Research On Data Transmission Technology Of Vehicular Network Based On MEC

The 5th generation mobile networks and mobile edge computing are key technologies that can provide vehicular networks with the abilities to transmit massive data and to complete latency-sensitive tasks.In vehicular networks,the bandwidth and computing resource of mobile edge server are limited.Fortunately,with the contributions of vehicle clustering mechanism and mobile edge server,the amount of data transmission and task computation can be dramatically decreased.Vehicle clustering,task offloading as well as content caching and computing resource allocation of mobile edge server have all become hot topics in vehicular networks.With potential research on data transmission and task computing,this paper proposes a vehicle clustering mechanism based on content similarity and develops an efficient solution that jointly optimizes content caching,task offloading and dynamic computing resource allocation,to improve Network data transmission efficiency and system computing performance.Firstly,we introduce a vehicle clustering mechanism based on data content similarity.Considering that the sensor networks of vehicle generate a large amount of heterogeneous data,vehicular networks should transmit them in a tolerant way to decrease the probability of network congestion.Due to high vehicle mobility,vehicles will detach from the cluster which causes loss of information.Therefore,in order to improve the link quality of vehicular networks and cluster stability,a method of clustering vehicles uses their geographical locations and velocities.In order to maximize efficiency of data transmission,we propose a vehicle clustering mechanism based on content similarity.Based on machine learning,the features of the transmission data of the vehicles in the cluster are extracted,and vehicles are again clustered based on the similarity of the data.The cluster head eliminates redundant parts of the transmission contents and aggregates valid data,and then offloads data to mobile edge computing server.Then,to meet the requirement of constrained delay and computation resource,it is imperative to develop an algorithm that jointly optimizes content caching,task offloading and computing resource allocation.Due to the limitations of road structures and traffic scenarios,the transmission contents of the vehicular networks are regular and repetitive.Based on the characteristics of data,using the mobile edge computing server to store related computing contents can reduce the amount of data transmission and computing workloads.Since the caching capacity and computing resource of each MEC are limited,and the coverage areas of MECs are overlapped,the vehicular networks have to decide what contents to cache,how to offload tasks and how much computing resource needs to be allocated for each task.To jointly tackle these issues,we formulate caching strategy,offloading decision and computing resource allocation coordinately as a mix integer nonlinear programming problem.To simplify the mathematical model and obtain the optimal solution of this problem,we divide it into two sub-problems.Firstly,we formulate an efficient cache strategy based on content similarity.Then,Mc Cormick Envelops theory and improved Branch and Bound algorithm are used to solve the optimal offloading decision and resource allocation strategy.By jointly optimizing content caching,data offloading,and computing resource allocation,system latency is reduced and network capacity is increased.