ICTACT Journal on Communication Technology

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Characterization of Univariate Long-Term Urban Internet Traffic Volume


Affiliations
1 Department of Computer Applications, Dr. B.C. Roy Engineering College, India
2 Department of Electronics and Communication Engineering, Dr. B. C. Roy Engineering College, India
3 Department of Applied Electronics and Instrumentation Engineering, Dr. B. C. Roy Engineering College, India
4 Department of Computer Science and Engineering, National Institute of Technology, Durgapur, India
5 Department of Electronics and Communication Engineering, National Institute of Technology, Durgapur, India
     

   Subscribe/Renew Journal


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 7th June 2005 to 31st 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.

Keywords

Time-Frequency Analysis, Smoothed Pseudo Wigner Ville Distribution SPWVD, Anderson Darling Test (AD-Test), Delay Vector Variance Analysis (DVV), Nonlinearity.
Subscription Login to verify subscription
User
Notifications
Font Size

  • S. Ansari, S.G. Rajeev and H.S. Chandrasekhar, “Packet Sniffing: Brief Introduction”, IEEE Potentials, Vol. 21, No. 5, pp. 17-19, 2003.

  • P. Asrodia and H. Patel, “Network Traffic Analysis using Packet Sniffer”, International Journal of Engineering Research and Applications, Vol. 2, No. 3, pp. 854-856, 2012.

  • M. Bhatia and S. Kaushal, “Online Statistical Internet Traffic Classification Approaches”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 2, No. 3, pp. 1-6, 2012.

  • C. Partridge, D. Cousins, A.W. Jackson, R. Krishnan, T. Saxena and W.T. Strayer, “Using Signal Processing to Analyze Wireless Data Traffic”, Proceedings of 1st ACM workshop on Wireless Security, pp. 67-76, 2002.

  • C. Barakat, P. Thiran, G. Iannaccon, C. Diot and P. Owezarski, “Modeling Internet Backbone Traffic at the Flow Level”, IEEE Transactions on Signal Processing, Vol. 51, No. 8, pp. 1-14, 2003.

  • J. Timmer, “Power of Surrogate Data Testing with Respect to Nonstationary”, Physical Review E, Vol. 58, No. 4, pp. 5153-5156, 1998.

  • Datamart, Available at: www.datamarket.com.

  • R.G. Brown, R.F. Meyer and D.A. Desopo, “The Fundamental Theory of Exponential”, Operations Research, Vol. 9, No. 5, pp. 673-687, 1961.

  • E.S. Gardner, “Exponential Smoothing-The State of the ArtPart II”, International Journal of Forecasting, Vol. 22, No. 4, pp. 637-666, 2006.

  • J. Galtung, “Theory and methods of Social Research”, British Journal for the Philosophy of Science, Vol. 4, No. 3, pp. 1-3, 1967.

  • L. Cohen, “Time Frequency Distribution: A Review”, Proceedings of the IEEE, Vol. 77, No. 7, pp. 941-981, 1989.

  • E. Ebrahimzadeh, S.M. Alavi, B. Ahmad and A. Pakkhesal, “A Novel Approach for Detection of Deception using Smoothed Pseudo Wigner-Ville Distribution”, Journal of Biomedical Science and Engineering, Vol. 6, No. 1, pp. 818, 2013.

  • S.N.E. Pereira De, M. Custaud, J. Frutoso, I. Somody, C. Gharib and J. Fortrat, “Smoothed Pseudo Wigner-Ville Distribution as an Alternative to Fourier Transform in Rats”, Autonomic Neuroscience: Basic and Clinical, Vol. 87, No. 2-3, pp. 258-267, 2001.

  • A. Taebi and A.M. Hansen, “Analysis of Seismocardiographic Signals using Polynomial Chirplet

  • Transform and Smoothed Pseudo Wigner-Ville Distribution”, Electrical Engineering and Systems Science, pp. 1-6, 2017.

  • Junhua Wu, Xinglin Chen and Zheshu Ma, “A Signal Decomposition Method for Ultrasonic Guided Wave Generated from Debonding Combining Smoothed Pseudo Wigner-Ville Distribution and Vold-Kalman Filter Order Tracking”, Shock and Vibration, Vol. 2017, pp. 1-13, 2017.

