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Reseach Article

Improving Congestion Performance in WSN by using Enhanced Algorithm

by P. S. Raghavendran, R. Asokan, V. Praveenkumar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 96 - Number 3
Year of Publication: 2014
Authors: P. S. Raghavendran, R. Asokan, V. Praveenkumar
10.5120/16774-6345

P. S. Raghavendran, R. Asokan, V. Praveenkumar . Improving Congestion Performance in WSN by using Enhanced Algorithm. International Journal of Computer Applications. 96, 3 ( June 2014), 18-23. DOI=10.5120/16774-6345

@article{ 10.5120/16774-6345,
author = { P. S. Raghavendran, R. Asokan, V. Praveenkumar },
title = { Improving Congestion Performance in WSN by using Enhanced Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { June 2014 },
volume = { 96 },
number = { 3 },
month = { June },
year = { 2014 },
issn = { 0975-8887 },
pages = { 18-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume96/number3/16774-6345/ },
doi = { 10.5120/16774-6345 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:20:47.218595+05:30
%A P. S. Raghavendran
%A R. Asokan
%A V. Praveenkumar
%T Improving Congestion Performance in WSN by using Enhanced Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 96
%N 3
%P 18-23
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Wireless sensor networks (WSNs) have expanded their monitoring and tracking applications in wide areas, such as military, medical, and aerospace fields. Although they are used in many important fields, their performance under harsh conditions still remains to be improved, especially when a WSN experience a congestion, the packets reaching the base station (BS) from the near-by node will be higher when compared with the packets delivered from the far-away node reaching the base station. Since the goal of WSN applications is to monitor the whole designated area, such unfairness is not suitable. In addition, the average latency during congestion is very long, failing to fulfil the data freshness requirement of WSN applications. To improve the performance the mostly FIFO (First-In, First-Out) technique is used. In this Technique packets reaching first will be delivered first and the packets coming from faraway node will reach lately than the nearby node hence they are delivered after delivering the nearby node packets. So during this process packets from faraway nodes will get losed more than the nearby nodes. To further improve the fairness performance, the single queue in each node is divided into multiple weighted sub-queues logically, and the packets in each Sub queue are forwarded based on its weight. By doing so the data receptions from other nodes at the BS get balanced. The simulation is done in Qualnet simulator. Both theoretical analysis and extensive experiments verify the performance improvement of our approach.

References
  1. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, "A survey on wireless sensor networks,"IEEE Communications Magazine, vol. 40, pp. 102–114, 2002.
  2. W. Li and J. Han, "Dynamic wireless sensor network parameters optimization adapting different node mobility," in Proc. of the IEEE Aerospace Conference, 2010.
  3. E. Reusens, W. Joseph, G. Vermeeren and J. Martens Ghent University Belgium (2012), "On-Body Measurement and Characterization of Wireless Communication Channel for Arm and Torso of Human".
  4. "Hybrid Congestion Control Protocol in Wireless Sensor Networks" JANG-PING SHEU, LI-JEN CHANG2 AND WEI-KAI HU Department of Computer Science National Tsing Hua University Hsinchu, 300 Taiwan.
  5. "An Ultra-Wideband Body Area Propagation Channel Model—From Statistics to Implementation" Andrew Fort, Student Member, IEEE, Claude Desset, Member, IEEE, Philippe De Doncker,Piet Wambacq, Member, IEEE, and Leo Van Biesen, Member, IEEE.
  6. "Priority-based Congestion Control in Wireless Sensor Networks" Chonggang Wang1, Kazem Sohraby1, Victor Lawrence, Bo Li, Yueming Hu Dept. Of Elec. Engi. , University of Arkansas, Fayetteville, AR 72701, USA Stevens Institute Technology, Hoboken, NJ 07030, USA Dept. of Computer Science, Hong Kong Univ. of Science & Tech. , Hong Kong.
  7. "On queueing with customer impatience until the beginning of service," Queueing Systems Theory and Applications, vol. 29, pp. 337–350, 1998.
  8. L. Schwiebert, S. Gupta, and J. Weinmann, "Research challenges in wireless networks of biomedical sensors," in Proceedings of the International Conference on Mobile Computing and Networking, 2001, pp. 151-165.
  9. Zdarsky, and L. Thiele, "A comprehensive worst-case calculus for wireless sensor networks with in-network processing," in Proc. of the IEEE Real-Time Systems Symposium (RTSS), Dec 2007.
  10. "Qualnet 4. 5 Network Emulation Interface Model Library" from Scalable Technologies 2008.
  11. Qualnet 5. 1 Advanced Model Library" from Scalable Network Technologies September 2012.
  12. O. M. Al-Kofahi and A. E. Kamal, "Network coding-based protection of many-to-one wireless flows," IEEE J. Sel. Areas Communication. , vol. 27,no. 5, pp. 797–813, 2009.
  13. V. N. Q. Bao, H. Y. Kong, and S. W. Hong, "Performance Analysis of MPAM and M-QAM with Selection Combining in Independent but Non-Identically Distributed Rayleigh Fading Paths," in Proc. VTC'08, pp. 1-5, Sep. 2008.
  14. M. W. Chiu, andW. R. Hawe, "A quantitative measure of fairness and discrimination for resource allocation in shared computer systems," Washington University, Tech. Rep. , 1984.
Index Terms

Computer Science
Information Sciences

Keywords

Wireless sensor network Queueing Latency Base station Qualnet.