Research On Method Of Collaborative Content Distribution For Internet Of Vehicles Based On Edge Computing Strategy

With the rapid development of Internet of Vehicles applications,more and more data are generated.How to effectively distribute content in Internet of vehicles to meet users’ service quality requirements has become one of the industry pain points in the field of intelligent vehicles and automatic driving.In order to solve this problem,Vehicle Edge Network is proposed.It integrates Mobile Edge Computing(MEC)into vehicle network and sinks computing and storage resources to the network edge near vehicle nodes,so as to provide more capabilities for executing resource intensive applications and reduce the communication delay of networked vehicles.However,due to the huge cost of deploying infrastructure such as roadside unit(RSU),the communication range of RSU,the scope of content distribution is limited,and the frequent link interruption caused by the rapid movement of vehicles,all of which seriously affect the efficiency of content distribution.This thesis studies the method of collaborative content distribution for Internet of Vehicles based on edge computing strategy to improve the overall performance of delay sensitive content and popular content distribution(PCD,a non secure information distribution service).The main work and innovations of this paper are as follows:(1)For popular content distribution scenarios,we propose a cooperative content distribution strategy for Internet of Vehicles based on fuzzy logic and alliance game.Firstly,fuzzy logic is used to calculate the ability of vehicles within RSU communication range as relay vehicles,and the proportion of relay vehicles is determined according to the density of vehicles;Then the road is segmented,and the alliance graph cooperative game algorithm is executed in each road section in parallel,so that the V2 V communication between vehicles forms an optimal network link structure in the current time slice,so as to distribute the content.Experimental simulation results show that the proposed strategy has good performance in reducing delay and energy consumption and expanding the scope of content distribution.(2)In order to better reduce the delay of content distribution and reduce the communication cost,we propose a content distribution strategy for Internet of Vehicles based on edge cache and artificial immune algorithm.Firstly,according to the historical request data of vehicle nodes within its coverage,RSU predicts the content popularity in the next time through forward neural network,and caches some content with high popularity from BS to local,so as to further reduce the content request delay and improve the hit rate;Then,in the content distribution stage,the requesting node can make different choices to maximize the system utility,It is modeled as an optimization problem,and propose an artificial immune cloning algorithm to obtain the optimal solution.The experimental results show that the proposed strategy not only improves the hit rate,but also effectively reduces the delay and network energy consumption caused by content distribution,and better meets the needs of vehicle user nodes.