Edge Resource Allocation Optimization Method In The Internet Of Vehicles

Today,most applications are using cloud computing technology to protect their data and dynamically provision and configure shared resources.However,the rapid development of mobile terminals has generated a large amount of data and huge resource service requirements.For latency-sensitive applications and users,the cloud computing service model is difficult to meet the needs of mobile support,low latency and geographical distribution,and location awareness,which leads to a decline in user satisfaction with the service.In response,the concept and application of mobile edge computing has begun to enter the horizons of researchers and data operators.This new paradigm can extend services in the cloud to communicate and manage resources locally,enabling low latency and supporting mobility.The rapid development of the Internet of Things has led to an increasingly urgent need for resource management,so mobile edge computing is seen as a key technology to solve this problem.How to meet user application requirements and ensure the efficiency of resource services has become the focus of mobile edge computing research.Therefore,the paper chooses resource management as the research topic,combining mobile edge computing and Internet of Vehicles,and the car as a mobile terminal applying for data resources to study resource management methods.Considering the characteristics of high mobility of vehicles and multiple QoS attributes of users,a resource management framework based on Stackelberg game and based on A resource management method for multiple RSUs in a vehicle ad hoc network in conjunction with V2 V.The details are as follows.1.We propose a specific vehicle edge resource management framework combining MEC and connected cars,which is composed of fog nodes,data service agents and cars.A dynamic service area division algorithm is designed to balance the load of DSA and improve service quality.A resource allocation framework based on the Stackelberg game model is proposed.The distributed iterative algorithm is used to analyze the pricing problem of FN and the data resource strategy of DSA.Simulation results show that the proposed framework can ensure the efficiency of FN resource allocation between cars and reduce service delay.This framework achieves the best strategies of participants and perfect Nash game equilibrium.2.A multi-rsu collaborative V2 V resource management scheme based on vehicle AD hoc network is proposed.An efficient clustering algorithm(MPCA)based on mobility prediction is proposed.The basic idea of MPCA is to first divide the whole region into different regions using the region partitioning algorithm,so that each car can predict its own life and the cost of becoming the cluster head of the current region.MPCA introduces a new combination metric called Vehicle Lifetime Value to describe the impact of vehicles on cluster stability and cost.The proposed MPCA algorithm selects the vehicle with the largest Vehicle Lifetime Value as the cluster head in the current area and analyzes the parameter set to improve the overall performance of MPCA.In addition,the performance of MPCA is also evaluated through simulation and compared with the two existing VANET clustering schemes.The simulation results are in good agreement with the analysis results,and it is proved that MPCA can significantly improve the stability of clustering architecture and reduce the delay of data service with higher prediction accuracy.