Research On Task Offloading Strategy For Internet Of Vehicles Based On Mobile Edge Computing

In recent years,with the increase in the number of cars on the road and the advancement of technology,cars are also moving rapidly in the direction of intelligence.However,The Internet of Vehicles(IOV),where smart vehicles have a large number of mobile applications,poses a number of challenges for smart vehicles,namely the need for real-time information gathering and a large amount of computing resources.To address these challenges,Mobile Edge Computing(MEC)provides low-latency and low-overhead computing services for mobile applications and connected vehicle systems by deploying edge servers around users.MEC is well suited to meet the requirements of high quality services and solve the problems that arise in the real-time and accuracy of mobile application provisioning.However,when all users perform task offloading,the computing power of MEC servers will be under great pressure.Therefore,effective task offloading decisions and resource allocation schemes are crucial to improve the performance and reliability of the Telematics system.To address the above issues,the specific research and innovations of this paper are as follows:(1)In this paper,we design a task offloading collaborative computing framework that can offload tasks to be computed locally,to be computed on RSUs(RSU)configured with MEC servers,and to be computed in the cloud.And a task offloading priority matrix is introduced in it to determine the priority offloading order of computational tasks.In addition,the total cost of task offloading in terms of instantaneous latency and energy consumption is taken as the optimization objective and an improved bald eagle search optimization algorithm(IBES)is designed to implement task offloading.The designed IBES is to introduce Tent chaotic mapping,Levy Flight mechanism and adaptive weights into the condor search optimization algorithm to increase the initial population diversity,enhance the local search and global convergence.The simulation results show that the IBES algorithm has better offloading performance and lower system cost compared with PSO and Local algorithms.(2)In this paper,we propose a mobile edge computing task offloading strategy scheme with cooperative roadside parking as a task offloading platform by using roadside parking vehicles with idle computing resources.This scheme joins roadside parking as a task offloading platform and combines three task offloading platforms,local,RSU and cloud server,for computational task offloading processing.And a hybrid algorithm(A Hybrid Algorithm Based on Hill-climbing Algorithm And Genetic Algorithm,HHGA)is proposed to introduce the hillclimbing algorithm based on genetic algorithm to improve the problem that genetic algorithm is stuck in local optimum.Comparative analysis is performed in several aspects such as system overhead,time delay,and energy consumption,and the experimental results show that the scheme proposed in this paper can effectively reduce the total system cost compared with other offloading schemes.