Know Thy Neighbor - A Data-Driven Approach to Neighborhood Estimation in VANETs

Karsten Roscher, Thomas Nitsche, R. Knorr
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引用次数: 4

Abstract

Current advances in vehicular ad-hoc networks (VANETs) point out the importance of multi-hop message dissemination. For this type of communication, the selection of neighboring nodes with stable links is vital. In this work, we address the neighbor selection problem with a data-driven approach. To this aim, we apply machine learning techniques to a massive data-set of ETSI ITS message exchange samples, obtained from simulated traffic in the highly detailed Luxembourg SUMO Traffic (LuST) Scenario. As a result, we present classification methods that increase neighbor selection accuracy by up to 43% compared to the state of the art.
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了解你的邻居——一种数据驱动的邻域估计方法
当前车载自组织网络(vanet)的发展表明了多跳消息分发的重要性。对于这种类型的通信,选择具有稳定链路的相邻节点至关重要。在这项工作中,我们用数据驱动的方法解决了邻居选择问题。为此,我们将机器学习技术应用于ETSI ITS消息交换样本的大量数据集,这些数据集来自非常详细的卢森堡相扑交通(LuST)场景中的模拟交通。因此,我们提出的分类方法与现有方法相比,将邻居选择精度提高了43%。
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