MPEG-4 Internet Traffic Estimation Using Recurrent CGPANN

  • Gul Muhammad Khan
  • Fahad Ullah
  • Sahibzada Ali Mahmud
Conference paper

DOI: 10.1007/978-3-642-41013-0_3

Part of the Communications in Computer and Information Science book series (CCIS, volume 383)
Cite this paper as:
Muhammad Khan G., Ullah F., Mahmud S.A. (2013) MPEG-4 Internet Traffic Estimation Using Recurrent CGPANN. In: Iliadis L., Papadopoulos H., Jayne C. (eds) Engineering Applications of Neural Networks. EANN 2013. Communications in Computer and Information Science, vol 383. Springer, Berlin, Heidelberg

Abstract

Stretching across the horizon of data communication and networking, in almost every scenario, accurate bandwidth allocation has been a challenging problem. From simple online video streaming to the sophisticated communication network underlying a Smart Grid, efficient management of bandwidth is always desired. One way to achieve such efficiency lies in the science of predication: an intelligent system can be deployed that can estimate the sizes of upcoming data packets by analyzing patterns in the previously received data. This paper presents such a system that implements a fast and robust Neuro-Evolutionary algorithm known as Recurrent-Cartesian Genetic Programming evolved Artificial Neural Network (R-CGPANN). Based on the previously received 10 MPEG-4 video frames, the system estimates the size of the next frame. The simulation results show that the recurrence in CGPANN measurably outperform not only the feed forward version of the said algorithm but other contemporary methods in the field.

Keywords

Neuro-Evolution frame estimation recurrent neural networks bandwidth management Cartesian Genetic Programming 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Gul Muhammad Khan
    • 1
  • Fahad Ullah
    • 1
  • Sahibzada Ali Mahmud
    • 1
  1. 1.Department of Electrical EngineeringUET PeshawarPakistan

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