CFP last date
22 April 2024
Reseach Article

Fuzzy based Distributed Cluster Formation and Route Construction in Wireless Sensor Networks

by Melaku Tamene, Kuda Nageswara Rao
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 140 - Number 5
Year of Publication: 2016
Authors: Melaku Tamene, Kuda Nageswara Rao
10.5120/ijca2016909300

Melaku Tamene, Kuda Nageswara Rao . Fuzzy based Distributed Cluster Formation and Route Construction in Wireless Sensor Networks. International Journal of Computer Applications. 140, 5 ( April 2016), 21-27. DOI=10.5120/ijca2016909300

@article{ 10.5120/ijca2016909300,
author = { Melaku Tamene, Kuda Nageswara Rao },
title = { Fuzzy based Distributed Cluster Formation and Route Construction in Wireless Sensor Networks },
journal = { International Journal of Computer Applications },
issue_date = { April 2016 },
volume = { 140 },
number = { 5 },
month = { April },
year = { 2016 },
issn = { 0975-8887 },
pages = { 21-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume140/number5/24591-2016909300/ },
doi = { 10.5120/ijca2016909300 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:41:29.801754+05:30
%A Melaku Tamene
%A Kuda Nageswara Rao
%T Fuzzy based Distributed Cluster Formation and Route Construction in Wireless Sensor Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 140
%N 5
%P 21-27
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The efficiency of cluster formation has a sound effect on limiting battery lifetime in cluster based wireless sensor networks. Fuzzy based decision support system enables node to make evaluation and pass a soft decision on the state of being configured as normal or cluster leader without the presence of precise information about different clustering parameters. In this paper, a distributed fuzzy based cluster formation is presented in which the set of appropriate cluster heads are elected to setup clusters and optimal routes toward the base station are constructed based on the candidate gateway nodes. Initially, the base station partitions the network into different tiers and then nodes define themselves to the corresponding tier. The minimum distance from the border of corresponding tier and energy level limit the selection probability of node to become cluster head or gateway node. The fuzzy logic toolbox is developed in C++ and integrated with OMNeT++ simulation platform to implement the protocol and experimental results reveal that the proposed protocol prolongs the network lifetime compared to LEACH-C and CHEF protocols.

References
  1. Akyildiz, I.F., Weilian, S., Sankarasubramaniam, Y. and Cayirci, E. 2002. A survey on sensor networks. IEEE Communications Megazine, vol.40, pp.102-114.
  2. Asada, G., Dong, M., Lin, T.S., Newberg, F., Pottie, G., Kaiser, W.J. and Marcy, H.O. 1998. Wireless integrated network sensors: Low power systems on a chip. In Proceedings of the 24th European Solid-State Circuits Conference (ESSCIRC), pp. 9–16.
  3. Sohrabi, K., Ailawadhi, V., Gao, J. and Pottie, G.J. 2000. Protocols for self-organization of a wireless sensor network. IEEE Personal Communications Magazine, vol. 7, no. 5, pp. 16–27.
  4. Anastasi, G., Conti, M., Francesco, M.D. and Passarella, A. 2009. Energy conservation in wireless sensor networks: A survey. Ad Hoc Networks, vol.7, no.7, pp. 537-568.
  5. Martinez, D., Blanes, F., Simo, J. and Crespo, A. 2008. Wireless sensor and actuator networks: characterization and case study for confined spaces health care applications. In Proceedings of International Conference on Computer Science and Information Technology (IMCSIT), pp.687-693.
  6. Reinisch, C., kastner, W., Neugschwandtner, G. and Granzer, W. 2007. Wireless Technologies in home and building automation. In Proceedings of IEEE International Conference on Industrial Informatics, vol.1, pp.93-98.
  7. Ephremides, A. 2002. Energy concerns in wireless networks. IEEE Wireless Communications Magazine, vol.9, no. 4, pp. 48–59.
  8. Borges, L.M., Velez, F.J and Lebres, A.S. 2014. A survey on the characterization and classification of wireless sensor networks applications. IEEE Communications Surveys Tutorials, vol. 16, no. 14, pp. 1860 - 1890.
  9. Gungor, V.C. and Hancke, G.P. 2009. Industrial wireless sensor networks: Challenges, design principles, and technical approaches. IEEE Transactions on Industrial Electronics, vol. 56, no. 10, pp. 4258–4265.
  10. Abbasi, A.A. and Younis, M. 2007. A survey on clustering algorithms for wireless sensor networks. Computer Communications, vol.30, pp.2826-2841.
  11. Ai-hua, Q. 2012. A Research of clustering algorithms and its improvements in wireless sensor networks. In Proceedings of IEEE International Conference on Computer Science and Network Technology (ICCSNT), pp.1835-1838.
  12. Younis, O., Krunz, M. and Ramasubramanian, S. 2006. Node clustering in wireless sensor networks: Recent developments and deployment challenges. IEEE Network, vol. 20, no. 3, pp. 20–25.
  13. Heinzelman W., Chandrakasan, A. and Balakrishnan, H. 2000. Energy efficient communication protocol for wireless microsensor networks. In Proceedings of IEEE Hawaii International Conference on System Sciences, pp.3005-3014.
  14. Heinzelman W., Chandrakasan, A. and Balakrishnan, H. 2002. Application specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, vol. 1, no. 4, pp. 660–670.
  15. Li, C., Ye, M., Chen, G. and Wu, J. 2005. An energy-efficient unequal clustering mechanism for wireless sensor networks. In Proceedings of IEEE International Conference on Mobile Adhoc and Sensor Systems.
  16. Bagci, H. and Yazici, A. 2010. An energy aware fuzzy unequal clustering algorithm for wireless sensor networks. In Proceedings of IEEE International Conference on Fuzzy Systems (FUZZ), pp.1-8.
  17. Younis, O. and Fahmy, S. 2004. HEED: a hybrid, energy efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing, vol.3, No.4, pp.366–379.
  18. Lee, J. and Cheng, W. 2012. Fuzzy-logic-based clustering approach for Wireless sensor networks using energy Predication. IEEE Sensors Journal, vol. 12, no. 9, pp. 2891 - 2897.
  19. Kim, J., Park, S., Han, Y. and Chung, T. 2008. CHEF: Cluster head election mechanism using fuzzy logic in wireless sensor networks. In Proceedings of 10th International Conference on Advanced Communication Technology, pp. 654–659.
  20. Bagci, H. and Yazici, A. 2011. An improved fuzzy unequal clustering algorithm for wireless sensor network. In Proceedings of International Conference on Communications and Networking in China (CHINACOM), pp.245-250.
  21. Smithgall, D. 1998. Toward the 60 gm wireless phone. In Proceedings of the 1998 Radio and Wireless Conference (RAWCON).
  22. Rappaport, T. 1996. Wireless Communications: Principles and Practice. Englewood Cliffs, NJ:Prentice-Hall.
  23. OMNeT++ Network Simulator. [Online]. Available: http://www.omnetpp.org/.
Index Terms

Computer Science
Information Sciences

Keywords

Cluster head election fuzzy based cluster formation network lifetime wireless sensor networks.