Machine Learning Research
Volume 2, Issue 1, March 2017, Pages: 1-9
Received: Jan. 4, 2017;
Accepted: Jan. 21, 2017;
Published: Feb. 20, 2017
Views 2228 Downloads 106
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
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.
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.
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.
Dong In Kim, Senior Member. Selective Relative Best Scheduling for Best-Effort Downlink Packet Data [J]. Las Vegas, NV USA: IEEE Transactions on Wirless Communication. 2006, 6.
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.
Wang D C. A risky asset model based on Lévy processes and asymptotically self-similar activity time processes with long-range dependence[J]. Science China Mathematics, 2013, 56 (11): 2353-2366.
Benhaddou R, Kulik R, Pensky M, et al. Multichannel deconvolution with long-range dependence: A minimax study[J]. Journal of Statistical Planning and Inference, 2014, 148: 1-19.
Karagiannis T, Molle M, Faloutsos M. Long-range dependence ten years of Internet traffic modeling[J]. Internet Computing, IEEE, 2004, 8 (5): 57-64.
Doi H, Matsuda T, Yamamoto M. Performance evaluation of multi-fractal nature of TCP traffic with RED gateway[C]//Local Computer Networks, 2004. 29th Annual IEEE International Conference on. IEEE, 2004: 400-401.
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.
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.
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.
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.
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.
Treiber M, Kesting A. Traffic flow dynamics[J]. Traffic Flow Dynamics: Data, Models and Simulation, Springer-Verlag Berlin Heidelberg, 2013.
Ting W, Shiqiang Z. Study on linear correlation coefficient and nonlinear correlation coefficient in mathematical statistics[J]. Studies in Mathematical Sciences, 2011, 3 (1): 58-63.
Arras B. On a class of self-similar processes with stationary increments in higher order Wiener chaoses[J]. Stochastic Processes and their Applications, 2014, 124 (7): 2415-2441.
Chen Y Q, Sun R, Zhou A. An improved Hurst parameter estimator based on fractional Fourier transform[C]//ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2007: 1223-1233.
Chen J P, Niemeyer R G. Periodic billiard orbits of self-similar Sierpiński carpets [J]. Journal of Mathematical Analysis and Applications, 2014, 416 (2): 969-994.