Cluster Routing-Based Data Packet Backhaul Prediction Method in Vehicular Named Data Networking
Rui Hou, Shuo Zhou, Yong Zheng, Mianxiong Dong, Kaoru Ota, Deze Zeng, Jiangtao Luo, Maode Ma
Abstract
Vehicular named data networking (V-NDN) is a network architecture that combines named data networking (NDN) and vehicular ad hoc networks (VANETs). Due to the high-speed mobility of the on-board unit (OBU) in V-NDNs, topological changes may cause the problem of reverse path breaking for data packets, thus impacting the communication quality of service (QoS) among vehicles. To address this issue, a data packet backhaul prediction method (DBPM) based on cluster routing in the V-NDN is proposed in this paper. The DBPM uses GPS and a convex programming location algorithm (CPLA) at roadside units (RSUs) to obtain the positioning information of vehicle in the clusters, and uses two positioning data items to predict the location of the vehicle's future access point (AP) for the cluster by using the Kalman filtering model. Then, the DBPM forwards the returned data packets to the vehicle by the cluster. Simulation experiments are performed by using the simulators Simulation of Urban Mobility (SUMO) and VanetMobiSim. Results show that the proposed DBPM can effectively reduce the average delay and packet loss ratio in the vehicle-to-infrastructure (V2I) communication in urban scenes, thus enhancing the robustness of data transmission and effectively supporting the data communication's QoS of V-NDN.