Abstract: Clustering is an effective method to improve the perception quality of Vehicular Crowd Sensing (VCS) and reduce costs.However,how to maximize the cluster stability while accounting for the high mobility of vehicles remains a challenging problem.Based on the communication characteristics of VANET,a clustering algorithm based on Fog Computing and Self-Assessment (FCSAC) is proposed,which divides VANET into many clusters,and each cluster selects a Master Cluster Head(MCH) for data dissemination.The results of vehicle cooperative perception in the cluster are given to the fog nodes by MCH,the vehicle mobility rate (VMR) is introduced to improve Master Cluster Head(MCH) election method,this parameter is calculated based on mobility metrics to satisfy the need for VANET great mobility.Then,this paper evaluates the impact of vehicle joining on cluster stability by defining scaling functions and weighting mechanisms.FCSAC strengthens clusters’ stability through the election of a Slave Cluster Head (SCH) in addition to the MCH.In order to improve the accuracy,timeliness,and effectiveness of traffic information,on the basis of fog computing,via chain collaboration traffic perception between the MCH,an accurate and comprehensive view of the local traffic perception is formed.Finally,the Veins simulation platform is used to eva-luate the performance.The results show that,compared with the CBRSDN algorithm and SACBR algorithm,the proposed algorithm performs better in terms of cluster stability,and effectively improves the throughput of VANET.Compared with the Fuzzy C-Means (FCM) algorithm,it has better traffic diversion capability and reduces the consumption of network communication.

Key words: Clustering, Cooperative perception, Crowd sensing, Fog computing, Internet of vehicles, Vehicular-hoc networks