Resource Sharing And Cooperative Incentive Mechanism In Vehicular Networks

In recent years,road and traffic problems has become more and more important and the development of Intelligent Transportation System(ITS)aims at solving these related issues.Vehicular Networks which is one of the most important parts of ITS has been proposed.It needs information sharing among vehicles themselves to meet various content request.However,some characteristics of vehicular networks,such as mobility of vehicles,rapidly changing of network topology and unbalanced density of vehicles,result in a common short duration of communication between vehicles.Data links often disconnect suddenly instead of providing a reliable end-to-end connection to ensure complete data transfer process.So,timely and effective dissemination of data in the vehicular networks has become a very tricky task.One way is to access LTE to acquire data alone for all mobile nodes but obviously the cost is usually too high.Therefore,supposing each vehicle may have the demand of obtaining content,it is feasible that the data transmission be realized by share and cooperation between vehicles themselves.However,the cooperation between the highly autonomous nodes in vehicular networks does not take place for granted because of saving their own limited bandwidth.In fact,it often seems that non-cooperation is more reasonable.So,effective incentive mechanism is necessary to promote the cooperation.Incentive mechanisms based on "reputation evaluation" and “virtual credit” are often used.Some problems are still to solve like the trust of feedback in reputation-based mechanism and deficiency in stimulating vehicles to “try their best” not only "try" in credit-based only mechanism.According to the description above,this paper first blends the information-centric network with the traditional vehicular networks to handle some unique issues like short-lived connectivity,rapidly changing topology and proposes a content cooperatively sharing model based on multi-node.Then,we design the optimization scheme and incentive mechanism of cooperative vehicles selection to reply to possible selfish behaviors in the networks.Central server is responsible for collecting and transmitting the distributed feedback generated by vehicles in the network.The node could use the optimization scheme based on Dempster-Shafer Theory and K-Means which is a machine learning algorithm to evaluate the reliability of cooperative nodes in real time to find normal and unselfish vehicles as much as possible so as to enhance the efficiency of content transmission.At the same time,nodes are motivated through the request priority queue and virtual credit.In addition,we also propose a auxiliary mechanism called “two-way acknowledgement” to minimize the generation of unreal feedback from the source.Simulation results show that the optimization and incentive mechanism could select reliable cooperative nodes more more accurately and motivate more vehicles to participate in content sharing so as to improve the stability and throughout of vehicular networks.