如何处理数据饥渴的V2X应用程序?

A. Bazzi, C. Campolo, B. Masini, A. Molinaro
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引用次数: 2

摘要

目前的车载通信技术是为所谓的第一阶段设计的,在这个阶段,汽车需要通知他们的存在。若干项目、研究活动和实地试验证明了它们在这一范围内的有效性。但进入第二阶段,需要提高对非互联物体和弱势道路使用者的感知,进入第三和第四阶段,频谱稀缺将成为一个关键问题,因为第三和第四阶段也需要协调。在这项工作中,我们概述了目前正在研究的各种5G及其他解决方案,这些解决方案将需要应对挑战。我们首先回顾访问层正在进行的旨在满足容量和带宽需求的活动。然后,我们讨论了新兴的网络模式在改善车辆数据传播,同时防止拥堵和更好地利用资源方面所起的作用。最后,我们将介绍边缘计算和机器学习技术,这将是有效处理和挖掘大量传感器数据的决定性因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
How to deal with data hungry V2X applications?
Current vehicular communication technologies were designed for a so-called phase 1, where cars needed to advise of their presence. Several projects, research activities and field tests have proved their effectiveness to this scope. But entering the phase 2, where awareness needs to be improved with non-connected objects and vulnerable road users, and even more with phases 3 and 4, where also coordination is foreseen, the spectrum scarcity becomes a critical issue. In this work, we provide an overview of various 5G and beyond solutions currently under investigation that will be needed to tackle the challenge. We first recall the undergoing activities at the access layer aimed to satisfy capacity and bandwidth demands. We then discuss the role that emerging networking paradigms can play to improve vehicular data dissemination, while preventing congestion and better exploiting resources. Finally, we give a look into edge computing and machine learning techniques that will be determinant to efficiently process and mine the massive amounts of sensor data.
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