Cooperative Edge Caching Study Based On Content Relevance In The Vehicular Network

Recenty,with the development of the mobile Internet and the Internet of Things,the amount of network data shows an exponential growth trend.The commercialization of5 G technology has provided a good access and communication foundation for the above scenarios.However,the 5G end-to-cloud architecture cannot solve the long delay and backhaul bandwidth limitation of mobile devices and the cloud.The application of Internet of Vehicles is developing from the early stage of security alarms to the coexistence of security alarms and entertainment services,which puts higher requirements on the delay and backhaul link of Internet of Vehicles.Therefore,reducing the latency of requests and improving the utilization of backhaul links have become one of the key issues in the research of Internet of Vehicles.Proactive edge caching is a technology that uses user context information to make predictions at the edge of the network to provide services to user requests.This article studies proactive edge caching applications in the Internet of Vehicles.Based on the prediction of vehicle request probability,this article studies the Road Side Unit(RSU)and proactive edge buffering of vehicles in the Internet of Vehicles,with the aim of reducing request latency and improving the utilization of backhaul links.The specific work is summarized as follows:(1)The item-based collaborative filtering algorithm is optimized and applied to the content request probability prediction of the Internet of Vehicles.A request prediction method based on content relevance is proposed.By analyzing the time,location,and preference in the vehicle historical request sequence,the potential relevance between the content is obtained,and the future request probability of the vehicle is predicted in conjunction with the content popularity.This method aims to improve the accuracy of request prediction,and lays a foundation for the design of subsequent cache strategies.(2)Based on the prediction method,this paper studies the proactive caching on the RSU node requested by the vehicle V2 I link,and designs a V2 I collaborative caching algorithm based on content relevance.By analyzing the movement model of the vehicle relative to the RSU,with the goal of reducing the average backhaul link traffic of the system,this paper proposes a cache decision algorithm with low time complexity.Simulation proves that the proposed algorithm can effectively improve the backhaul link utilization,reduce request delay and improve service efficiency compared with the traditional popularity algorithm.(3)Based on the prediction method and the V2 I collaborative cache,considering the willingness of idle vehicles to provide content delivery services for the requesting vehicle,we study the proactive cache on the vehicle node requested by the vehicle V2 V link and design a V2 X collaborative caching algorithms.Fully considering the vehicle-tovehicle contact model and content request model,this paper proposes a cache decision algorithm that can obtain the suboptimal solution of the frac12 approximation rate.Simulation proves that the proposed algorithm has better cache performance than traditional algorithms and V2 I cooperative cache algorithms,which can fully reduce backhaul link traffic,request delay,and improve service efficiency.