Research On Resource Optimization And Data Sharing In Vehicular Edge Computing

Vehicular edge computing is emerged as new vehicular applications,e.g.,self-driving and augmented reality,have critical requirements for computing capabilities at the network edge of Internet of vehicles.By intergrating mobile edge computing(MEC)and fog computing into Internet of vehicles,vehicular edge computing aims to enhance computing environement around vehicular users.Localized data processing is achieved for realizing mobility support,reducing service delay and bandwidth consumption,and finally improving quality-of-service.In this dissertation,the concept and system model of vehicular edge computing are proposed with details.Based on a self-developped testbed,four kinds of typical application scenarios enabled by vehicular edge computing are studied,namely,computation offloading,parked vehicular collaboration,vehicular crowd sensing,and data sharing.Furthermore,this dissertation pays attention to discuss,analyse and research key performance optimization problems for the above application scenarios The following are the crucial contribution of this dissertation1.The method of resource allocation based on reputation is studied for computation offloading scenario.As proposed,MEC servers consider both task requirements and reputation values of served vehicles to allocate limited computation resources for them By analyzing different reqirements of various computation tasks,the optimization problem about resource allocation with the consideration of reputation is sloved by using baragaining game theory2.The collaborative computing problem is discussed for parked vehicular collaboration scenario.To cope with the overload problem,a part of computation tasks can be offloaded from VEC server to parked vehicles for collaborative task execution.To stimulate parked vehicles to participate in collaborative computing,a contract theoretic approach is adopted to design optimal collaborative computing strategy for the service provider,which maximizes the revenue of the service provider while enhancing the utilities of the parked vehicles.3.The vehicular crowd sensing scheme assited by bus is discussed for crowd sensing scenario.In the scheme,a data provider manipulates fog computing servers to schedule specified buses for collecting urban data,and send pre-processed data to a data user for earning benefits.Greedy algorithm and Stackelberge game theory are proposed for optimizing crowd sensing data allocation and crowd sensing data trading,respectively.4.The data storage consortium blockchain(DSCB)is designed for secure data storage and sharing in data sharing scenario.Both the main system components and operational mechanisms are introduced for supporting the DSCB.In addition,security analysis is provided for demonstrating the superiority of the DSCB.