VANET中可见光通信的高效聚类

IF 2.1 Q2 ENGINEERING, MULTIDISCIPLINARY Inventions Pub Date : 2023-06-29 DOI:10.3390/inventions8040083
Yu-Yen Chen, Pi-Chung Wang
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引用次数: 0

摘要

车辆自组织网络(VANET)的部署对自动驾驶汽车的发展至关重要。在VANET中,射频(RF)技术被用于在车辆和基础设施之间传输信息。然而,当车辆在高密度环境中传输信息时,有限的射频频段可能会造成干扰。此外,当许多车辆同时向基础设施传输消息时,同时传输可能会导致信道拥塞。可见光通信(VLC)技术可以解决信号干扰问题,而车辆聚类技术可以提高车辆的传输性能。VLC是一种新兴技术,具有抗电磁干扰的优点。车辆聚类技术将车辆分成不同的集合,每个集合有一个leader用于集群内消息传递。本文提出了一种基于VLC的VANET聚类算法。我们的算法根据车辆当前的运动来估计它们的位置。然后,根据相邻车辆的数量选择簇首,生成聚类。我们评估了我们的方案在城市和高速公路场景下的性能。仿真结果表明,该算法可以最大限度地减少集群数量,提高车辆的传输数据速率。
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Efficient Clustering of Visible Light Communications in VANET
The deployment of vehicular ad hoc network (VANET) is crucial to the development of autonomous vehicles. Radio frequency (RF) technology has been employed to transmit messages between vehicles and infrastructure in VANET. However, the limited RF bands may cause interference when vehicles transmit messages in a high-density environment. Moreover, when numerous vehicles transmit messages to the infrastructure at the same time, the simultaneous transmissions may cause channel congestion. While the issue of signal interference can be solved by the techniques of Visible Light Communication (VLC), vehicle clustering can be employed to improve the transmission performance of vehicles. VLC is an emerging technology with the advantage of immunity to electromagnetic interference. The technique of vehicle clustering categorizes vehicles into different sets, where each set has a leader for intra-cluster messaging. In this work, we present a clustering algorithm for VANET based on VLC. Our algorithm estimates the positions of vehicles based on their current movements. Then, it selects cluster heads based on the number of neighboring vehicles and generates clusters. We evaluate the performance of our scheme for both urban and highway scenarios. The simulation results show that the proposed algorithm can minimize the number of clusters and improve the transmission data rate for vehicles.
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来源期刊
Inventions
Inventions Engineering-Engineering (all)
CiteScore
4.80
自引率
11.80%
发文量
91
审稿时长
12 weeks
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