Clustering based on Hybridization of Genetic Algorithm and Improved K-Means (GA-IKM) in an IoT Network

The continuous development of Internet infrastructures and the evolution of digital electronics, particularly Nano-computers, are making the Internet of Things (IoT) emergent. Despite the progress, these IoT objects suffer from a crucial problem which is their limited power supply. IoT objects are often deployed as an ad-hoc network. To minimize their consumption of electrical energy, clustering techniques are used. In this paper, a centralized clustering algorithm with single-hop routing based on a genetic algorithm and Improved k-means is proposed. The proposed approach is compared with the LEACH, K-means and OKmeans algorithms. Simulation results show that the proposed algorithm performs well in terms of network lifetime and energy consumption.

Authors

Publication Details

Journal
International Journal of Wireless & Mobile Networks
Published
2024-12-28
DOI
https://doi.org/10.5121/ijwmn.2024.16602
Primary Topic
Wireless Sensor Networks and IoT
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Clustering based on Hybridization of Genetic Algorithm and Improved K-Means (GA-IKM) in an IoT Network

Nadia Ben Azzouna, Moez Elarfaoui
International Journal of Wireless & Mobile Networks
Wireless Sensor Networks and IoT
article

Clustering based on Hybridization of Genetic Algorithm and Improved K-Means (GA-IKM) in an IoT Network

Nadia Ben Azzouna, Moez Elarfaoui
article en

Abstract

The continuous development of Internet infrastructures and the evolution of digital electronics, particularly Nano-computers, are making the Internet of Things (IoT) emergent. Despite the progress, these IoT objects suffer from a crucial problem which is their limited power supply. IoT objects are often deployed as an ad-hoc network. To minimize their consumption of electrical energy, clustering techniques are used. In this paper, a centralized clustering algorithm with single-hop routing based on a genetic algorithm and Improved k-means is proposed. The proposed approach is compared with the LEACH, K-means and OKmeans algorithms. Simulation results show that the proposed algorithm performs well in terms of network lifetime and energy consumption.

International Journal of Wireless & Mobile NetworksVol. 16(6)
Openalex Percentile: Top 69%
Wireless Sensor Networks and IoT
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.