Software Engineering and Applications
Vol. 08  No. 03 ( 2019 ), Article ID: 30868 , 10 pages
10.12677/SEA.2019.83015

The Influencing Factors of Network Packet Loss’s Long-Range Dependence Have Impacts on the Packet Loss Rate under the Internet of Things

Yibin Hou, Jin Wang

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

Received: May 30th, 2019; accepted: June 14th, 2019; published: June 21st, 2019

ABSTRACT

The mapping model of the network and network video packet loss rate to the quality of user experience is a hot topic in the academia and the industry and commerce. 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, and the number of superimposed source N, shape parameter, Hurst parameter and the output link speed have impacts on long-range dependence. We came to the conclusion that when superimposed source N is more, 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.

Keywords:No-Reference, Quality Assessment Model, Network Packet Loss, Long-Range Dependence, The Long Phase of Network Packet Loss

1. 引言

2. 相关内容介绍和原理

2.1. 单路复用网络模型的介绍

2.2. 长相关性介绍以及原理

2.2.1. 自相似过程的定义

2.2.2. 自相似过程的性质

2.2.3. 自相似过程的原理

2.2.4. 小波分析的介绍和原理

3. 影响网络丢包的长相关性的因素

NS2产生自相似业务的原理：NS2提供了四种类型的流量产生器：1) EXPOO。2) POO：Pareto分布。(ON/OFF)产生通信量。3) CBR，以确定的速率产生流量。4) TrafficTrace，根据跟踪文件产生流量。POO_Traffic是包含在OTCL类Application/Traffic/Pareto中的一个流量发生器。它按照Pareto on/off分布产生流量。在on期，以固定的速率发送包，而在off期，没有包的发送。N个叠加的多个重尾的On/OFF源叠加可产生自相似业务流。N越大，自相似现象越发的明显。各个文件的位置：1) Application类：C++中Application类(~/ns/apps/app.h)。2) trafficGennerator抽象基类(~ns/tools/trafgeh.h)。3) POO_traffic (~ns/tools/pareto.cc)。4) CBR_Traffic (~ns/tools/cbr_traffic.cc) [7] 。本文据此配置POO_traffic的参数如下：

set traffic [new Application/Traffic/Pareto]

$traffic set packetSize_500$traffic set burst_time_500 ms

$traffic set idle_time_500 ms$traffic set rate_100k

\$traffic set shape_1.5

Figure 1. Flow rate chart

3.1. 叠加源个数N对长相关性的影响

Figure 2. Rate map with N value 5

Figure 3. Rate map with N value 7

Figure 4. Rate map with N value 9

Figure 5. Rate map with N value 11

3.2. 形状参数对长相关性的影响

3.3. Hurst参数对长相关性的影响

3.4. 输出链路速度对长相关性影响

N = 5，形状参数为1.5。link = 10 MB。我们假设链路速度越大，长相关性减小，Hurst参数减小，丢包率越小。链路速度分别设置为5 MB，10 MB，15 MB，20 MB，在这些设置情况下的流量速率图如图 [12] 。通过观察上面的四个图的线型和线型与横轴围成的面积我们可以发现，随着输出链路速度的逐渐增大，自相似性逐渐减小，H减小，丢包率越小。由此可以得出结论，输出链路速度越大，长相关性越弱，H越小，丢包率越小。

4. 总结和展望

The Influencing Factors of Network Packet Loss’s Long-Range Dependence Have Impacts on the Packet Loss Rate under the Internet of Things[J]. 软件工程与应用, 2019, 08(03): 121-130. https://doi.org/10.12677/SEA.2019.83015

1. 1. Kim, D.I. (2006) Selective Relative Best Scheduling for Best-Effort Downlink Packet Data. IEEE Transactions on Warless Communication, 5, 1254-1259.

2. 2. Kim, H.J. and Choi, S.G. (2010) A Study on a QoS/QoE Correlation Model for QoE Evaluation on IPTV Service. Proceedings of the 12th International Conference on Advanced Communication Technology (ICACT), Phoenix Park, 7-10 February 2010, 1377-1382.

3. 3. Wang, D.C. (2013) A Risky Asset Model Based on Lévy Processes and Asymptotically Self-Similar Activity Time Processes with Long-Range Dependence. Science China Mathe-matics, 56, 2353-2366. https://doi.org/10.1007/s11425-013-4626-9

4. 4. Benhaddou, R., Kulik, R., Pensky, M. and Sapatinas, T. (2014) Multichannel Deconvolution with Long-Range Dependence: A Minimax Study. Journal of Statistical Planning and Inference, 148, 1-19. https://doi.org/10.1016/j.jspi.2013.12.008

5. 5. Wang, T. and Zhang S.Q. (2011) Study on Linear Correlation Coefficient and Nonlinear Correlation Coefficient in Mathematical Statistics. Studies in Mathematical Sciences, 3, 58-63.

6. 6. Arras, B. (2014) On a Class of Self-Similar Processes with Stationary Increments in Higher Order Wiener Chaoses. Stochastic Processes and Their Applications, 124, 2415-2441. https://doi.org/10.1016/j.spa.2014.02.012

7. 7. Wellens, M., Riihijarvi, J. and Mahonen, P. (2009) Modeling Primary System Activity in Dynamic Spectrum Access Networks by Aggregated ON/OFF-Processes. 2009 6th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communica-tions and Networks Workshops (SECON Workshops’09), Rome, 22-26 June 2009, 1-6. https://doi.org/10.1109/SAHCNW.2009.5172946

8. 8. Treiber, M. and Kesting, A. (2013) Traffic Flow Dynamics. In: Traffic Flow Dynamics: Data, Models and Simulation, Springer-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-642-32460-4

9. 9. Zou, J. and Zhao, D. (2009) Real-Time CBR Traffic Scheduling in IEEE 802.16-Based Wireless Mesh Networks. Wireless Networks, 15, 65-72. https://doi.org/10.1007/s11276-007-0025-x

10. 10. Sanyasiraju, Y. and Satyanarayana, C. (2013) On Optimization of the RBF Shape Parameter in a Grid-Free Local Scheme for Convection Dominated Problems over Non-Uniform Centers. Applied Mathematical Modeling, 37, 7245-7272. https://doi.org/10.1016/j.apm.2013.01.054

11. 11. Chen, Y.Q., Sun, R. and Zhou, A. (2007) An Improved Hurst Parameter Estimator Based on Fractional Fourier Transform. ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Las Vegas, Nevada, 4-7 September 2007, 1223-1233.

12. 12. Chen, J.P. and Niemeyer, R.G. (2014) Periodic Billiard Orbits of Self-Similar Sierpiński Carpets. Journal of Mathematical Analysis and Applications, 416, 969-994. https://doi.org/10.1016/j.jmaa.2014.03.001