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International Journal of Sensors, Wireless Communications and Control


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

Research Article

An Efficient Fitness Function for Clustering of Wireless Sensor Networks

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

Volume 10, Issue 3, 2020

Page: [318 - 324] Pages: 7

DOI: 10.2174/2210327909666190408124107

Price: $65


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. Clustering is one of the methods that has been offered for resolving this issue. In this paper, we pursue evolutionary clustering and propose a new fitness function that har-nesses multiple propagation indices.

Methods: In this paper we develop an efficient fitness function by first selecting the best clusters, and then selecting the best attribution of cluster to clusters. The distance between the nodes and relevant cluster heads was used for the mathematical modelling necessary. In the end we develop the fitness function equation by using normalization of the raw data.

Results: Simulation results show improvement compared to previous fitness functions in clustering of the wireless sensor networks.

Keywords: Battery powered devices, clustering, energy consumption, fitness function, sensor networking, wireless.

Graphical Abstract
Vanhie-Van GJ, De Poorter E, Latré B, Moerman I, Demeester P. Real-life performance of protocol combinations for wireless sensor networks. 2010 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, Newport Beach, CA, California
Berger A, Pötsch A, Springer A. Real-time data collection in a spatially extended TDMA-based wireless sensor network. 2012 IEEE Topical Conference on Wireless Sensors and Sensor Networks, Santa Clara, CA, California
Kim M, Kyung C, Yi K. An energy management scheme for solar-powered wireless visual sensor networks toward uninterrupted operations. 2013 International SoC Design Conference (ISOCC), Busan, South Korea.
Karakaya A, Akleylek S. A survey on security threats and authentication approaches in wireless sensor networks. 2018 6th International Symposium on Digital Forensic and Security (ISDFS), Antalya, Turkey.
John A. Research challenges for wireless sensor networks SIGBED review: Special Issue on Embedded Sensor Networks and Wireless Computing, 2004; 1(2)
Ye W, Heidemann J, Estrin D. An energy-efficient mac protocol for wireless sensor networks. Proceedings of the 21st International Annual Joint Conference of the IEEE Computer and Communications Societies (IN- FOCOM 2002), New York, NY, USA.
Halkes G, Dam TV, Langendoen K. Comparing energy-saving MAC protocols for wireless sensor networks. Mob Netw Appl 2005; 10(5): 783-91.
Lindsey S, Raghavendra C, Sivalingam KM. Data gathering algorithms in sensor networks using energy metrics. IEEE Trans Parallel Distrib Syst 2002; 13(9): 924-35.
Tang X, Xu J. Extending network lifetime for precision constrained data aggregation in wireless sensor networks. Proceedings IEEE INFOCOM 2006. 25th IEEE International Conference on Computer Communications, Barcelona, Spain.
Heinzelman W, Chandrakasan A, Balakrishnan H. Energy efficient communication protocol for wireless micro- sensor networks. Proceedings of the Hawaii International Conference on System Science. Maui, Hawaii. 2000.
Wang Q, Hempstead M, Yang W. A realistic power consumption model for wireless sensor network devices. Proceedings of IEEE 3rd Annual Communications Society on Sensor and Ad Hoc Communications and Networks.(SECON), Reston, VA, USA, 2006
Karenos K, Kalogeraki V, Krishnamurthy S. Cluster-based congestion control for sensor networks. ACM Trans Sens Netw 2008; 4: 1-39.
Hosseinpour A. MSc Thesis, " Improving fitness functions for evolutionary algorithms in wireless sensor networks and presenting new fitness functions for clustering in such networks via genetic algorithm, 2011.
Hussain S, Matin AW, Islam O. Genetic algorithm for hierarchical wireless sensor networks. J Netw 2007; 2(5): 87-97.
D. Karaboga, S. Okdem, C. Ozturk. “ Cluster Based Wireless Sensor Network Routings using Artificial Bee Colony Algorithm ”, 978-1-4244-7107-2/10/$26.00 ©2010 IEEE.
H. Seo, S. Oh, C. Lee. Evolutionary Genetic Algorithm for Efficient Clustering of Wireless Sensor Networks

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