LAUSR

Due to the complicated and changing urban traffic conditions and the dynamic mobility of vehicles, the network topology can rapidly change which causes the communication links between vehicles disconnected frequently, and further affects the performance of vehicular networking. To overcome this problem, we propose a intelligent multi-attribute routing scheme (MARS) for two-layered software-defined vehicle networks (SDVNs). The proposed scheme is divided into two phases, the routing path calculation and the multi-attribute vehicle autonomous routing decision-making. In this paper, we construct the topology diagram in SDVNs for finding the efficient routing paths. To increase the packet arrival rate and reduce the end-to-end delay, an intelligent multi-attribute routing scheme is proposed by employing fuzzy logic and design a technique of order preference by similarity to ideal solution (TOPSIS) algorithm to find the next-hop forwarder. To solve the uncertainty problem of multiple attributes, we apply the fuzzy logic to identify the weight of each attribute in TOPSIS algorithm. Simulation results demonstrate that MARS can effectively improve packet delivery ratio and reduce average end-to-end delay in urban environments compared with its counterparts.