Efficient Content Delivery And Task Offloading Of Internet Of Vehicle Based On Edge Computing

As a novel paradigm,mobile edge computing is becoming an important promoter of the consumer-centric Internet of Things.It extends cloud computing functions and services to the edge of the network,and realizes real-time information transmission and calculation by transferring computing tasks from mobile devices to the edge of the network.It is widely used in scenarios that require real-time data processing and feedback,such as in-vehicle networks and autonomous driving.Currently,in-vehicle networks are facing the challenge of providing ubiquitous connectivity and high-quality services for many vehicles.In order to solve these problems,Mobile Edge Computing(MEC)will be used as the core starting point to access computing resources at the edge of the network in vehicle networks for task transmission and processing.The content of this article mainly consists of the following parts:The first of all,in view of the increasing pressure of base station wireless resource management in the mobile environment,when faced with the problem of efficient content delivery and task offloading in the Internet of Vehicles,this paper is based on MEC and considers the communication power consumption during the interaction between the user and the edge cloud in the MEC application scenario And power constraints,and then consider the issue of pricing limited computing resources.In detail,considering the limited resources of the edge cloud,a distributed method with communication power constraints based on price is proposed to manage users' offload computing tasks,and the interaction between the edge cloud and users is modeled using the Stackelberg game.The edge cloud sets prices based on limited computing power to maximize its revenue;for a given price,each user makes an offload decision locally to minimize their own costs.Simulation results show that the algorithm has better performance in optimizing system overhead while meeting task offload constraints.Then,For the task offload problem in the vehicle network,considering the multibase station application scenario,for the tasks that arrive at the ratio in the system,the power consumption cost of the task processing of the vehicle network system based on edge computing is taken as the optimization goal of task offload,based on the system model,Corresponding constraints and other control methods,an efficient task offloading algorithm is proposed to optimize the interaction of vehicle network content,that is,a task offloading algorithm with a caching mechanism based on queuing theory in the multi-base station MEC server scenario.For the tasks in the system,vehicle users and edge base stations make optimization decisions on offload ratio and cache ratio,minimize the total power consumption in the system,and compare and analyze with the other three algorithms.The system simulation results show that in multi-base station MEC servers In the scenario,when the vehicle enters the coverage area of the edge base station and executes the task offloading scheme,the performance of the task offloading scheme we proposed is superior to other schemes because the strategy is different from focusing only on the calculation offloading decision.It has also been effectively used,and the edge cloud's use of cache can effectively reduce the total power consumption of the system.