Call for Paper - January 2024 Edition
IJCA solicits original research papers for the January 2024 Edition. Last date of manuscript submission is December 20, 2023. Read More

Reliable Communication Protocol for Wireless Sensor Networks by using Clustering and Cellular Learning Automata

Print
PDF
International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 89 - Number 6
Year of Publication: 2014
Authors:
Mojtaba Gorban Alizadeh
Mahmood Javadi
Ali Hosseinalipour
10.5120/15508-4281

Mojtaba Gorban Alizadeh, Mahmood Javadi and Ali Hosseinalipour. Article: Reliable Communication Protocol for Wireless Sensor Networks by using Clustering and Cellular Learning Automata. International Journal of Computer Applications 89(6):24-28, March 2014. Full text available. BibTeX

@article{key:article,
	author = {Mojtaba Gorban Alizadeh and Mahmood Javadi and Ali Hosseinalipour},
	title = {Article: Reliable Communication Protocol for Wireless Sensor Networks by using Clustering and Cellular Learning Automata},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {89},
	number = {6},
	pages = {24-28},
	month = {March},
	note = {Full text available}
}

Abstract

Limitation of energy consumption that impress lifetime of sensor network directly, is one of the major challenges in wireless sensor networks. In this paper, a confident communication protocol is presented that include low energy consumption for wireless sensor networks that dispense monotonous cargo of energy between sensors and increases network lifetime. This protocol is a cluster-Based one that originated from combination of DBS and clustering in wireless sensor networks by using cellular learning automata. Based on the proposed protocol, the cluster head nodes are identified in various phases based on parameters: 1) the amount of node energy 2) distance from the base station 3) the number of neighbors 4)the number of CH nodes in neighbors. Under this method, the percentage of reliability increase and energy consumption and communication delay between the sensors of wireless sensor network greatly reduced.

References

  • M. R. Meybodi, H. Beigy, "New Class of Learning Automata Based Scheme for Adaptation of Back propagation Algorithm Parameters", Proc. Of EUFIT-98, Sep. 7-10, Achen, Germany, pp. 339-344, 1998.
  • B. J. Oommen, D. C. Y. Ma, "Deterministic Learning Automata Solution to the Keyboard Optimization Problem", IEEE Trans. On Computers, Vol. 37, No. 1, pp. 2-3, 1988.
  • H. Beigy, M. R. Meybodi,"Optimization of Topology of Neural Networks Using Learning Automata", Proc. Of 3th Annual Int. Computer Society of Iran Computer Conf. CSICC-98, Tehran, Iran, pp. 417-428, 1999.
  • A. A. Hashim, S. Amir, p. Mars, "Application of Learning Automata to Data Compression, In Adaptive and Learning Systems", K. S. Narendra (Ed), New York: Plenum Press, pp. 229-234, 1986.
  • Heinzelman W. R. , A. P. Chandrakasan and H. Balakrishnan, "Energy-Efficient Communication Protocol for Wireless Microsensor Networks", Proc. of the 33rd IEEE International Conference on System Sciences, Honolulu, USA, Jan. 2000,pp. 1–10.
  • Handy M. J. , M. Haase and D. Timmermann, "Low Energy Adaptive Clustering Hierarchy with Deterministic Cluster-Head Selection", Proc. of 4th IEEE International Conference on Mobile and Wireless Communications Networks, Stockholm, Sweden, 2002, pp. 368-372.
  • Heinzelman W. R. , A. P. Chandrakasan and H. Balakrishnan, "An Application-Specific Protocol Architecture for Wireless Microsensor Networks", IEEE Transactions on Wireless Communications, vol. 1, no. 4, Oct. 2002, pp. 60-670.
  • Farajzadeh, N. and Meybodi, M. R. , "Learning Automata-based Clustering Algorithm for Sensor Networks", Proceedings of 12 th Annual CSI Computer Conference of Iran, Shahid Beheshti University, Tehran, Iran, pp. 780-787 , Feb. 20-22 ,2007.
  • Esnaashari, M. and Meybodi, M. R. , "A Cellular Learning Automata based Clustering Algorithm for Wireless Sensor Networks", Sensor Letters, 2008.
  • N. Aminit. Fazeli, S. G. Miremadi, M. T. Manzuri, "Distance-Based Segmentation: An Energy-Efficient Clustering Hierarchy for Wireless Microsensor Networks", IEEE Communication Networks and Services Research, 2007. CNSR '07. Fifth Annual Conference.