CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Fuzzy Modeling for Wireless Sensor Networks

by A.H. Mohamed
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 138 - Number 13
Year of Publication: 2016
Authors: A.H. Mohamed
10.5120/ijca2016909054

A.H. Mohamed . Fuzzy Modeling for Wireless Sensor Networks. International Journal of Computer Applications. 138, 13 ( March 2016), 29-33. DOI=10.5120/ijca2016909054

@article{ 10.5120/ijca2016909054,
author = { A.H. Mohamed },
title = { Fuzzy Modeling for Wireless Sensor Networks },
journal = { International Journal of Computer Applications },
issue_date = { March 2016 },
volume = { 138 },
number = { 13 },
month = { March },
year = { 2016 },
issn = { 0975-8887 },
pages = { 29-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume138/number13/24442-2016909054/ },
doi = { 10.5120/ijca2016909054 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:39:38.403711+05:30
%A A.H. Mohamed
%T Fuzzy Modeling for Wireless Sensor Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 138
%N 13
%P 29-33
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Applications of the wireless sensor networks have widely increased in recent years. However, a lot of work has been developed to improve the performance of these wireless sensor networks. But, there are some main limitations till now due to complexity appeared for maximizing the lifetime, dealing with the noisy data, having a balance of the node loading, speeding up the transmission process of wireless sensor networks (WSNs). The present work proposes a new routing technique that has used the fuzzy modeling to overcome these drawbacks of WSNs. Proposed fuzzy technique seeks to determine the optimal route path from source to destination so that the energy consumption is balanced and minimized. The proposed fuzzy routing technique is applied for a WSN used in radiated products' system. Then, the suggested system is compared with Traditional Low- Energy Adaptive Clustering Hierarchy (LEACH) commonly used protocol for WSNs. The obtained results show significant increase in the performance of the WSNs. However, the proposed system has proved its well suitability for the real-time applications.

References
  1. Akyildiz F., (2002), “A Survey on Sensor Networks,” Computer Journal of IEEE Communications, Magazine, 40 (8):102-114.
  2. Zhang, Y.; Li, W. (2012); Modeling and energy consumption evaluation of a stochastic wireless sensor network, Eurasip J Wirel Comm, ISSN 1687-1499, 2012(1): 1-11.
  3. Hadjila, M., Guyennet, H., Feham, M., (2013), Energy-Efficient in wireless sensor networks using fuzzy C-Means clustering approach, International Journal of Sensors and Sensor Networks, 1(2) : 21-26
  4. Huruiala, P.-C. Urzica, A. and Gheorghe, L. , (2010), "Hierarchical routing protocol based on evolutionary algorithms for Wireless Sensor Networks," in Proc. 9th Roedunet Int. Conf. (RoEduNet), 2010, pp. 387-392.
  5. Taruna, S., Kusum Jain and Purohit, G.N., (2011), “Power Efficient Clustering Protocol (PECP)- Heterogeneous Wireless Sensor Network,” International Journal of Wireless & Mobile Networks (IJWMN) vol.3, no.3, June 2011.
  6. Zhang, Y. Q. and Wei, L. , (2010) "Improving the LEACH protocol for wireless sensor networks," in Proc. IET-WSN Wireless Sensor Network IET Int. Conf, 2010, pp. 355-359.
  7. Godbole, V., (2012), Performance Analysis of Clustering Protocol Using Fuzzy Logic for Wireless Sensor Network, IAES International Journal of Artificial Intelligence (IJ-AI), 1(3): 103-111.
  8. H. Bagci and A. Yazici, "An energy aware fuzzy unequal clustering algorithm for wireless sensor networks," in Proc. IEEE Int Fuzzy Systems (FUZZ) Conf, 2010, pp. 1-8.
  9. Akyildiz F., (2002), “A Survey on Sensor Networks,” Computer Journal of IEEE Communications, Magazine, 40 (8):102-114.
  10. Heinzelman, A. Chandrakasan and H. Balakrishnan, An Application-Specific Protocol Architecture For Wireless Microsensor Networks, in IEEE Transactions on Wireless communications, pp. 660 -670, Oct 2002.
  11. Lee, J.-S. and Cheng, W.-L. , (2012) , "Fuzzy-Logic-Based Clustering Approach for Wireless Sensor Networks Using Energy Predication," IEEE Sensors Journal, 12 (9):2891-2897.
  12. Al-Jarrah, R., A. Al-Jarrah, M., Roth, H., (2016), WSNs and on-Board Visual Fuzzy Servoing on Blimp Robot for Tracking Purposes, International Journal of Computer and Communication Engineering, 5(3):215-221.
  13. Samarasooriya V. N. S. and Varshney, P. K. (2000), “A fuzzy modeling approach to decision fusion under uncertainty,” Fuzzy Sets Syst., vol. 114,pp. 59–69.
  14. Gharajeh, M.S. , (2014), Determining the State of the Sensor Nodes Based on Fuzzy Theory in WSNs, International Journal of Computers Communications & Control, 9(4):419-429.
  15. Manjunatha, P., Verma, A. and Srividya, A., (2008), “Multi-sensor data fusion in cluster based wireless sensor networks using fuzzy logic method,” in Industrial and Information Systems, 2008.ICIIS2008.IEEE Region 10 and the Third international Conference on, 2008, pp. 1 –6.
  16. Chang H., Leandros S., and Tassiulas L., “Maximum Lifetime Routing in Wireless Sensor Networks,” Computer Journal of IEEE/ACM Transactions on Networking, vol. 12, no. 4, pp. 609-619, 2004.
  17. Gu, S. , Yue, Y., Maple, C. and Wu, C. , (2012), “Fuzzy logic based localization in Wireless Sensor Networks for disaster environments,” Proc. Int. Conf. on Automation and Computing, pp. 1-5.
  18. http://www.dma.fi.upm.es
Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy Modeling Wireless Sensor Networks.

Powered by PhDFocusTM