Volume 1, Issue 4, December 2008, Pages 379 - 389
Neural Network Based Traffic Prediction for Wireless Data Networks
- Gowrishankar 0, P.S. Satyanarayana 1
0Department of Computer Science and Engineering, B.M.S College of Engineering
1Department of Electronics and Communication Engineering, B.M.S. College of Engineering
Available Online 2 January 2009.
- https://doi.org/10.2991/ijcis.2008.1.4.9How to use a DOI?
- Traffic flow, Time series, QoS, Prediction, FARIMA and Neural 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 Integrated Moving Average (FARIMA) model. The traffic prediction accuracy of neural network and statistical models are in the range of 96.4% to 98.3% and 78.5% to 80.2% respectively.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - JOUR AU - Gowrishankar AU - P.S. Satyanarayana PY - 2009 DA - 2009/01 TI - Neural Network Based Traffic Prediction for Wireless Data Networks JO - International Journal of Computational Intelligence Systems SP - 379 EP - 389 VL - 1 IS - 4 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.2008.1.4.9 DO - https://doi.org/10.2991/ijcis.2008.1.4.9 ID - Gowrishankar2009 ER -