  • P. Flandrin, “Time-Frequency/Time-Scale Analysis”, 1st Edition, Academic Press, 1999.

  • T.W. Anderson and D.A. Darling, “A Test of Goodness of Fit”, Journal of the American Statistical Association, Vol. 49, pp. 765-769, 2014.

  • T. Guatama, D.P. Mandic and M.M. Van Hulle, “The Delay Vector Variance Method for Detecting Determinism and Nonlinearity in Time Series”, Physica D: Nonlinear Phenomena, Vol. 190, No. 3-4, pp. 167-176, 2004.

  • R. Ray, M.H. Khodekar, K. Ghosh and A.K. Bhattacharjee, “Scaling and Nonlinear Behaviour of Daily mean Temperature Time Series across India”, Chaos, Solitons and Fractals, Vol. 84, pp. 9-14, 2016.

  • T. Schreiber and A. Schmitz, “Improved surrogate data for nonlinearity tests”, Physical Review Letters, Vol. 77, pp. 635-638, 1996.

  • P. Mansier, J. Clairambault, N. Charlotte, C. Medique, C. Vermeiren, G. LePape, F. Carre, A. Gounaropoulou and B. Swynghedauw, “Linear and Non-Linear Analyses of Heart Rate Variability: a Minireview”, Cardiovascular Research, Vol. 31, No. 3, pp. 371-379, 1996.

  • T. Schreiber and A. Schmitz, “Surrogate Time Series”, Physica D: Nonlinear Phenomena, Vol. 142, No. 3-4, pp. 346-382, 2000.

  • T. Gautama, D. Mandic and M.V. Hulle, “A Differential Entropy based method for Determining the Optimal Embedding Parameters of a Signal”, Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 113-117, 2003.

  • Y. Yuan, Y. Li and D.P. Mandic, “Comparison Analysis of Embedding Dimension between Normal and Epileptic EEG Time Series”, Journal of Physiological Sciences, Vol. 58, No. 4, pp. 239-247, 2008.

  • J. Theiler, S. Eubank, A. Longtin and B. Galdrikian, “Testing for Non Linearity in Time Series:the Method of Surrogate Data”, Physica D: Nonlinear Phenomena, Vol. 58, No. 1-4, pp. 77-94, 1992.

  • H.V. Ravinder, “Determining The Optimal Values of Exponential Smoothing Constants-does Solver Really Work?”, American Journal of Business Education, Vol. 6, No. 3, pp. 347-360, 2013.

  • S. Ata, M. Murata and H. Miyahara, “Analysis of Network Traffic and its Application to Design of High-Speed Routers”, IEICE Transactions on Information and Systems, Vol. 83, No. 5, pp. 988-995, 2000.

  • R. Ray, M.H. Khondekar, K. Ghosh and A.K. Bhattacharjee, “Memory Persistency and Non Linearity in Daily Mean Dew Point across India”, Theoretical and Applied Climatology, Vol. 124, No. 1-2, pp. 119-128, 2015.


Abstract Views: 246

PDF Views: 3




  • Characterization of Univariate Long-Term Urban Internet Traffic Volume

Abstract Views: 246  |  PDF Views: 3

Authors

Subhasish Debroy
Department of Computer Applications, Dr. B.C. Roy Engineering College, India
Rajdeep Ray
Department of Electronics and Communication Engineering, Dr. B. C. Roy Engineering College, India
Mofazzal Hossain Khondekar
Department of Applied Electronics and Instrumentation Engineering, Dr. B. C. Roy Engineering College, India
Baisakhi Chakraborty
Department of Computer Science and Engineering, National Institute of Technology, Durgapur, India
Anup Kumar Bhattacharjee
Department of Electronics and Communication Engineering, National Institute of Technology, Durgapur, India

Abstract


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 7th June 2005 to 31st 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.

Keywords


Time-Frequency Analysis, Smoothed Pseudo Wigner Ville Distribution SPWVD, Anderson Darling Test (AD-Test), Delay Vector Variance Analysis (DVV), Nonlinearity.

References