Research On Data Scheduling Algorithm In Internet Of Vehicles

The Internet of Vehicles is a high-speed mobility network,which has the characteristics of fast and convenient,shortens the space distance,and is conducive to travel,and promotes the development of autonomous driving and intelligent driving.More and more auto companies and Internet companies combine their existing resources to make cars more intelligent,including the optimal driving trajectory of the vehicle,so that the motion state of the car is orderly and controllable.However,with the increase of automobile data and its high-speed mobility characteristics,data transmission between vehicles becomes more and more difficult.Therefore,how to make full use of communication resources to ensure the user’s communication experience and realize the security of data transmission in a high-speed mobile environment is a research problem that is concerned by both academia and industry.Data scheduling algorithm is a data communication technology that processes vehicular data and sends it to other vehicles or devices.However,the traditional data scheduling algorithm needs to consume too many communication resources,which is only suitable for the traffic area with more perfect infrastructure.In actual Internet of Vehicles scenarios,the deployed infrastructure is limited for most traffic environments.Some scholars have proposed V2 V and V2 I collaborative communication technologies to solve this problem.However,these algorithms are difficult to meet the requirements of reliability,efficiency,and security of communication between vehicles at the same time.Therefore,how to make full use of communication resources in the high-speed mobile Internet of Vehicles environment,so that vehicles can quickly process data and obtain results,while ensuring the efficiency,reliability and security of data transmission between vehicles,is a very important question in the Internet of Vehicles.This thesis studies the data scheduling algorithm in the Internet of Vehicles environment.First,this thesis proposes a clustering cooperative scheduling algorithm based on reinforcement learning to improve the reliability of communication by combining the communication strategies of V2 V and V2 I.Secondly,traditional communication algorithms based on opportunistic scheduling are easy to waste communication resources.To make full use of communication resources,this thesis proposes a robust data scheduling algorithm with data transmission stability in combination with the specific needs of Internet of Vehicles communication.Then,to reduce the time for vehicles to perform key tasks,and at the same time make the information of completing key tasks more secure,the thesis proposes an incentive mechanism-oriented data scheduling algorithm,which enables vehicles to participate in key tasks and uses data interference to make mission-critical information more secure.Finally,to improve the efficiency and security of data transmission,the data scheduling algorithm that supports trajectory privacy protection is studied to protect the trajectory privacy of vehicles,make data transmission more efficient by cooperative key task distribution,and use trajectory privacy protection to make data transmission between vehicles more secure.The specific research work of this thesis is as follows:(1)Research on cooperative scheduling algorithm based on reinforcement learningAs the number of connected vehicles and their motion uncertainties increases,it poses a great challenge to transmit data reliably and efficiently between vehicles.At the same time,the existing work usually uses routing strategies and cooperative scheduling to improve the transmission efficiency,but it is difficult to ensure the reliability of data transmission when the information is transmitted to the destination vehicle.Therefore,to make the communication between vehicles have higher efficiency and reliability,this thesis proposes a clustering cooperative scheduling algorithm based on reinforcement learning.The algorithm utilizes stability,distance,and bandwidth efficiency to select cluster head vehicles,reducing the uncertainty of vehicle motion for transmitting data,thereby improving data transmission efficiency.On this basis,the algorithm proposed in this thesis further makes the communication between vehicles more reliable through the auxiliary transmission based on reinforcement learning.Experiments show that the performance of the scheduling algorithm is better than the existing algorithms.(2)Research on robust data scheduling algorithm based on stability and fairnessStable and fair data transmission in the Internet of Vehicles can improve traffic efficiency and reduce traffic accidents.However,it is very challenging to ensure the stability and fairness of data transmission between vehicles at the same time.Therefore,this thesis proposes a robust data scheduling algorithm based on stability and fairness,while ensuring the stability and fairness of data transmission between vehicles.The algorithm first uses the back pressure vector to judge the stability of the average rate,which makes the data transmission between vehicles more stable.At the same time,considering the factors of speed,distance and movement trend,it realizes the fair distribution of channel resources in a distributed computing manner,and finally achieves the purpose of ensuring the stability and fairness of data transmission at the same time.Experiments show that the algorithm outperforms other existing algorithms.(3)Research on incentive mechanism-oriented privacy-preserving data scheduling algorithmIn the Internet of Vehicles,the introduction of existing lightweight encryption schemes improves the reliability and security of data transmission,but the selfishness of vehicles makes it difficult to comply with this lightweight encryption scheme.Moreover,the high-speed mobility of vehicles also poses challenges.Therefore,the thesis first proposes a multi-objective-oriented multi-dimensional incentive mechanism to enable vehicles to participate in the execution of tasks;secondly,the thesis uses the method of key task identification to improve the efficiency of completing key tasks;finally,to ensure the security of data,the proposed algorithm enables the secure transmission of mission-critical information between vehicles by means of data interference.Experiments show that the proposed algorithm outperforms traditional methods in both the number of participants and the completion of key tasks.(4)Research on data scheduling algorithm with trajectory privacy protectionIn the Internet of Vehicles,the existing work performs data processing through opportunistic computing and scheduling,which makes data communication more efficient.However,due to the existence of malicious nodes,the communication between vehicles faces the problem of data security.Therefore,considering the efficient data transmission and trajectory privacy of vehicles,this thesis proposes a data scheduling algorithm based on trajectory privacy protection.The algorithm utilizes edge computing to distribute key tasks in a cooperative manner to ensure the efficiency of completing key tasks.At the same time,the vehicle’s trajectory information is protected by the average distance deviation and pseudonym entropy,which makes the communication between vehicles more secure.Experiments show that the proposed algorithm outperforms other existing algorithms.