Task Offloading Algorithm With AoI As Optimization Target In Vehicular Networks

With the gradual maturity of fifth generation(5G)mobile communication technology,the application scenarios in vehicular networks have become more abundant and the number of tasks and the density of requests for vehicles to receive and process messages is increasing.However,the limited computing resources of the vehicle can hardly meet the performance requirements of the requested tasks.In order to improve the processing speed of tasks,offloading the tasks requested by vehicles to roadside infrastructure with larger computing capacity for processing has become a hot topic.In this dissertation,by classifying vehicular tasks based on actual application scenarios and considering the dynamic changes of vehicle positions,vehicular task offloading is investigated based on V2I(Vehicle to Infrastructure)communications.Furthermore,AoI(Age of Information)is adopted as the performance criteria to evaluate the timeliness of the task processing results received by vehicles and different task offloading strategies are analyzed and compared.Moreover,the effect of some main factors,including the time when the offloading task reaches the base station,the type of task and the location status of the task vehicle on the AoI of overall vehicular network are analyzed during the task offloading process.On this basis,a comprehensive priority task response strategy is proposed to optimize the AoI of overall vehicular network.The simulation results shows that,compared with the FIFO(First In First Out)strategy,the proposed task offloading strategy has a maximum reduction of 15.44%,29.17% and 69.91% in the average AoI,peak AoI and task loss value,respectively.Finally,the concurrent task offloading are investigated.For the complexity of the concurrent task offloading,this dissertation carries out theoretical analysis and demonstration from some aspects such as the initial AoI of the vehicle execution instruction.On this basis,the traditional genetic algorithm is improved to optimize the AoI of concurrent task process by dynamically adjusted according to different characteristics of the concurrent tasks and adapted to the complex and variable concurrent task offloading process.Simulation results show that the proposed improved genetic algorithm based task offloading strategy can effectively reduce the overall AoI of the vehicular network,with a decrease of about 38.63%.At the same time,it also shows the good adaptability of the proposed strategy to the offloading process of complex and variable concurrent tasks.