Research Of Game Theory's Application In Data Downloading On Vehicle Heterogeneous Networks Based On NDN

With rapid development of mobile Internet and research of Vehicular Ad Hoc Networks (VANETs), the function of VANETs has changed from initial support of traffic management and maintenance of traffic safety to provide convenient Internet services for vehicles. In this new trend, Named Data Networking (NDN), compared with traditional TCP/IP network, has better performance on mobility support, multi-network addressing and in-network caching. These advantages make VANETs and NDN integration to enhance Internet content distribution in vehicles become a state-of-the-art tendency. But under new network architecture, how to obtain original Internet data from roadside network in a complicated wireless heterogeneous network environment becomes a major obstacle to the combination of VANETs and NDN, which is major topic of this paper.Due to content-centric thinking, after integration of VANETs and NDN, heterogeneity of data caused by network heterogeneity no longer exists, so issue of data downloading is simplified to how vehicles choose roadside network and how vehicles utilize the advantages of NDN to download data from Internet. These two key steps are studied in this paper.In this paper, Steinberg game model is used to classify vehicles and networks into two roles: leader and follower. By quantifying bandwidth demand of vehicles, preference of roadside networks and pricing strategies of roadside networks, this paper proposes a new algorithm: Multi-End-User Multi-Networks Selective Model (MEUMNS), which can maximizes utility of vehicles and networks and find the best strategy of both parties.Simulation results show that MEUMNS can effectively guide two sides to adjust their strategies.After vehicles determining which networks to connect, according to characteristics of NDN, this paper proposes a priority-response incentive mechanism (PRIM) to encourage vehicles to download parts of the target data respectively and complete cooperative downloading by sharing in VANETs. In order to ensure successful completion of cooperative downloading and isolation of selfish vehicles, PRIM revised the core components and algorithms of NDN and analyzed participant's gains using repeated game theory. By finding Nash equilibrium, participants can get optimal strategy in cooperation. Simulation results obtained from network simulator NS-3 show that PRIM can reduce repetitive download of data and punish selfish vehicles with less delay under the premise of satisfying vehicles' data demand.