基于归一化多维参数的蜂窝V2X亲和传播聚类

Koshimizu Takashi, Huan Wang, Zhenni Pan, Jiang Liu, S. Shimamoto
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引用次数: 5

摘要

本文介绍了一种新的车载自组织网络(VANET)聚类方案,名为“基于规范化多维参数的亲和传播聚类(NMDP-APC)”。NMPD-APC的相似函数由归一化的多维参数组成,以表示VANET的运动动力学。这种相似性函数是关联传播聚类(APC)方案中充分表征VANET流量动态同质性的关键。与以往针对VANET的APC方案的研究相比,我们的方案没有关联任何不稳定的未来预测项。我们还在本研究中应用了最近推出的蜂窝V2X无线电功能,这大大有助于减少通信延迟。在仿真中,将GHR (Gazis-Herman-Rothery)汽车跟随模型应用于实际采样的交通数据。仿真结果表明,在VANET聚类成功的同时,对归一化多维参数有显著的影响。仿真还证明了NMDP-APC方案在实际交通数据上的聚类所需的最小迭代次数和聚类粒度的可控性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Normalized multi-dimensional parameter based affinity propagation clustering for cellular V2X
This paper introduces a novel Vehicular Ad Hoc Networks (VANET) clustering scheme, titled “Normalized MultiDimensional Parameter based Affinity Propagation Clustering (NMDP-APC).” The similarity function of NMPD-APC consists of normalized multi-dimensional parameters in order to represent VANET's motion dynamics. This similarity function is the key in the Affinity Propagation Clustering (APC) scheme to adequately representing the homogeneity of traffic dynamics of VANET. In contrast to the previous research on APC scheme for VANET, our proposal does not associate any un-stable future prediction term. We also applied recently introduced Cellular V2X radio capability in this study, which significantly contributed to the reduction of communication latency. In conducting simulations, Gazis-Herman-Rothery (GHR) car following model was applied on the real sampled traffic data. The simulation resulted remarkable effects on the normalized multidimensional parameters along with successful VANET clustering. The simulation also demonstrated required minimum number of iterations for the clustering and controllability of clustering granularity in the proposed NMDP-APC scheme on the real traffic data.
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