V2X Offloading And Resource Allocation In Internet Of Vehicles With MEC

With the rapid development of the automotive industry and the Internet of Things technology,the rapid growth of in-vehicle applications has brought challenges to limited vehicle computing resources.The continuous update of on-board equipment allows vehicles to communicate with surrounding service nodes for Vehicle-to-Everything(V2X)communication.Therefore,offloading tasks to cloud servers or neighboring vehicles for calculation can effectively expand the computing capacity of the vehicular networks.However,the remote deployment of cloud servers is prone to cause delay jitter,which cannot meet the requirements of "low latency and high reliability" of vehicular networks.Mobile Edge Computing(MEC)sinks cloud services to the edge of wireless networks and provides computing services close to users,thereby making up for the delay fluctuations caused by remote cloud computing and effectively improving user service quality.However,due to the limited computing capacity of the MEC servers and the vehicles,the massive amount of computing data has brought heavy pressure on the scarce network resources,which seriously affects the vehicle task computing cost.Therefore,this thesis studies the problem of how to properly select the offloading node and how to reasonably allocate the communication,computing,and caching resources in the network when the task is offloaded in the MEC-based vehicular networks.The main contents of this thesis are summarized as follows:1.To address the serious problem of delay and energy consumption increase and service quality degradation caused by complex network status and huge amounts of computing data in the scenario of vehicle-to-everything(V2X),a vehicular network architecture combining mobile edge computing(MEC)and software defined network(SDN)was constructed.In order to further reduce system overhead,a joint task offloading and resource allocation mechanism is proposed.By modeling the MEC-based V2 X offloading and resource allocation,the reasonable offloading decision,communication and computing resource allocation scheme were derived.Considering the NP-hard attribute of the problem,Agglomerative Clustering was used to select the initial offloading node,and Q-learning was used for resource allocation.The offloading decision was modeled as an exact potential game,and the existence of Nash equilibrium was proved by the potential function structure.The simulation results show that,as compared to other mechanisms,this mechanism can effectively reduce the system overhead.2.Along with vehicular networks being content-centric,the demand for multimedia services has grown exponentially.A large amount of data exchange has bought a heavy burden on the mobile networks.Therefore,a V2 X collaborative caching and resource allocation framework for vehicular networks based on mobile edge computing is constructed.MEC servers and neighboring vehicles are used to enhance edge cache capabilities to reduce repeated transmission and improve resource utilization.In order to further reduce computing overhead,by designing a V2 X cooperative caching and resource allocation mechanism,the effective allocation of computing resources,communication resources,and caching resources in the network is achieved.According to the multi-objective optimization model,the graph coloring model is used to allocate appropriate channels to the offloading users to reduce channel interference according to the demand of the requesting vehicle for the transmission rate.Aiming at the goal of minimizing system overhead,Lagrange multiplier method is used to allocate power and computing resources.The simulation results show that the proposed mechanism can effectively reduce system overhead and reduce task completion delay under different system parameters.