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Deep Learning in Edge of Vehicles: Exploring Trirelationship for Data Transmission

机译：车辆边缘的深度学习：探索数据传输的三关系

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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.
机译：当前，车辆具有自动相互通信的能力。对于车辆互联网（IoV），迫切需要减少延迟并提高车辆之间数据传输的吞吐量。通过探索车辆之间的三关系，本文提出了一种基于深度学习的传输策略。具体而言，我们考虑了在IoV边缘（即车辆边缘）的车辆的社会和自然属性。提取车辆的社会特征，通过构造三角形图案结构来获得具有密切关系的主要邻居，从而建立网络模型。此外，还可以根据车辆和设备的特性估计节点的连接概率，从而提出基于卷积神经网络的内容共享伙伴发现算法。最后，实验结果证明了我们的方法在各个方面的有效性，例如消息传递率，平均延迟和已连接设备的百分比。

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