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
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.
Fig.1Blockchain assisted vehicular social network architecture
Fig.2Formation of activity-aware social vehicle clusters
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
Tab.1Description of vehicle status
Fig.3Transition of states of vehicles during clustering
Fig.4Process of cluster head election
Fig.5Simulation map of SUMO
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
Tab.2Simulation parameters in process of vehicle clustering
Fig.6Influence of stable communication distance on number of cluster members
Fig.7Influence of stable communication distance and number of cluster members on duration of vehicles within clusters
Fig.8Influence of stable communication distance and number of cluster members on number of vehicles in clusters
Fig.9Influence of maximum speed of vehicles on stability of clusters
Fig.10Influence of maximum speed of vehicles on duration of clusters using different clustering algorithms
Fig.11Influence of maximum speed of vehicles on number of vehicles in different states in cluster using different clustering algorithms
Fig.12Influence of weighting factor on total similarity score of vehicles
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]
Tab.3Description of three typical road scenes
Fig.13Influence of weighting factor on total similarity score of vehicles under different road scenarios
Fig.14Four kinds of scores of vehicles V1 to V5
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
Tab.4Comparison of cluster head selection results
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