CASCADE: Cluster-Based Accurate Syntactic Compression of Aggregated Data in VANETs

Khaled Ibrahim, Michele C. Weigle
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引用次数: 109

Abstract

We present a method for accurate aggregation of highway traffic information in vehicular ad hoc networks (VANETs). Highway congestion notification applications need to disseminate information about traffic conditions to distant vehicles. In dense traffic, aggregation is needed to allow a single frame to carry information about a large number of vehicles. Our technique, CASCADE, uses compression to provide aggregation without losing accuracy. We show that CASCADE makes efficient use of the wireless channel while providing each vehicle with data that is highly accurate, represents a large area in front of the vehicle, and can be combined with aggregated data from other vehicles to further extend the covered area.
级联:基于聚类的VANETs中聚合数据的精确语法压缩
提出了一种车辆自组织网络(VANETs)中高速公路交通信息的精确聚合方法。公路拥堵通知应用程序需要向远处的车辆传播有关交通状况的信息。在密集的交通中,需要聚合以允许单个帧携带大量车辆的信息。我们的技术,CASCADE,使用压缩来提供聚合而不损失准确性。我们的研究表明,CASCADE有效地利用了无线信道,同时为每辆车提供高度准确的数据,这些数据代表了车辆前方的大片区域,并且可以与其他车辆的汇总数据相结合,进一步扩大覆盖区域。
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