arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2009.01634cs.NI
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互联车辆中的信息传播

Message Dissemination in Connected Vehicles

Anirudh Paranjothi, Mohammed Atiquzzaman, Mohammad S. Khan

摘要:

近年来,基于车辆自组织网络(VANESTs)的连接车辆的进展在以有效方式传播消息方面在智能交通系统(ITS)中获得了显著关注。VANET使用专用短程通信(DSRC)在车辆之间和基础设施之间传播消息。虽然基于DSRC的通信是可行的,但当车辆不在彼此的传输范围内时,及时传播消息仍然具有挑战性。此外,当道路上的车辆密度增加时,DSRC通信信道严重拥塞。为了解决这些限制,采用两种新兴范例:1)车辆云计算和2)车辆雾计算以在连接的车辆环境中在车辆之间传播消息。车辆雾计算使用雾节点在车辆之间传播消息。任何真实世界的对象都可以通过获取1)网络连接性,2)计算性和3)存储性等属性来形成雾节点。在本书的这一章中,我们强调了基于DSRC、车载云计算和车载雾计算等技术的互联车辆中的消息传播的重要性。我们的目标是帮助读者更好地了解互联车辆和通信技术的基本原理,同时在车辆之间和基础设施之间传播信息。

英文摘要:

Advances in connected vehicles based on Vehicular Ad-hoc Networks (VANETs) in recent years have gained significant attention in Intelligent Transport Systems (ITS) in terms of disseminating messages in an efficient manner. VANET uses Dedicated Short Range Communication (DSRC) for disseminating messages between vehicles and between infrastructures. Though DSRC based communications are viable, it is still challenging to disseminate messages in a timely manner when vehicles are not in the transmission range of each other. Furthermore, DSRC communication channels are heavily congested when the vehicle density increases on the road. To address these limitations, two emerging paradigms: 1) vehicular cloud computing and 2) vehicular fog computing are been adopted to disseminate message between the vehicles in a connected vehicular environment. Vehicular fog computing uses fog nodes for the dissemination of messages among vehicles. Any real-world object can be formed as a fog node by acquiring the properties such as 1) network connectivity, 2) computation, and 3) storage. In this book chapter, we highlight the significance of message dissemination in connected vehicles based on techniques like DSRC, vehicular cloud computing, and vehicular fog computing. Our objective is to help the readers better understand the fundamentals of connected vehicles and communication techniques while disseminating messages between vehicles and between infrastructures.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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