1.0]
城市道路 60 300 (0.2, 1.0]
高速道路 100 400 (0, 0.2]
表 3  3种典型道路场景说明
图 13  不同道路场景下权重因子对车辆总相似性分值的影响
图 14  车辆V1~V5的4种分值散点图
算法 IDCH $ S_i^{{\text{Act}}} $ $ S_i^{{\text{tot}}} \left( {q = 0.2} \right) $ $ {S_i} $ $ {I_i} $
本研究所提分簇算法 V4 0.65 0.8124 0.823 0.7700
经典最小编号分簇算法 V1 0.58 0.7795 0.793 0.7253
自适应分簇算法 V3 0.61 0.8080 0.840 0.6800
动态分簇算法 V3 0.61 0.8080 0.840 0.6800
表 4  簇头筛选结果对比
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