Neural Network Based Traffic Prediction for Wireless Data Networks

In a wireless network environment accurate and timely estimation or prediction of network traffic has gained much importance in the recent past. The network applications use traffic prediction results to maintain its performance by adopting its behaviors. Network Service provider will use the prediction values in ensuring the better Quality of Service(QoS) to the network users by admission control and load balancing by inter or intra network handovers. This paper presents modeling and prediction of wireless network traffic. Here traffic is modeled as nonlinear and non-stationary time series. The nonlinear and non-stationary time series traffic is predicted using neural network and statistical methods. The results of both the methods are compared on different time scales or time granularity. The Neural Network (NN) architectures used in this study are Recurrent Radial Basis Function Network (RRBFN) and Echo state network (ESN).The statistical model used here in this work is Fractional Auto Regressive Integ...

[1]  J. Nazuno.  Haykin, Simon. Neural networks: A comprehensive foundation, Prentice Hall, Inc. Segunda Edición, 1999 , 2000 .

[2]  David Kotz,et al.  Analysis of a Campus-Wide Wireless Network , 2005, Wirel. Networks.

[3]  Harri Niska,et al.  Methods for imputation of missing values in air quality data sets , 2004 .

[4]  Chidchanok Lursinsap,et al.  Time-series data prediction based on reconstruction of missing samples and selective ensembling of FIR neural networks , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

[5]  Xiuchao Wu,et al.  Link characteristics estimation for IEEE 802.11 DCF based WLAN , 2004, 29th Annual IEEE International Conference on Local Computer Networks.

[6]  Witold Pedrycz,et al.  A Novel Framework for Imputation of Missing Values in Databases , 2007, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[7]  Rajeev Shorey,et al.  Mobile, Wireless and Sensor Networks: Technology, Applications and Future Directions , 2005 .

[8]  Paramvir Bahl,et al.  Characterizing user behavior and network performance in a public wireless LAN , 2002, SIGMETRICS '02.

[9]  Konstantina Papagiannaki,et al.  Long-term forecasting of Internet backbone traffic , 2005, IEEE Transactions on Neural Networks.

[10]  Stefano Giordano,et al.  Analysis of f-ARIMA processes in the modelling of broadband traffic , 2001, ICC 2001. IEEE International Conference on Communications. Conference Record (Cat. No.01CH37240).

[11]  S. A. Sarra,et al.  Integrated multiquadric radial basis function approximation methods , 2006, Comput. Math. Appl..

[12]  Haipeng Shen,et al.  Short-Term Traffic Forecasting in a Campus-Wide Wireless Network , 2005, 2005 IEEE 16th International Symposium on Personal, Indoor and Mobile Radio Communications.

[13]  Noureddine Zerhouni,et al.  Recurrent radial basis function network for time-series prediction , 2003 .

[14]  Oliver W. W. Yang,et al.  Traffic prediction using FARIMA models , 1999, 1999 IEEE International Conference on Communications (Cat. No. 99CH36311).

[15]  Ashok K. Agrawala,et al.  Characterizing the IEEE 802.11 Traffic: The Wireless Side , 2004 .

[16]  Tae Yoon Kim,et al.  Artificial neural networks for non-stationary time series , 2004, Neurocomputing.

[17]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[18]  Narasimhan Sundararajan,et al.  Performance Evaluation of GAP-RBF Network in Channel Equalization , 2005, Neural Processing Letters.

[19]  Kavitha Chandra,et al.  Time series models for Internet data traffic , 1999, Proceedings 24th Conference on Local Computer Networks. LCN'99.

[20]  Hyoung-Kee Choi,et al.  A behavioral model of Web traffic , 1999, Proceedings. Seventh International Conference on Network Protocols.

[21]  Sunghyun Choi,et al.  Analysis of User Behavior and Traffic Pattern in a Large-Scale 802.11a/b Network , 2004 .

[22]  Pieter S. Kritzinger,et al.  Delay analysis of downlink IP traffic on UMTS mobile networks , 2005, Perform. Evaluation.

[23]  C. Nelson,et al.  Spurious Periodicity in Inappropriately Detrended Time Series , 1981 .

[24]  Harald Haas,et al.  Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication , 2004, Science.

[25]  Alan Weiss,et al.  A compound model for TCP connection arrivals for LAN and WAN applications , 2002, Comput. Networks.

[26]  Félix Hernández-Campos,et al.  Spatio-temporal modeling of traffic workload in a campus WLAN , 2006, WICON '06.

[27]  Yantai Shu,et al.  Study on network traffic prediction techniques , 2005, Proceedings. 2005 International Conference on Wireless Communications, Networking and Mobile Computing, 2005..

[28]  R. Dahlhaus.  Fitting time series models to nonstationary processes , 1997 .

[29]  Gebhard Kirchgässner,et al.  Introduction to Modern Time Series Analysis , 2007 .

[30]  Songwu Lu,et al.  Characterizing flows in large wireless data networks , 2004, MobiCom '04.