WMCD: A Situation Aware Multicast Congestion Detection Scheme Using Support Vector Machines in MANETs

Xiaoming Liu, H. Nyongesa, James Connan
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引用次数: 2

Abstract

Congestion is one of the most important issues impeding the development and deployment of IP multicast and multicast application in Mobile ad-hoc network (MANETs). In this paper, we propose a situation aware multicast congestion detection scheme with support vector machines in MANETs. We focus on using support vector machines to detect incipient multicast congestion by using structural situation information. In this way, by using a situation aware learning system, we can detect incipient congestion in advance instead of waiting packet loss. The rate adaptation algorithm can reduce the transmission rate only if the loss is classified as a congestion loss. Simulation results show that a support vector machine is an appropriate mechanism for decision making in proactive multicast congestion detection.
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基于支持向量机的动态多播拥塞检测方案
拥塞是阻碍IP组播及其在移动自组网(manet)中应用的发展和部署的主要问题之一。在本文中,我们提出了一种基于支持向量机的情景感知多播拥塞检测方案。重点研究了利用结构状态信息,利用支持向量机检测初发的组播拥塞。通过这种方式,我们可以使用情境感知学习系统来提前检测早期拥塞,而不是等待丢包。速率自适应算法只有在被归为拥塞损耗的情况下才能降低传输速率。仿真结果表明,支持向量机是一种适合于主动组播拥塞检测的决策机制。
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