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Investigation on the QOE and Packet Loss Rate of the IOT Network

Received: 22 December 2016     Accepted: 6 January 2017     Published: 4 February 2017
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Abstract

The Internet of things, including Internet technology, including wired and wireless networks. In this paper, we investigate on the QOE and packet loss rate of the network because QOE is important in the network and packet loss rate is the key point in many papers. In order to study the influence of packet loss on the users’ quality of experience QoE and establish the Mapping model of the two when the video transmit in the network, building a NS2+ MyEvalvid simulation platform, by the method of modifying QoS parameters to simulate different degrees of packet loss, focus on the influence of packet loss on QoE and establish the mapping model between them. Experimental results show that, packet loss has a significant influence on Quality of experience. Packet loss rate and the Quality of experience presents a nonlinear relationship, and use Matlab to establish the mapping model, this model’s accuracy is high, easy to operate, can real-time detect packet loss has influence on the user’s quality of experience (QoE). High thinking turn thinking and intelligence to do system and intelligent housing system. The application of intelligent transportation and intelligent building and intelligent engineering system and Intelligent farm and JSP sponge in the Internet of things is the future direction of development.

Published in American Journal of Data Mining and Knowledge Discovery (Volume 2, Issue 1)
DOI 10.11648/j.ajdmkd.20170201.13
Page(s) 15-30
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

Packet Loss Rate, Influence, Quality of Experience, Mapping Model

References
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[3] Klaue J, Rathke B, Wolisz A. Evalvid–A framework for video transmission and quality evaluation [C]//Proc of the 13th International Conference on Modelling Techniques and Tools for Computer Performance Evaluation. Springer Berlin Heidelberg, 2003: 255-272.
[4] Yu C Y, Ke C H, Chen R S, et al. MyEvalvid_RTP: A evaluation framework for more realistic simulations of multimedia transmissions [J]. International Journal of Software Engineering and Its Applications, 2008, 2 (2): 21-32.
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[15] Mikoaj Leszczuk, Lucjan Janowski, Piotr Romaniak, Zdzisaw Papir. Assessing quality of experience for high definition video streaming under diverse packet loss patterns [SD]. Image Communication, 2011: 137-143.
[16] Jasna Zei, Mesud Hadiali, Adisa Haskovi. An approach to estimate correlation between QoS and perceptual video quality in packet-switched networks [C]. MIPRO, 2012 Proceedings of the 35th International Convention. IEEE, 2012: 573-578.
[17] Amy R. Reibman, Vinay A. Vaishampayan, Yegnaswamy Sermadevi. Quality Monitoring of Video Over a Packet Network. IEEE Transactions on Multimedia, 2004.
[18] Verscheure Olivier, Frossard Pascal, Hamdi Maher. MPEG-2video services over packet networks: Joint effect of encoding rate and data loss on user-oriented QoS. Proceedings of the8th International Workshop on Network and Operating Systems Support for Digital Audio and Video, 1998.
[19] K. Yamagishi, T. Hayashi. Analysis of psycological factors for quality assessment of interctive multimodal service [J]. Electronic Imaging, 2005: 130-138.
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  • APA Style

    Yibin Hou, Jin Wang. (2017). Investigation on the QOE and Packet Loss Rate of the IOT Network. American Journal of Data Mining and Knowledge Discovery, 2(1), 15-30. https://doi.org/10.11648/j.ajdmkd.20170201.13

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    ACS Style

    Yibin Hou; Jin Wang. Investigation on the QOE and Packet Loss Rate of the IOT Network. Am. J. Data Min. Knowl. Discov. 2017, 2(1), 15-30. doi: 10.11648/j.ajdmkd.20170201.13

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    AMA Style

    Yibin Hou, Jin Wang. Investigation on the QOE and Packet Loss Rate of the IOT Network. Am J Data Min Knowl Discov. 2017;2(1):15-30. doi: 10.11648/j.ajdmkd.20170201.13

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  • @article{10.11648/j.ajdmkd.20170201.13,
      author = {Yibin Hou and Jin Wang},
      title = {Investigation on the QOE and Packet Loss Rate of the IOT Network},
      journal = {American Journal of Data Mining and Knowledge Discovery},
      volume = {2},
      number = {1},
      pages = {15-30},
      doi = {10.11648/j.ajdmkd.20170201.13},
      url = {https://doi.org/10.11648/j.ajdmkd.20170201.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajdmkd.20170201.13},
      abstract = {The Internet of things, including Internet technology, including wired and wireless networks. In this paper, we investigate on the QOE and packet loss rate of the network because QOE is important in the network and packet loss rate is the key point in many papers. In order to study the influence of packet loss on the users’ quality of experience QoE and establish the Mapping model of the two when the video transmit in the network, building a NS2+ MyEvalvid simulation platform, by the method of modifying QoS parameters to simulate different degrees of packet loss, focus on the influence of packet loss on QoE and establish the mapping model between them. Experimental results show that, packet loss has a significant influence on Quality of experience. Packet loss rate and the Quality of experience presents a nonlinear relationship, and use Matlab to establish the mapping model, this model’s accuracy is high, easy to operate, can real-time detect packet loss has influence on the user’s quality of experience (QoE). High thinking turn thinking and intelligence to do system and intelligent housing system. The application of intelligent transportation and intelligent building and intelligent engineering system and Intelligent farm and JSP sponge in the Internet of things is the future direction of development.},
     year = {2017}
    }
    

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  • TY  - JOUR
    T1  - Investigation on the QOE and Packet Loss Rate of the IOT Network
    AU  - Yibin Hou
    AU  - Jin Wang
    Y1  - 2017/02/04
    PY  - 2017
    N1  - https://doi.org/10.11648/j.ajdmkd.20170201.13
    DO  - 10.11648/j.ajdmkd.20170201.13
    T2  - American Journal of Data Mining and Knowledge Discovery
    JF  - American Journal of Data Mining and Knowledge Discovery
    JO  - American Journal of Data Mining and Knowledge Discovery
    SP  - 15
    EP  - 30
    PB  - Science Publishing Group
    SN  - 2578-7837
    UR  - https://doi.org/10.11648/j.ajdmkd.20170201.13
    AB  - The Internet of things, including Internet technology, including wired and wireless networks. In this paper, we investigate on the QOE and packet loss rate of the network because QOE is important in the network and packet loss rate is the key point in many papers. In order to study the influence of packet loss on the users’ quality of experience QoE and establish the Mapping model of the two when the video transmit in the network, building a NS2+ MyEvalvid simulation platform, by the method of modifying QoS parameters to simulate different degrees of packet loss, focus on the influence of packet loss on QoE and establish the mapping model between them. Experimental results show that, packet loss has a significant influence on Quality of experience. Packet loss rate and the Quality of experience presents a nonlinear relationship, and use Matlab to establish the mapping model, this model’s accuracy is high, easy to operate, can real-time detect packet loss has influence on the user’s quality of experience (QoE). High thinking turn thinking and intelligence to do system and intelligent housing system. The application of intelligent transportation and intelligent building and intelligent engineering system and Intelligent farm and JSP sponge in the Internet of things is the future direction of development.
    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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