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

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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[7] Zhou X, Wang G, Wang B. An algorithm for constructing orthogonal armlet multi-wavelets with multiplicity r and dilation factor a[J]. Journal of Electronics (China), 2011, 28 (4-6): 643-651.
[8] Karagiannis T, Molle M, Faloutsos M, et al. A nonstationary Poisson view of Internet traffic[C]//INFOCOM 2004. Twenty-third AnnualJoint Conference of the IEEE Computer and Communications Societies. IEEE, 2004, 3: 1558-1569.
[9] Zou J, Zhao D. Real-time CBR traffic scheduling in IEEE 802.16-based wireless mesh networks[J]. Wireless Networks, 2009, 15 (1): 65-72.
[10] Sanyasiraju Y, Satyanarayana C. On optimization of the RBF shape parameter in a grid-free local scheme for convection dominated problems over non-uniform centers[J]. Applied Mathematical Modelling, 2013, 37 (12): 7245-7272.
[11] Wellens M, Riihijarvi J, Mahonen P. Modelling primary system activity in dynamic spectrum access networks by aggregated ON/OFF-processes[C]//Sensor, Mesh and Ad Hoc Communications and Networks Workshops, 2009. SECON Workshops' 09. 6th Annual IEEE Communications Society Conference on. IEEE, 2009: 1-6.
[12] Treiber M, Kesting A. Traffic flow dynamics[J]. Traffic Flow Dynamics: Data, Models and Simulation, Springer-Verlag Berlin Heidelberg, 2013.
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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
  • School of Software Engineering, Department of Information, Beijing University of Technology, Beijing, China

  • School of Software Engineering, Department of Information, Beijing University of Technology, Beijing, China

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