Investigation of the IOT Network of Packet Loss’s Long-Range Dependence and QOE

Yibin Hou; Jin Wang

doi:doi:10.11648/j.mlr.20170201.11

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Investigation of the IOT Network of Packet Loss’s Long-Range Dependence and QOE

Yibin Hou, Jin Wang

Published in Machine Learning Research (Volume 2, Issue 1)

Received: 4 January 2017 Accepted: 21 January 2017 Published: 20 February 2017

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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.

Published in	Machine Learning Research (Volume 2, Issue 1)
DOI	10.11648/j.mlr.20170201.11
Page(s)	1-9
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

No-Reference, Quality Assessment Model, Network Packet Loss, Long-Range Dependence

References

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APA Style

Yibin Hou, Jin Wang. (2017). Investigation of the IOT Network of Packet Loss’s Long-Range Dependence and QOE. Machine Learning Research, 2(1), 1-9. https://doi.org/10.11648/j.mlr.20170201.11

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ACS Style

Yibin Hou; Jin Wang. Investigation of the IOT Network of Packet Loss’s Long-Range Dependence and QOE. Mach. Learn. Res. 2017, 2(1), 1-9. doi: 10.11648/j.mlr.20170201.11

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AMA Style

Yibin Hou, Jin Wang. Investigation of the IOT Network of Packet Loss’s Long-Range Dependence and QOE. Mach Learn Res. 2017;2(1):1-9. doi: 10.11648/j.mlr.20170201.11

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@article{10.11648/j.mlr.20170201.11,
  author = {Yibin Hou and Jin Wang},
  title = {Investigation of the IOT Network of Packet Loss’s Long-Range Dependence and QOE},
  journal = {Machine Learning Research},
  volume = {2},
  number = {1},
  pages = {1-9},
  doi = {10.11648/j.mlr.20170201.11},
  url = {https://doi.org/10.11648/j.mlr.20170201.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20170201.11},
  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.},
 year = {2017}
}

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TY  - JOUR
T1  - Investigation of the IOT Network of Packet Loss’s Long-Range Dependence and QOE
AU  - Yibin Hou
AU  - Jin Wang
Y1  - 2017/02/20
PY  - 2017
N1  - https://doi.org/10.11648/j.mlr.20170201.11
DO  - 10.11648/j.mlr.20170201.11
T2  - Machine Learning Research
JF  - Machine Learning Research
JO  - Machine Learning Research
SP  - 1
EP  - 9
PB  - Science Publishing Group
SN  - 2637-5680
UR  - https://doi.org/10.11648/j.mlr.20170201.11
AB  - 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.
VL  - 2
IS  - 1
ER  -

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Author Information

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

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Plain Text BibTeX RIS

APA Style

Yibin Hou, Jin Wang. (2017). Investigation of the IOT Network of Packet Loss’s Long-Range Dependence and QOE. Machine Learning Research, 2(1), 1-9. https://doi.org/10.11648/j.mlr.20170201.11

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ACS Style

Yibin Hou; Jin Wang. Investigation of the IOT Network of Packet Loss’s Long-Range Dependence and QOE. Mach. Learn. Res. 2017, 2(1), 1-9. doi: 10.11648/j.mlr.20170201.11

Copy | Download

AMA Style

Yibin Hou, Jin Wang. Investigation of the IOT Network of Packet Loss’s Long-Range Dependence and QOE. Mach Learn Res. 2017;2(1):1-9. doi: 10.11648/j.mlr.20170201.11

Copy | Download

@article{10.11648/j.mlr.20170201.11,
  author = {Yibin Hou and Jin Wang},
  title = {Investigation of the IOT Network of Packet Loss’s Long-Range Dependence and QOE},
  journal = {Machine Learning Research},
  volume = {2},
  number = {1},
  pages = {1-9},
  doi = {10.11648/j.mlr.20170201.11},
  url = {https://doi.org/10.11648/j.mlr.20170201.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20170201.11},
  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.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Investigation of the IOT Network of Packet Loss’s Long-Range Dependence and QOE
AU  - Yibin Hou
AU  - Jin Wang
Y1  - 2017/02/20
PY  - 2017
N1  - https://doi.org/10.11648/j.mlr.20170201.11
DO  - 10.11648/j.mlr.20170201.11
T2  - Machine Learning Research
JF  - Machine Learning Research
JO  - Machine Learning Research
SP  - 1
EP  - 9
PB  - Science Publishing Group
SN  - 2637-5680
UR  - https://doi.org/10.11648/j.mlr.20170201.11
AB  - 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.
VL  - 2
IS  - 1
ER  -

Copy | Download