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Evaluation of the Effect of Variations in Vehicle Velocity and Channel Bandwidth on an Image-Streaming System in Vehicular Networks

机译:评估车辆速度和信道带宽在车辆网络中的图像流系统中的效果

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In this paper, we propose a solution for implementing a real-time image-streaming system for vehicle networks. Our proposed system organizes each vehicle as a local Internet of Things network. In each network, nodes are connected to cameras to capture images of the surrounding environment of the road. Then, the captured data are published to a streaming server via a 4G internet connection. In order to adapt to the change in bandwidth channel and vehicle speed, we propose algorithms to control the quality of image capture and the number of images taken. Our algorithms are based on a prediction method of the available bandwidth of the channel to control the rate of sending data from each local node in subsequent transmissions. Also in this paper, we present the relationships between channel bandwidth, vehicle velocity, and image quality. The experimental results and our simulation show that the proposed system significantly reduces end-to-end delay when the number of nodes increases. This offers a capability for high-quality image-streaming applications over vehicle networks. The system is also successfully implemented in a real-world application, and the results show that the collected images are of high quality.
机译:在本文中,我们提出了一种用于实现车辆网络的实时图像流系统的解决方案。我们所提出的系统将每个车辆组织为当地的内容网络。在每个网络中,节点连接到摄像机以捕获道路周围环境的图像。然后,捕获的数据通过4G Internet连接发布到流式服务器。为了适应带宽信道和车速的变化,我们提出了控制图像捕获质量和拍摄的图像数量的算法。我们的算法基于通道的可用带宽的预测方法,以控制从后续传输中从每个本地节点发送数据的速率。同样在本文中,我们介绍了信道带宽,车辆速度和图像质量之间的关系。实验结果和我们的仿真表明,当节点数量增加时,所提出的系统显着降低了端到端延迟。这为车辆网络提供了高质量的图像流应用的能力。该系统也成功地在真实应用程序中实现,结果表明收集的图像具有高质量。

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