Activity-aware social vehicle clustering algorithm

doi:10.3785/j.issn.1008-973X.2022.05.022

Journal of ZheJiang University (Engineering Science)

2022, Vol. 56

Issue (5): 1044-1054 DOI: 10.3785/j.issn.1008-973X.2022.05.022

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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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

Received: 29 September 2021 Published: 31 May 2022

CLC:	TN
	92	(Hydrospheric and atmospheric geophysics)

Fund: 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）

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Cite this article:

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.

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

活跃度感知的社交车辆分簇算法

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

Fig.1 Blockchain assisted vehicular social network architecture

Fig.2 Formation of activity-aware social vehicle clusters

Tab.1 Description of vehicle status

Fig.3 Transition of states of vehicles during clustering

Fig.4 Process of cluster head election

Fig.5 Simulation map of SUMO

Tab.2 Simulation parameters in process of vehicle clustering

Fig.6 Influence of stable communication distance on number of cluster members

Fig.7 Influence of stable communication distance and number of cluster members on duration of vehicles within clusters

Fig.8 Influence of stable communication distance and number of cluster members on number of vehicles in clusters

Fig.9 Influence of maximum speed of vehicles on stability of clusters

Fig.10 Influence of maximum speed of vehicles on duration of clusters using different clustering algorithms

Fig.11 Influence of maximum speed of vehicles on number of vehicles in different states in cluster using different clustering algorithms

Fig.12 Influence of weighting factor on total similarity score of vehicles

Tab.3 Description of three typical road scenes

Fig.13 Influence of weighting factor on total similarity score of vehicles under different road scenarios

Fig.14 Four kinds of scores of vehicles V1 to V5

Tab.4 Comparison of cluster head selection results


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