ARSH_FATI_a_Novel_Meta_heuristic_Revised.pdf (999.26 kB)
Download file

ARSH-FATI a Novel Metaheuristic for Cluster Head Selection in Wireless Sensor Networks

Download (999.26 kB)
journal contribution
posted on 01.07.2020, 10:36 by Haider Ali, Umair Ullah Tariq, Mubashir Hussain, Liu Lu, John Panneerselvam, Xiaojun Zhai
Wireless sensor network (WSN) consists of a large number of sensor nodes distributed over a certain target area. The WSN plays a vital role in surveillance, advanced healthcare, and commercialized industrial automation. Enhancing energy-efficiency of the WSN is a prime concern because higher energy consumption restricts the lifetime (LT) of the network. Clustering is a powerful technique widely adopted to increase LT of the network and reduce the transmission energy consumption. In this article (LT) we develop a novel ARSH-FATI-based Cluster Head Selection (ARSH-FATI-CHS) algorithm integrated with a heuristic called novel ranked-based clustering (NRC) to reduce the communication energy consumption of the sensor nodes while efficiently enhancing LT of the network. Unlike other population-based algorithms ARSH-FATI-CHS dynamically switches between exploration and exploitation of the search process during run-time to achieve higher performance trade-off and significantly increase LT of the network. ARSH-FATI-CHS considers the residual energy, communication distance parameters, and workload during cluster heads (CHs) selection. We simulate our proposed ARSH-FATI-CHS and generate various results to determine the performance of the WSN in terms of LT. We compare our results with state-of-the-art particle swarm optimization (PSO) and prove that ARSH-FATI-CHS approach improves the LT of the network by ∼25% .

History

Citation

IEEE Systems Journal, 2020, https://doi.org/10.1109/JSYST.2020.2986811

Author affiliation

School of Informatics

Version

AM (Accepted Manuscript)

Published in

IEEE Systems Journal

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

issn

1932-8184

eissn

1937-9234

Copyright date

2020

Available date

01/07/2020

Language

en

Publisher version

https://ieeexplore.ieee.org/document/9103563