Research On Self-adaptive Power Minimized Vehicle Clustering Algorithm For Mobile Edge Computing In Vehicular Communication

Due to the highly unstable characteristics of the VANET,it poses a great challenge to the low delay and high reliability of data forwarding in vehicular communication.In addition,with the increasing of the various time-delay sensitive tasks in Io V,the cloud computing can no longer meet the processing requirements of millisecond delay.Therefore,the application of mobile edge computing in Io V is the trend.At the same time,the vehicles will have sufficient computing resources to act as the edge server in the vehicle edge network.In this case,how to design an efficient vehicle clustering algorithm to improve the robustness of the network and reduce the overall power consumption of the vehicle has become an urgent problem.This paper focuses on the vehicle clustering in vehicle-mounted edge network,the overall power consumption of the on-board edge server is reduced and the stability of the VANET is improved by proposing an adaptive optimal energy consumption model and a new vehicle clustering algorithm.The main research work of this paper is as follows:Firstly,this paper proposed an adaptive power consumption optimal model for the problem of high energy consumption caused by the fact that vehicles can be considered as edge service nodes to provide edge cloud services for adjacent vehicles or pedestrians.The proposed model replaces the current method of evenly distributing CPU,and dynamically adjusts the computing resources of each virtual machine in the vehicle according to the popularity of the task request flow to the vehicle edge server,so as to minimize the overall power consumption of the vehicle servers.The simulation results show that compared with the current edge server energy consumption optimization model,the adaptive energy consumption optimization model proposed in this paper can effectively reduce the overall power consumption of the servers,up to 5.74%,and the delay could be controlled within an acceptable range.Secondly,this paper proposes a self-adaptive power minimized vehicle clustering algorithm based on the fuzzy C-Means for mobile edge computing in vehicular communication.After that,the neighbor list of the vehicle is determined by calculating the angle between the vehicles,preventing the vehicle from erroneous clustering on the intersecting road.Then,the optimal clustering number which minimizes the total energy consumption of vehicles is obtained according to the power consumption optimal model,and the fuzzy C-means is used to effectively cluster the vehicle.Additionally,the cluster head is selected by using the vehicle moving direction,the weighted mobility value and the entropy as the indicators of the clustering algorithm.The simulation results show that the SAPMVC algorithm can reduce the power consumption of vehicles when meeting the requirements of delay and improve the average duration of clusters and cluster head.