基于模糊逻辑的智能体选择在VANETs故障管理中的应用

Yang Liu, Zheng Xue, G. Han, Chang Liu, Canliang Liao
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引用次数: 0

摘要

在vanet中,由于软件或硬件故障,故障节点可能会吸收网络流量并丢弃所有数据包。在这种情况下,故障管理(即识别故障节点)起着至关重要的作用。该任务由网络中选定的车辆执行,这些车辆被称为“代理”。代理选择不当会严重降低最终故障节点检测的性能。对于分布式方案,由于拓扑结构的快速变化,VANETs的代理选择具有挑战性。提出了一种基于模糊逻辑的智能体选择方法。该方法综合考虑了候选车辆的速度、领导和路线。当选择合适的代理时,我们在其上部署基于神经网络的故障节点检测算法。通过数值模拟验证了所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fuzzy Logic Based Agent Selection for Failure Management in VANETs
In VANETs, due to software or hardware mal-function, a failure node may absorb the network traffic and drop all packets. Failure management (i.e. identifying the failure node), plays a vital role in this situation. This task is performed by selected vehicles in the network, which are called 'agents'. Improper choice of agents can degrade the final failure node detection performance severely. For distributed schemes, agent selection for VANETs is challenging because of quick topology changes. In this paper, a fuzzy logic based agent selection method is proposed. This method comprehensively considers velocity, leadership, and routes of candidate vehicles. When the appropriate agent is selected, we deploy a neural network based failure node detection algorithm on it. We use numerical simulations to validate the effectiveness of the proposed method.
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