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International Journal of Sensors, Wireless Communications and Control

Editor-in-Chief

ISSN (Print): 2210-3279
ISSN (Online): 2210-3287

Research Article

Energy Efficient Clustering for Wireless Sensor Network Using Hybrid Genetic - Bees Algorithm

Author(s): Atiieh Hoseinpour , Mojtaba Jafari Lahijani and Javad Kazemitabar*

Volume 9, Issue 2, 2019

Page: [253 - 259] Pages: 7

DOI: 10.2174/2210327908666181107101620

Price: $65

Abstract

Background & Objective: A sensor network is composed of a large number of sensor nodes that are deployed to perform measurement and/or command and control in a field. Sensor nodes are battery powered devices and replacement or recharging of their batteries may not be feasible. One of the major challenges with sensory wireless networks is excessive energy consumption in nodes.

Methods: Clustering is one of the methods that have been offered for resolving this issue. Clustering provides a means to reduce the number of wireless communications that greatly increase the life expectancy of the network. In this paper, we propose a novel hybrid genetic-bees algorithm that harnesses an efficient fitness function. This hybrid algorithm can smartly divide the sensor nodes into clusters and thus reduce the energy consumption.

Results and Conclusion: The simulation results show that this algorithm can simultaneously process multiple points in the search grid and also converge to the optimal solution in reasonable time.

Keywords: Battery powered devices, clustering, excessive energy consumption, hybrid genetic-bees algorithm, sensor, wireless communication.

Graphical Abstract
[1]
Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E. A survey on sensor networks. IEEE Commun Mag 2002; 40(8): 102-14.
[2]
Heinzelman WR, Chandrakasan A, Balakrishnan H. Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd annual Hawaii international conference on system sciences Wailea, Maui, Hawaii: IEEE. 2000; p. Jan 4;. 10.
[3]
Wang Q, Hempstead M, Yang W. A realistic power consumption model for wireless sensor network devices. In: 2006 3rd annual IEEE communications society on sensor and Ad-hoc communications and networks Reston, VA, USA: IEEE . 2006; 1: pp. 286-95.
[4]
Jin SM, Zhou M, Wu AS. Sensor network optimization using a genetic algorithm. In: Proceedings of the 7th world multi conference on systemic, cybernetics and informatics. 2003.
[5]
Hussain S, Matin AW, Islam O. Genetic algorithm for energy efficient clusters in wireless sensor networks. In: Fourth international conference on information technology (ITNG’07) Las Vegas, NV, USA: IEEE 2007 Apr 2. 147-54.
[6]
Liu JL, Ravishankar CV. LEACH-GA: Genetic algorithm-based energy-efficient adaptive clustering protocol for wireless sensor networks. Int J Mach Learn Comput 2011; 1(1): 79.
[7]
Hoseinpour A. Improvement of evolutionary algorithm’s fitness function and clustering WSN using a novel fitness function Master’s thesis, University of science and technology of Mazandaran, Babol, June 2012.
[8]
Sheta A, Turabieh H. A comparison between genetic algorithms and sequential quadratic programming in solving constrained optimization problems. ICGST Int J Artif Intell Mach Learn 2006; 6(1): 67-74.
[9]
Goldberg DE. Genetic algorithms in search, optimization, and machine learning. Boston, MA: Addison-Wesley 1989.
[10]
Jones K. Comparison of genetic algorithm and particle swarm optimization. In: International conference on computer systems and technologies-CompSys Tech. 2005.pages IA1-IA8, Bulgaria, 2005.
[11]
Pham DT, Ghanbarzadeh A, Koc E, Otri S, Rahim S, Zaidi M. The BEES algorithm. Technical Note, Manufacturing Engineering Centre: Cardiff University UK. 2005.
[12]
Karaboga D. An idea based on honey bee swarm for numerical optimization Technical report-tr06, Erciyes university, engineering faculty, computer engineering department; 2005.Oct..
[13]
Jha SK, Eyong EM. An energy optimization in wireless sensor networks by using genetic algorithm. Telecomm Syst 2018; 67(1): 113-21.
[14]
Zangeneh MA, Ghazvini M. An energy-based clustering method for WSNs using artificial bee colony and genetic algorithm. In: 2017 2nd conference on swarm intelligence and evolutionary computation (CSIEC) Kerman, Iran: IEEE. 2017.Mar 7; 35-41.
[15]
Kaur S, Mir RN. Clustering in wireless sensor networks- a survey. Int J Comp Netw Inf Secur 2016; 6: 38-51.
[16]
Aggarwal R, Mittal A, Kaur R. Various optimization techniques used in wireless sensor networks. Int Res J Eng Technol 2016; 3(6): 2085-90.
[17]
Nan G, Li M. Evolutionary based approaches in wireless sensor networks: A survey. In: 2008 Fourth International Conference on Natural Computation Jinan, China: IEEE 2008; 5. 217-2.

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