Research On Task Computing Offloading Of Internet Of Vehicles Based On Mobile Edge Computin

With the rapid development of Internet of Vehicles(Io V)technology,the limited computing power of vehicles cannot meet the computing needs of large amounts of data and timeliness requirements,such as image processing,traffic analysis,and so on.Mobile edge computing(MEC),relying on its advantages of low latency and high bandwidth,has become a key technology to achieve efficient vehicle unloading computing tasks.The upload power of vehicle user tasks and resource allocation factors of edge servers can affect the effectiveness of task offloading methods in MEC systems.How to allocate power and resources,and design a computing offload method with low latency and low energy consumption are key issues that need to be addressed urgently in MEC systems.In order to solve this problem,this thesis takes the video analysis task in Io V as the background and studies two methods for computing and offloading vehicle networking tasks based on MEC system.The main achievements are as follows:(1)A method for computing and offloading tasks for vehicle networking based on task status is proposed.Different users have different latency and energy consumption requirements.Uploaded power allocation policies and edge server resource allocation policies have an impact on task calculation.To address these issues,we first proposed a network model based on task offloading for MEC systems.Next,we present a time model,a energy consumption model,and a privacy protection model for local and offload computing of video analysis tasks.On this basis,the unloading optimization problem is constructed with the task calculation cost function as the objective function.Then,the dichotomy method is used to solve the transmission power allocation strategy,and the convex optimization method is used to solve the resource allocation strategy.Finally,an algorithm that combines the offload strategy,transmission power allocation,and resource allocation is proposed to obtain the optimal solution of the original problem.Experiments on the MATLAB simulation platform demonstrate the effectiveness of this method.(2)A method for computing and unloading vehicle networking tasks based on matching models is proposed.MEC systems that support non orthogonal multiple access(NOMA)allow different users to simultaneously upload tasks on the same subchannel.However,differences in task upload delays and same channel interference among different users can affect each other.It leads to complexity in the way tasks are calculated.To solve this problem,we first describe a network communication model for task uploading for a pair of NOMA vehicle users,and provide a definition of the task computing optimization problem.Next,we derive the conditions for reducing the average overall calculation delay of vehicle users,and achieve optimal power allocation.Then,by analyzing the Karush-Kuhn-Tucker(KKT)condition,an optimal computing resource allocation strategy that takes the minimum task execution delay of the edge server as the target is obtained.Finally,a computational offload and resource allocation algorithm based on a matching model is proposed to iteratively obtain offload decisions and NOMA user pairing.Experimental results show that the method can converge to an approximate optimal solution and the system can achieve a smaller average overall delay.