Rate Optimization Congestion Control Protocol for Wireless Sensor Networks

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Abstract:

Congestion is an essential problem in wireless sensor networks. In view of resolving the problem of congestion, a novel congestion control algorithm is proposed based on rate optimization. It performs the estimation of congestion level in the cluster and between the clusters based on the distribution of queue length of the node, and uses the maximum utility function to optimize the sending rate of source nodes. In addition, the algorithm adopts the cluster structure to prolong the system lifetime. The simulation results show that ROCC can prevent effectively and alleviate the congestion, and dynamic adjust the sending rate of source nodes while controlling congestion.

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Periodical:

Advanced Materials Research (Volumes 311-313)

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1642-1647

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August 2011

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© 2011 Trans Tech Publications Ltd. All Rights Reserved

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[1] Sun Limin, Li Bo, and Zhou Xinyun, A Survey of Congestion Control Technology for Wireless Sensor Networks (In Chinese) [J], Journal of Computer Research and Development, 2008, 45(1):63-72.

Google Scholar

[2] C.T. Wan, S.B. Eisenman, and A.T. Campbell, CODA: Congestion detection and avoidance in sensor networks[C], in Proceedings of ACM Sensys'03, Nov.5-7, 2003, Los Angeles, California, USA.

Google Scholar

[3] B.Hull, K.Jamieson, and H.Balakrishnan, Mitigating congestion in wireless sensor networks[C], in Proceedings of ACM Sensys'04, Nov.3-5, 2004, Baltimore, Maryland, USA.

Google Scholar

[4] C. Wang, K.Sohraby, V.Lawrence, and B.Li, Priority-based congestion control in wireless sensor networks[C], Accepted to appear in the IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC2006), June 5-7, 2006, Taichung, Taiwan

DOI: 10.1109/sutc.2006.1636155

Google Scholar

[5] Kyriakos Karenos, Vana Kalogeraki, Srikanth V Krishnamurthy. Cluster-based congestion control for supporting multiple classes of traffic in sensor networks [C]. The 2nd IEEE Workshop on EmNetS-II, Sydney,2005.

DOI: 10.1109/emnets.2005.1469105

Google Scholar

[6] Shenker S. Fundamental design issues for the future internet [J]. IEEE Journal of Select Areas Communication, 1995, 13:1176-1188.

DOI: 10.1109/49.414637

Google Scholar

[7] Kelly F P, Maulloo A K, Tan D K H. Rate Control in Communication Networks: Shadow prices, Proportional Fairness and Stability [J]. Journal of the Operational Research Society, 1998, 49(3): 237-252.

DOI: 10.1057/palgrave.jors.2600523

Google Scholar

[8] UCN/LBL/VINT: Network simulator-ns2. http://www.isi.edu.nsnam/ns/

Google Scholar

[9] http://www.dcc.ufla.br/infocomp/artigos/v6/3/art02.pdf

Google Scholar

[10] Kelly F P, Maulloo A K, Tan D K H. Rate Control in Communication Networks: Shadow prices, Proportional Fairness and Stability [J]. Journal of the Operational Research Society, 1998, 49(3): 237-252.

DOI: 10.1057/palgrave.jors.2600523

Google Scholar

[11] Alpean T, Basar T, A game-theoretic framework for congestion control in general topology networks[C]. In Proc. 41st IEEE Conference on Decision and Control, Las Vegas, Nevada, December 2002.

DOI: 10.1109/cdc.2002.1184680

Google Scholar

[12] Low S H, Lapsley D E. Optimization flow control, I: Basic algorithm and convergence[C]. IEEE/ACM Trans. On Networking, 1999,7(9): 861-874.

DOI: 10.1109/90.811451

Google Scholar