Research On Data Forwarding And Caching Strategies For VANET Based On NDN

Vehicular Named Data Networking(V-NDN)is a Vehicular Ad-hoc Network(VANET)that uses the Named Data Networking(NDN)architecture,which has the disadvantages of inefficient packet routing and difficult data redundancy control.Therefore,this thesis optimizes the V-NDN network model for different data content and studies the data forwarding and caching issues.To solve the problem that vehicles in different connected regions communicate difficult,this thesis constructs a RSU(Road Side Unit)aided V-NDN network model,which uses the high-speed wired channel between RSUs to assist the forwarding of interest packets and data packets based on geographic location,thereby improving the data request success rate of V2V(vehicle-to-vehicle communication)and reducing the average delay of successful request data.In addition,aiming at the problem that RSU nodes have low interest packet hit rate and insufficient storage space in RSU aided V-NDN,a caching strategy for data packets based on decision tree is proposed.The RSU classifies and learns received data packets by using a decision tree algorithm,and caches hot spot data in the current local network to improve hit rate of interest packets,thereby achieving efficient use of storage space.Simulation analysis shows that RSU aided V-NDN has a 63.5%higher success rate than the traditional V-NDN,and it has a 59.3%lower average delay of the successful acquisition of data than the cellular aided V-NDN.For the problem of high transmission interruption and high delay when V-NDN provides streaming media services,this thesis establishes a V-NDN-based assisted download network model,and uses the same direction vehicles and opposite vehicles to assist target vehicle download data.When the target vehicle enters the first half of the blind zone,it uses the same direction vehicles to perform multi-hop communication to download data;when the target vehicle enters the second half of the blind zone,it downloads data from the opposite vehicles via V2V.For the selection of the assisting vehicle,this thesis models it as an optimization problem,aims at maximizing the network throughput on the premise of guaranteeing the user's playing experience,and proposes an sequential decision vehicle selection method based on delay tolerance(SSDT)to solve.For the waste of resources caused by frequent sending interest packets,this thesis uses an object-level data request method to improve the efficiency of data packet transmission.Simulation analysis shows that the download strategy of streaming media data proposed in this thesis is reduced by 10.4%in the average download time compared to the V-NDN assisted download strategy based on the SSRF(sequential decision vehicle selection method based on the residual file),and it is increased by 7.3%in the streaming media service quality.