Research Repository

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

Ali, Haider and Tariq, Umair Ullah and Hussain, Mubashir and Lu, Liu and Panneerselvam, John and Zhai, Xiaojun (2021) 'ARSH-FATI a Novel Metaheuristic for Cluster Head Selection in Wireless Sensor Networks.' IEEE Systems Journal, 15 (2). pp. 2386-2397. ISSN 1932-8184

ARSH_FATI_a_Novel_Meta_heuristic_Revised.pdf - Accepted Version

Download (1MB) | Preview


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% .

Item Type: Article
Uncontrolled Keywords: ARSH-FATI; clustering; cluster head (CH); lifetime; sensor nodes; wireless sensor network (WSN)
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 05 Jun 2020 20:14
Last Modified: 23 Sep 2022 19:40

Actions (login required)

View Item View Item