Title: Cross-joint K-means and genetic scheme for internet of things sensor clustering

Authors: Malha Merah; Zibouda Aliouat; Hakim Mabed

Addresses: LRSD Laboratory, Computer Science Department, Faculty of Sciences, Ferhat Abbas University Setif 1, Setif, 19000, Algeria ' LRSD Laboratory, Computer Science Department, Faculty of Sciences, Ferhat Abbas University Setif 1, Setif, 19000, Algeria ' FEMTO-ST Institute/DISC, University of Bourgogne Franche-Comte, Montbeliard, 25200, France

Abstract: Machine learning paradigms have gained considerable interest in the field of wireless networks. In particular, clustering techniques have been widely used to implement efficient routing plans, manage massive data, and increase network performance. The K-means clustering algorithm is one of the algorithms that has attracted the most interest from the scientific community for clustering in various wireless network applications. However, this algorithm relies on parameters that need to be adjusted to achieve viable performance. Recently, a free-initialisation version of K-means has been proposed for pattern recognition. In this paper, we demonstrate the effectiveness of this method for cluster identification and energy conservation in wireless sensor networks. In addition, we present a bio-inspired scheme for efficient cluster head selection. The effectiveness of the proposed protocol is evaluated using the PyCharm simulation tool. The results show that the proposed protocol outperforms recent state-of-the-art solutions in terms of energy consumption and network lifetime.

Keywords: internet of things; IoT; wireless sensor networks; WSNs; clustering; K-means; unsupervised K-means; genetic algorithm; energy efficiency; network lifetime.

DOI: 10.1504/IJSNET.2024.138758

International Journal of Sensor Networks, 2024 Vol.45 No.1, pp.1 - 15

Received: 12 Dec 2023
Accepted: 15 Mar 2024
Published online: 30 May 2024 *

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