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Fuzzy Logic and Regression Approaches for Adaptive Sampling of Multimedia Traffic in Wireless Computer Networks ?

机译:无线计算机网络多媒体流量自适应采样的模糊逻辑和回归方法。

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Organisations such as hospitals and the public are increasingly relying on large computer networks to access information and to communicate multimedia-type data. To assess the effectiveness of these networks, the traffic parameters need to be analysed. Due to the quantity of the data packets, examining each packet’s transmission parameters is not practical, especially in real time. Sampling techniques allow a subset of packets that accurately represents the original traffic to be examined and they are thus important in evaluating the performance of multimedia networks. In this study, an adaptive sampling technique based on regression and a fuzzy inference system was developed. The technique dynamically updates the number of packets sampled by responding to the traffic’s variations. Its performance was found to be superior to the conventional nonadaptive sampling methods.
机译:诸如医院和公众之类的组织越来越依赖大型计算机网络来访问信息并传递多媒体类型的数据。为了评估这些网络的有效性,需要分析流量参数。由于数据包的数量,检查每个包的传输参数是不切实际的,尤其是实时的。采样技术允许检查准确代表原始流量的数据包子集,因此在评估多媒体网络的性能中很重要。在这项研究中,开发了一种基于回归和模糊推理系统的自适应采样技术。该技术通过响应流量的变化来动态更新采样数据包的数量。发现其性能优于常规的非自适应采样方法。

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