智能黑名单:一种提高低密度网络加载中黑名单性能的解决方案

F. V. Trimpont, Gaston Bayot Katumba, V. Moeyaert, S. Bette
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引用次数: 0

摘要

G3-PLC正在成为智能计量应用中具有吸引力的通信标准。该标准包括不同的机制和协议,允许处理电力线信道传输性能差的问题。特别是,网络的所有节点都可以充当中继。2014年,ITU-T G.9903推荐的G3-PLC标准提出了LOADng协议来执行路由。黑名单是该协议中包含的一种机制,通过忽略它们来处理质量差的链接。然而,当节点密度过低时,黑名单机制会受到性能不足的影响。事实上,一些质量差的链接是唯一可能到达一些更远节点的链接,因此通过将它们列入黑名单,这些更远的节点就无法再到达了。这就是为什么必须开发一个解决方案来解决这个问题。我们的解决方案是智能黑名单机制,大大降低了将关键环节列入黑名单的概率。在这项工作中,我们重点研究了低密度下的黑名单问题,并比较了经典黑名单和智能黑名单的性能。为此,我们在Contiki/Cooja平台上开发了自己的模拟器,在该平台上我们实现了loading协议以及G3-PLC网络的不同功能。基于我们的工具,我们报告了使用经典黑名单机制、不使用黑名单机制和使用智能黑名单机制时不同节点密度和不同链路质量的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Smart blacklisting: A solution to increase performance of blacklisting in LOADng for low density networks
G3-PLC is emerging as an attractive communication standard for smart metering applications. This standard includes different mechanisms and protocols allowing to deal with the poor transmission performance of the power line channel. In particular, all nodes of the network can act as relay. In 2014, G3-PLC standard, the ITU-T G.9903 recommendation, proposed the LOADng protocol to perform the routing. Blacklisting is a mechanism included in this protocol that deals with bad quality links by ignoring them. However, the blacklisting mechanism suffers from a lack of performance when the density of nodes is too low. Indeed, some bad quality links are the only ones possible to reach some farther nodes so that by blacklisting them, these farther nodes cannot be reached anymore. This is why a solution had to be developed in order to solve this problem. Our solution is the smart blacklisting mechanism that highly reduces the probability of blacklisting a critical link. In this work, we highlight the problem of blacklisting in low densities and compare the performance of classical blacklisting and smart blacklisting. For that purpose, we developed our own simulator in the Contiki/Cooja platform in which we have implemented the LOADng protocol as well as the different features of G3-PLC networks. Based on our tool, we report the results we obtained for different node densities and for different link qualities with the classical blacklisting mechanism, without it and with the smart blacklisting mechanism.
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