Volume 2, Issue 1, March 2017, Page: 1-9
Investigation of the IOT Network of Packet Loss’s Long-Range Dependence and QOE
Yibin Hou, School of Software Engineering, Department of Information, Beijing University of Technology, Beijing, China
Jin Wang, School of Software Engineering, Department of Information, Beijing University of Technology, Beijing, China
Received: Jan. 4, 2017;       Accepted: Jan. 21, 2017;       Published: Feb. 20, 2017
DOI: 10.11648/j.mlr.20170201.11      View  1695      Downloads  83
Abstract
The Internet of things, including Internet technology, including wired and wireless networks. Internet of Things and the Internet is the relationship between the parent and the child. In this paper, we aim to study the Investigation on the network packet loss’s long-range dependence and QOE and gain a good result and conclusion. In order to better establish no-reference video quality assessment model considering the network packet loss and further gain a better QoE evaluation, so we build NS2 + MyEvalvid simulation platform to study the scale characteristic of the network packet loss, scale characteristic of packet loss through the influence of packet loss rate to influence QoE. The experimental results show that, packet loss processes have long-range dependence, the number of superimposed source N, shape parameter, Hurst parameter, the output link speed have impacts on long-range dependence. We came to the conclusion that when superimposed source N is more, the shape parameter is smaller, Hurst parameter is bigger, the output link speed is smaller, packet loss’s long range dependence is larger, packet loss rate is high.
Keywords
No-Reference, Quality Assessment Model, Network Packet Loss, Long-Range Dependence
To cite this article
Yibin Hou, Jin Wang, Investigation of the IOT Network of Packet Loss’s Long-Range Dependence and QOE, Machine Learning Research. Vol. 2, No. 1, 2017, pp. 1-9. doi: 10.11648/j.mlr.20170201.11
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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