VANET属性对网络性能的影响

A. Prasetijo, S. Alwakeel, Hesham Altwaijry
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引用次数: 2

摘要

在车载自组织网络上广播消息的简单泛洪机制会导致大量消息冗余、争用和碰撞,即广播风暴问题。大多数可用的广播风暴解决方案通常是基于根据给定的单一属性选择重播节点,例如:发送方到接收方的距离、节点密度、车辆的速度、移动方向或接收到的重复消息数量。由于任何给定属性的设置都可能对网络性能产生显著影响,因此对这些设置进行比较是一个重要的研究课题。本文通过对各种属性设置的比较,提出了一种简单有效的解决VANET广播风暴问题的方法,并基于多网络和节点属性相结合的方法选择了转播节点。据我们所知,大多数广播风暴问题的解决方案只使用单属性方案,而不考虑使用两个或多个属性进行广播抑制。本文建议的方案有两个原因:首先,它被认为是为特定网络应用程序的目标选择适当方法/属性的指南,例如,在特定网络密度下,以减少冗余和/或最小化消息延迟实现最大可达性。其次,它为未来的研究奠定了基础,即通过基于机器学习的方法使用多个属性来减轻广播风暴。该方案的仿真结果表明,适当的属性组合可以比最佳的单属性方案的性能更好。与最佳的基于距离的单一属性广播相比,同时实现距离和重复属性的组合广播方案显著减少了41%的消息冗余。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Effects of VANET's attributes on network performance
A simple flooding mechanism for broadcasting messages over vehicular ad-hoc networks causes massive message redundancy, contention and collision known as the broadcast storm problem. Most available solutions to broadcast storm are normally based on the selection of the rebroadcast nodes depending on a given single attribute such as: sender-to-receiver distance, node density, vehicle's speed, movement direction, or number of message duplicates received. As the settings of any given attributes may remarkably impact the network performance, comparisons of such settings are therefore, an important research topic. In this paper, we suggest a simple but effective solution to the VANET broadcast storm problem through comparison of various attribute settings and select the rebroadcasting nodes based on a combination of multi- network and node attributes. To the best of our knowledge, most of the solutions to the broadcast-storm problem make use of only single-attribute schemes and do not consider on employing two or more attributes for broadcast suppression. The scheme suggested throughout this paper is valuable for two reasons: First, it is considered as a guide to select proper methods/attributes for a specific network application's goal, e.g. maximum reachability with a reduced redundancy and/or with minimum message delay at specific network densities. Second, it sets a basis for future studies that employ multiple attributes on mitigating broadcast-storm through machine-learning-based approach. The simulation results of this proposed scheme show that a proper combination of attributes may outperform the performance of the best single-attribute-based schemes. A combined broadcast scheme which implements both distance and duplicate attributes provides a remarkable reduction on message redundancy by 41% compared to the best single distance-based attribute broadcast.
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