Research On Computing Offloading Strategy In 5g Internet Of Vehicles Scenario Based On Edge Computing

With the development of emerging technologies such as artificial intelligence,big data and 5G communication technology,delay-sensitive in-vehicle applications such as automatic driving and automatic navigation have emerged one after another.energy consumption requirements.Mobile Cloud Computing(MCC)can alleviate the application’s demand for computing resources.However,due to the long deployment distance of cloud servers and the instability of the backhaul link,the communication delay of task offloading is likely to be too large,and cloud computing alone cannot.Meet the latency requirements of latency-sensitive tasks.Mobile Edge Computing(MEC)technology sinks computing resources to the edge of the network closer to the user equipment(UE),by deploying the MEC server to the base station(Base Station,BS)or wireless access point,which can provide a closer calculation for the vehicle terminal.The Internet of Vehicles(Io V)edge computing technology can give vehicle edge nodes powerful data processing capabilities and information transmission capabilities,and can provide more efficient and low-latency services.This paper studies the problem of computing task offloading strategy based on MEC in the Internet of Vehicles,mainly including the following two aspects:Firstly,“Vehicle-Edge-Cloud” collaborative computing offloading scheme based on low latency: in a system consisting of vehicles,edge servers,and cloud computing centers.Assuming that in the single-user Io V computing offloading scenario,the terminal vehicle has a computing-intensive and delay-sensitive task.According to the computing resources and network capacity of local and cloud computing nodes,a “vehicle-edge-cloud”collaborative computing offloading strategy based on an improved particle swarm algorithm is proposed to obtain the optimal offloading distribution coefficient.The simulation results show that the task computing delay based on the “vehicle-edge-cloud”offloading strategy is lower than other offloading strategies,which proves the superiority of the proposed strategy.Secondly,“Vehicle-Edge” collaborative computing offloading scheme that balances latency and energy consumption: in a vehicle networking network architecture consisting of vehicles,edge servers,and SDN(Software Defined Networks)controllers.For the computing offloading of multi-tasks in the Internet of Vehicles,the importance of tasks is firstly divided,and energy consumption and delay are weighed to optimize the total overhead cost.Using Q-learning reinforcement learning algorithm,Lagrangian multiplier method and gradient descent algorithm to find the best influence factor,so as to realize the optimization of delay and energy consumption.Compared with a single optimization objective,this offloading strategy can fully consider the delay and energy consumption to construct the objective function,saving overhead.