Design of QoS in Intelligent Communication Environments Based on Neural Network

Authors:
N. Xiong;L. T. Yang;Y. Yang;J. H. Park;G. Wei
Affiliations:
College of Computer Science, Wuhan University of Science and Engineering, Wuhan, China;Department of Computer Science, St. Francis Xavier University, Antigonish, Canada B2G 2W5;Department of Computer Science, Western Illinois University, Macomb, USA 61455;Department of Computer Science and Engineering, Seoul National University of Technology, Seoul, Korea and Department of Computer Science and Engineering, Seoul National University of Technology, S ...;College of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou, China 310018
Venue:
Wireless Personal Communications: An International Journal
Year:
2011

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Abstract

Due to the latest developments in communication and computing, smart services and applications are being deployed for various applications such as entertainment, health care, smart homes, security and surveillance. In intelligent communication environments, the main difficulty arising in designing an efficient congestion control scheme lies in the large propagation delay in data transfer which usually leads to a mismatch between the network resources and the amount of admitted traffic. To attack this problem, this paper describes a novel congestion control scheme in intelligent communication environments, which is based on a Back Propagation (BP) neural network technique. We consider a general computer communication model with multiple sources and one destination node. The dynamic buffer occupancy of the bottleneck node is predicted and controlled by using a BP neural network. The controlled best-effort traffic of the sources uses the bandwidth, which is left over by the guaranteed traffic. This control mechanism is shown to be able to avoid network congestion efficiently and to optimize the transfer performance both by the theoretic analyzing procedures and by the simulation studies.