Welcome to the InfoSci Platform
Reference Hub2
Minimizing the Energy Consumption of WSN Using Noble SMOWA-GA Algorithm

Minimizing the Energy Consumption of WSN Using Noble SMOWA-GA Algorithm

Sudip Kumar De, Avishek Banerjee, Koushik Majumder, Samiran Chattopadhyay
Copyright: © 2022 |Volume: 13 |Issue: 1 |Pages: 22
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781799885405|DOI: 10.4018/IJAMC.298313
Cite Article Cite Article

MLA

De, Sudip Kumar, et al. "Minimizing the Energy Consumption of WSN Using Noble SMOWA-GA Algorithm." IJAMC vol.13, no.1 2022: pp.1-22. http://doi.org/10.4018/IJAMC.298313

APA

De, S. K., Banerjee, A., Majumder, K., & Chattopadhyay, S. (2022). Minimizing the Energy Consumption of WSN Using Noble SMOWA-GA Algorithm. International Journal of Applied Metaheuristic Computing (IJAMC), 13(1), 1-22. http://doi.org/10.4018/IJAMC.298313

Chicago

De, Sudip Kumar, et al. "Minimizing the Energy Consumption of WSN Using Noble SMOWA-GA Algorithm," International Journal of Applied Metaheuristic Computing (IJAMC) 13, no.1: 1-22. http://doi.org/10.4018/IJAMC.298313

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

In this paper, the authors have concentrated on the practical application of optimization problems related to the minimization of the energy consumption of WSN. Here a noble algorithm called Self-adaptive Multi-Objective Weighted Approach-Genetic Algorithm (SMOWA-GA) is proposed to resolve the optimization problem. A multi-objective optimization problem was chosen as the subject of this research. The main objective of the paper is to propose and apply different WSN node deployment strategies to design an efficient Wireless Sensor Network to minimize the energy consumption of the whole WSN. The statistical analysis also has been carried out on the obtained data of the optimization techniques. To analyze the obtained result a statistical tool, Wilcoxon rank-sum test has been used. The Wilcoxon rank-sum test assists in determining whether the population chosen for the experiment (SMOWA-GA) is accurate. The statistical analysis also will help the reader to gather a detailed analysis of obtained data from the Multi-objective energy-efficient optimization problem.