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.
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Alan Bivens J., Szymanski B. K., Embrechts M. J. (2002) Network congestion arbitration and source problem prediction using neural networks. Smart Engineering System Design 4: 243–252
Ascia, G., Catania, V., Ficili, G., & Panno, D. (2001). A fuzzy buffer management scheme for ATM and IP networks, In IEEE INFOCOM 2001 (pp. 1539–1547) Anchorage, Alaska, April 22–26.
Ascia, G., Catania, V., & Panno, D. (2002). An efficient buffer management policy based on an integrated Fuzzy-GA approach, IEEE INFOCOM 2002, New York, June 23–27, No.107.
Aweya J., Montuno D. Y., Zhang Q.-J., Orozco-Barbosa L. (2000a) Multi-step neural predictive techniques for congestion control-part 2: Control procedures. International Journal of Parallel and Distributed Systems and Networks 3(3): 139–143
Aweya J., Montuno D. Y., Zhang Q.-J., Orozco-Barbosa L. (2000b) Multi-step neural predictive techniques for congestion control-part 1: Prediction and control models. International Journal of Parallel and Distributed Systems and Networks 3(1): 1–8
Benmohamed L., Meerkov S. M. (1993) Feedback control of congestion in packet switching networks: The case of single congested node. IEEE/ACM Transaction on Networking 1(6): 693–708
Cavendish D. (1995) Proportional rate-based congestion control under long propagation delay. International Journal of Communication Systems 8: 79–89
Chen W. H., Zheng W. X. (2008) Improved delay-dependent asymptotic stability criteria for delayed neural networks. IEEE Transactions on Neural Networks 19(11): 1–8
Cotter N. E. (1990) The stone-weierstrass theorem and its application to neural networks. IEEE Transactions on Neural Networks 1(4): 290–295
Darbyshire, P. & Wang, D. H. (2003). Learning to survive: Increased learning rates by communication in a multi-agent System. In The 16th Australian Joint Conference on Artificial Intelligence (AI’03), Perth, Australia, 3–5 December.
Ensari T., Arik S. (2005a) Global stability of class of neural networks with time varying delays. IEEE Transactions on Circuit and Systems-II, Express Briefs 52(3): 126–130
Ensari T., Arik S. (2005b) Global stability analysis of neural networks with multiple time varying delays. IEEE Transactions on Automatic Control 50(11): 1781–1785
Filipiak J. (1988) Modeling and control of dynamic flows in communication networks. Springer, New York
Ge S. S., Yang C., Lee T. H. (2008) Adaptive predictive control using neural network for a class of pure-feedback systems in discrete time. IEEE Transactions on Neural Networks 19(9): 1599–1614
Guarneri P., Rocca G., Gobbi M. (2008) A neural-network-based model for the dynamic simulation of the tire/suspension system while traversing road irregularities. IEEE Transactions on Neural Networks 19(9): 1549–1563
He, Y. Xiong, N., & Yang, Y. (2004). Data Transmission Rate Control in Computer Networks Using Neural Predictive Networks, ISPA 2004 (pp. 875–887).
Hu R. Q., Petr D. W. (2000) A predictive self-tuning fuzzy-logic feedback rate controller. IEEE/ACM Transactions on Networking 8(6): 689–696
Jagannathan, S. (2001). Control of a class of nonlinear systems using multilayered neural networks. In IEEE Transactions on Neural Networks 12, 5.
Jagannathan S., Galan G. (2003) A one-layer neural network controller with preprocessed inputs for autonomous underwater vehicles. IEEE Transactions on Vehicular Technology 52(5): 1342–1355
Jagannathan S., Talluri J. (2002) Adaptive predictive congestion control of high-speed networks. IEEE Transactions on Broadcasting 48(2): 129–139
Jain R., Kalyanaraman S., Fahmy S., Goyal R. (1996) Source behavior for ATM ABR traffic management: An explanation. IEEE Communication Magazine 34(11): 50–57
Keshav S. (1991) A control-theoretic approach to flow control. Proceedings of ACM SIGCOMM’91 21(4): 3–15
Lin, W. W. K., Ip, M. T. W. et al. A neural network based proactive buffer control approach for better reliability and performance for object-based internet applications, In International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA 2001), Las Vegas, Nevada, USA, CSREA Press.
Murata N., Yoshizawa S., Amari S. I. (1994) Network information criterion—determining the number of hidden units for an artificial neural network model. IEEE Transactions on Neural Networks 5(6): 865–872
Park J. H. (2006) A new stability analysis of delayed cellular neural networks. Applied Mathematics and Computation 181(1): 200–205
Scarselli F., Tsoi A. C. (1998) Universal approximation using FNN: A survey of some existing methods and some new results. Neural Networks 11: 15–37
Simon H. (1998) Neural networks: A comprehensive foundation, 2nd edn. Prentice Hall, New York
Wang D. H., Lee N. K., Dillon T. S. (2003) Extraction and optimization of fuzzy protein sequence classification rules using GRBF neural networks. Neural Information Processing—Letters and Reviews 1(1): 53–59
Xiong, N., Defago, X., Jia, X., Yang, Y., & He, Y. (2006). Design and analysis of a self-tuning proportional and integral controller for active queue management routers to support TCP flows, INFOCOM.
Xiong, N., Tan, L., & Yang, Y. (2004). A novel congestion control algorithm using the BP neural network. Computer Enginnering, 30(24).
Yang C. Q., Reddy A. A. S. (1995) A taxonomy for congestion control algorithms in packet switching networks. IEEE Network Magazine 9(5): 34–45
Yu R., Wang D. H. (2003) Further study on structural properties of LTI singular systems under output feedback. Automatica 39: 685–692
Zuo W., Cai L. (2008) Adaptive-fourier-neural-network-based control for a class of uncertain nonlinear systems. IEEE Transactions on Neural Networks 19(10): 1689–1701
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This research is supported by the Ubiquitous Computing and Network (UCN) Project, Knowledge and Economy Frontier R & D Program of the Ministry of Knowledge Economy (MKE) in Korea and a result of subproject UCN 09C1-T2-10M. In addition, this research was partly supported by Hubei Provincial Department of Education, China (Grant No: Q20091704), and this fund was controlled by Ruhan He (College of Computer Science, Wuhan University of Science and Engineering, Wuhan, 430073, China).
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Xiong, N., Yang, L.T., Yang, Y. et al. Design of QoS in Intelligent Communication Environments Based on Neural Network. Wireless Pers Commun 56, 97–115 (2011). https://doi.org/10.1007/s11277-009-9880-3
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DOI: https://doi.org/10.1007/s11277-009-9880-3