通讯作者：Kong, XJ (reprint author), Dalian Univ Technol, Sch Software, Software Engn, Dalian 116620, Peoples R China.
合写作者：Feng, Yufan,Collotta, Mario,Kong, Xiangjie,Wang, Xiaojie,Guo, Lei,Hu, Xiping,Hu, Bin
发表刊物：IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
关键字：Device-to-device communication; Mobile handsets; Data communication; Deep learning; Physical layer; Real-time systems; Trajectory; Data transmission; deep learning; device to device; edge of vehicles; triangle motif
摘要：Currently, vehicles have the abilities to communicate with each other autonomously. For Internet of Vehicles (IoV), it is urgent to reduce the latency and improve the throughput for data transmission among vehicles. This article proposes a deep learning based transmission strategy by exploring trirelationships among vehicles. Specifically, we consider both the social and physical attributes of vehicles at the edge of IoV, i.e., edge of vehicles. The social features of vehicles are extracted to establish the network model by constructing triangle motif structures to obtain primary neighbors with close relationships. Additionally, the connection probabilities of nodes based on the characteristics of vehicles and devices can be estimated, by which a content sharing partner discovery algorithm is proposed based on convolutional neural network. Finally, the experiment results demonstrate the efficiency of our method with respect to various aspects, such as message delivery ratio, average latency, and percentage of connected devices.