| With the large-scale construction of 5G wireless communication networks,C-V2 X has achieved rapid and diversified development,but large-scale computing services such as global road condition analysis have also brought huge transmission and processing pressure to Io V terminals.MEC provides computing power and storage capacity for Io V services at a location close to users,reducing the delay of data transmission and routing.Based on MEC,this paper studies the computing offloading strategy and anti-collision application of the Internet of Vehicles in the C-V2 X scenario.The main research contents are as follows:(1)Aiming at the problem of distributed unloading of multiple sub-tasks in Io V applications,the application is divided into multiple sub-tasks that are linearly related,and the application tasks are collaboratively completed through three strategies: local computing,MEC multi-hop backhaul,and V2V-assisted backhaul.In order to obtain the subtask offloading strategy with the lowest delay energy cost,a total cost model of delay energy consumption is established,and by improving the coding method and the elite selection strategy in the classical genetic algorithm,the genetic algorithm will not converge too fast and fall into local Optimal solution.On the basis of maintaining the inheritance of the optimal solution to the greatest extent,the subtask offloading strategy with the lowest total cost of delay and energy consumption is solved.The simulation results show that the algorithm can reduce the delay cost by 11%,the energy cost by 6%,and the total energy cost of delay by 7% compared with the offload strategy obtained by the traditional genetic algorithm in the offloading of Io V computing.(2)Aiming at the problems of pedestrians and non-motor vehicles on the road that are often not considered in the anti-collision application of the Internet of Vehicles,an anti-collision system based on MEC is established,and vulnerable users such as pedestrians are added to the anti-collision system.User-safe collision detection algorithm,which uses the basic safety information of vehicles and pedestrians on the road to predict trajectories,and sends collision warnings to users who are at risk of collision.Simulation results show that the proposed collision detection algorithm can always detect possible collisions and avoid them in time,with high reliability.At the same time,studies have shown that the performance of the MEC-based anti-collision system is better than the situation where the anti-collision application is deployed in the central cloud or locally.(3)On the basis of the proposed MEC-based anti-collision system,a simulation platform of the anti-collision system is built using the SUMO traffic simulator to simulate the real road and personnel conditions,and Python is used to simulate the communication process and run the collision detection algorithm.The experimental results further demonstrate the effectiveness of the proposed MEC-based collision avoidance system. |
