CHARACTERIZATION OF UNIVARIATE LONG-TERM URBAN INTERNET TRAFFIC VOLUME

The proposed work deals with a real time hourly internet traffic data set in bits collected from ISPs located in 11 cities of European Country for the period 7 th June 2005 to 31 st July 2005. Then a thorough statistical inference has been drawn regarding the central tendency, dispersion and distribution of the data. Time-frequency analysis using Smoothed Pseudo Wigner Ville Distribution (SPWVD) is implied to infer knowledge about the non-stationarity of the system. A nonparametric test for normality, Anderson Darling Test (AD-Test) has been performed to detect the binary signature of nonlinearity in the signal. Delay Vector Variance Analysis (DVV) are being exploited to infer deeper knowledge about the determinism and nonlinearity in the system. The results confirm a nonstationary, relatively stochastic and nonlinear profile of the signal under observation.

[1]  Patrick Flandrin,et al.  Time-Frequency/Time-Scale Analysis , 1998 .

[2]  J. Clairambault,et al.  Linear and non-linear analyses of heart rate variability: a minireview. , 1996, Cardiovascular research.

[3]  Jens Timmer,et al.  Power of surrogate data testing with respect to nonstationarity , 1998, chao-dyn/9807039.

[4]  T. Schreiber,et al.  Surrogate time series , 1999, chao-dyn/9909037.

[5]  Ye Yuan,et al.  A comparison analysis of embedding dimensions between normal and epileptic EEG time series. , 2008, The journal of physiological sciences : JPS.

[6]  Rajdeep Ray,et al.  Scaling and nonlinear behaviour of daily mean temperature time series across India , 2016 .

[7]  Rajdeep Ray,et al.  Memory persistency and nonlinearity in daily mean dew point across India , 2016, Theoretical and Applied Climatology.

[8]  M. Hulle,et al.  The Delay Vector Variance Method for Detecting Determinism and Nonlinearity in Time Series , 2004 .

[9]  D. Darling,et al.  A Test of Goodness of Fit , 1954 .

[10]  Bhor Pradnya Network Traffic Analysis Using Packet Sniffer-A Review , 2015 .

[11]  E. S. Gardner EXPONENTIAL SMOOTHING: THE STATE OF THE ART, PART II , 2006 .

[12]  Philippe Owezarski,et al.  Modeling Internet backbone traffic at the flow level , 2003, IEEE Trans. Signal Process..

[13]  Sakshi Kaushal,et al.  Online Statistical Internet Traffic Classification Approaches , 2012 .

[14]  Amirtaha Taebi,et al.  Analysis of seismocardiographic signals using polynomial chirplet transform and smoothed pseudo Wigner-Ville distribution , 2017, 2017 IEEE Signal Processing in Medicine and Biology Symposium (SPMB).

[15]  L. Cohen,et al.  Time-frequency distributions-a review , 1989, Proc. IEEE.

[16]  Rajesh Krishnan,et al.  Using signal processing to analyze wireless data traffic , 2002, WiSE '02.

[17]  James Theiler,et al.  Testing for nonlinearity in time series: the method of surrogate data , 1992 .

[18]  H. S. Chandrashekar,et al.  Packet sniffing: a brief introduction , 2003 .

[19]  Danilo P. Mandic,et al.  A differential entropy based method for determining the optimal embedding parameters of a signal , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[20]  Claude Gharib,et al.  Smoothed pseudo Wigner–Ville distribution as an alternative to Fourier transform in rats , 2001, Autonomic Neuroscience.

[21]  Schreiber,et al.  Improved Surrogate Data for Nonlinearity Tests. , 1996, Physical review letters.

[22]  Masayuki Murata,et al.  Analysis of network traffic and its application to design of high-speed routers , 2000 .

[23]  Xinglin Chen,et al.  A Signal Decomposition Method for Ultrasonic Guided Wave Generated from Debonding Combining Smoothed Pseudo Wigner-Ville Distribution and Vold–Kalman Filter Order Tracking , 2017 .

[24]  B. Somekh,et al.  Theory and Methods in Social Research , 2013 .

[25]  Handanhal Ravinder,et al.  Determining The Optimal Values Of Exponential Smoothing Constants – Does Solver Really Work? , 2013 .