Internet of Things and Cloud Computing

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QOE Forecast Under the WSN Internet of Things

Received: 21 February 2017    Accepted: 13 April 2017    Published: 07 June 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 because QOE is important in the network and packet loss rate is the key point in many papers. In order to study the QOE forecast under the Internet of things, building a NS2+MyEvalvid simulation platform, by the method of modifying QoS parameters to simulate different degrees of packet loss, focus on the QOE forecast under the Internet of things. Experimental results show that, QOE forecast under the Internet of things have many methods and is very important. SVM+PCA is an important method in the field of Internet of things, the Internet of things, including Internet technology, WSN networks, RFID can be part of the WSN network. 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 farmand JSP sponge in the Internet of things is the future direction of development.

DOI 10.11648/j.iotcc.20170502.12
Published in Internet of Things and Cloud Computing (Volume 5, Issue 2, April 2017)
Page(s) 29-37
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), 2024. Published by Science Publishing Group

Keywords

QOE, Forecast, Internet of Things

References
[1] Kim H J, Choi S G. A study on a QoS/QoE correlation model for QoE evaluation on IPTV service[C]//Advanced Communication Technology (ICACT), 2010 The 12th International Conference on. IEEE, 2010, 2: 1377-1382.
[2] Khan A, Sun L, Jammeh E, et al. Quality of experience-driven adaptation scheme for video applications over wireless networks [J]. IET communications, 2010, 4(11): 1337-1347.
[3] Zhang F, Steinbach E, Zhang P. MDVQM: A novel multidimensional no-reference video quality metric for video transcoding [J]. Journal of Visual Communication and Image Representation, 2014, 25(3): 542-554.
[4] Khan A, Sun L, Jammeh E, et al. Quality of experience-driven adaptation scheme for video applications over wireless networks[J].Communications, IET, 2010, 4(11): 1337-1347.
[5] Maisonneuve J, Deschanel M, Heiles J, et al. An overview of IPTV standards development [J]. Broadcasting, IEEE Transactions on, 2009, 55(2): 315-328.
[6] NACCARI M, TAGLIASACCHI M, TUBARO S.No-reference video quality monitoring for H.264/AVCcoded video [J]. IEEE Transactions on Multimedia, 2009.
[7] Yang F, Wan S, Xie Q, et al. No-reference quality assessment for networked video via primary analysis of bit stream [J]. Circuits and Systems for Video Technology, IEEE Transactions on, 2010, 20(11): 1544-1554.
[8] Tao S, Apostolopoulos J, Guérin R. Real-time monitoring of video quality in IP networks [J]. IEEE/ACM Transactions on Networking (TON), 2008, 16(5): 1052-1065.
[9] Chen J, Ji G. Weighted least squares twin support vector machines for pattern classification[C]//Computer and Automation Engineering (ICCAE), 2010 The 2nd International Conference on. IEEE, 2010, 2: 242-246.
[10] CHEN Jing, JI Guang-rong. Weighted least squares twin support vector machines for pattern classification. Proceedings of the 2nd International Conference on Computer and Automation Engineering. 2010.
Author Information
  • Department of Information, School of Software Engineering, Beijing University of Technology, Beijing, China

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

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    Yibin Hou, Jin Wang. (2017). QOE Forecast Under the WSN Internet of Things. Internet of Things and Cloud Computing, 5(2), 29-37. https://doi.org/10.11648/j.iotcc.20170502.12

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    Yibin Hou; Jin Wang. QOE Forecast Under the WSN Internet of Things. Internet Things Cloud Comput. 2017, 5(2), 29-37. doi: 10.11648/j.iotcc.20170502.12

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    AMA Style

    Yibin Hou, Jin Wang. QOE Forecast Under the WSN Internet of Things. Internet Things Cloud Comput. 2017;5(2):29-37. doi: 10.11648/j.iotcc.20170502.12

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  • @article{10.11648/j.iotcc.20170502.12,
      author = {Yibin Hou and Jin Wang},
      title = {QOE Forecast Under the WSN Internet of Things},
      journal = {Internet of Things and Cloud Computing},
      volume = {5},
      number = {2},
      pages = {29-37},
      doi = {10.11648/j.iotcc.20170502.12},
      url = {https://doi.org/10.11648/j.iotcc.20170502.12},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.iotcc.20170502.12},
      abstract = {The Internet of things, including Internet technology, including wired and wireless networks. In this paper, we investigate on the QOE because QOE is important in the network and packet loss rate is the key point in many papers. In order to study the QOE forecast under the Internet of things, building a NS2+MyEvalvid simulation platform, by the method of modifying QoS parameters to simulate different degrees of packet loss, focus on the QOE forecast under the Internet of things. Experimental results show that, QOE forecast under the Internet of things have many methods and is very important. SVM+PCA is an important method in the field of Internet of things, the Internet of things, including Internet technology, WSN networks, RFID can be part of the WSN network. 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 farmand JSP sponge in the Internet of things is the future direction of development.},
     year = {2017}
    }
    

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  • TY  - JOUR
    T1  - QOE Forecast Under the WSN Internet of Things
    AU  - Yibin Hou
    AU  - Jin Wang
    Y1  - 2017/06/07
    PY  - 2017
    N1  - https://doi.org/10.11648/j.iotcc.20170502.12
    DO  - 10.11648/j.iotcc.20170502.12
    T2  - Internet of Things and Cloud Computing
    JF  - Internet of Things and Cloud Computing
    JO  - Internet of Things and Cloud Computing
    SP  - 29
    EP  - 37
    PB  - Science Publishing Group
    SN  - 2376-7731
    UR  - https://doi.org/10.11648/j.iotcc.20170502.12
    AB  - The Internet of things, including Internet technology, including wired and wireless networks. In this paper, we investigate on the QOE because QOE is important in the network and packet loss rate is the key point in many papers. In order to study the QOE forecast under the Internet of things, building a NS2+MyEvalvid simulation platform, by the method of modifying QoS parameters to simulate different degrees of packet loss, focus on the QOE forecast under the Internet of things. Experimental results show that, QOE forecast under the Internet of things have many methods and is very important. SVM+PCA is an important method in the field of Internet of things, the Internet of things, including Internet technology, WSN networks, RFID can be part of the WSN network. 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 farmand JSP sponge in the Internet of things is the future direction of development.
    VL  - 5
    IS  - 2
    ER  - 

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