Cargando…
K-Means Clustering-Based Safety System in Large-Scale Industrial Site Using Industrial Wireless Sensor Networks
A large number of workers and heavy equipment are used in most industrial sizes, and the prevention of safety accidents is one of the most important issues. Therefore, although a number of systems have been proposed to prevent accidents, existing studies assume that workers are gathered in some area...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9028440/ https://www.ncbi.nlm.nih.gov/pubmed/35458881 http://dx.doi.org/10.3390/s22082897 |
_version_ | 1784691618256781312 |
---|---|
author | Seo, Dongyeong Kim, Sangdae Oh, Seungmin Kim, Sang-Ha |
author_facet | Seo, Dongyeong Kim, Sangdae Oh, Seungmin Kim, Sang-Ha |
author_sort | Seo, Dongyeong |
collection | PubMed |
description | A large number of workers and heavy equipment are used in most industrial sizes, and the prevention of safety accidents is one of the most important issues. Therefore, although a number of systems have been proposed to prevent accidents, existing studies assume that workers are gathered in some areas. These assumptions are not suitable for large-scale industrial sites in which workers form as a group and work in a large area. In other words, in a large-scale industrial site, existing schemes are unsuitable for the timely notifying of warnings of threats, and excessive energy is consumed. Therefore, we propose a k-means clustering-based safety system for a large-scale industrial site. In the proposed scheme, workers deployed over a large area are divided into an appropriate number of groups, and threat notification is delivered by a multicasting tree toward each cluster. The notification to workers is delivered through local flooding in each cluster. The simulation results show that the system is able to deliver the notification within a valid time, and it is energy efficient compared to the existing scheme. |
format | Online Article Text |
id | pubmed-9028440 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-90284402022-04-23 K-Means Clustering-Based Safety System in Large-Scale Industrial Site Using Industrial Wireless Sensor Networks Seo, Dongyeong Kim, Sangdae Oh, Seungmin Kim, Sang-Ha Sensors (Basel) Article A large number of workers and heavy equipment are used in most industrial sizes, and the prevention of safety accidents is one of the most important issues. Therefore, although a number of systems have been proposed to prevent accidents, existing studies assume that workers are gathered in some areas. These assumptions are not suitable for large-scale industrial sites in which workers form as a group and work in a large area. In other words, in a large-scale industrial site, existing schemes are unsuitable for the timely notifying of warnings of threats, and excessive energy is consumed. Therefore, we propose a k-means clustering-based safety system for a large-scale industrial site. In the proposed scheme, workers deployed over a large area are divided into an appropriate number of groups, and threat notification is delivered by a multicasting tree toward each cluster. The notification to workers is delivered through local flooding in each cluster. The simulation results show that the system is able to deliver the notification within a valid time, and it is energy efficient compared to the existing scheme. MDPI 2022-04-09 /pmc/articles/PMC9028440/ /pubmed/35458881 http://dx.doi.org/10.3390/s22082897 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Seo, Dongyeong Kim, Sangdae Oh, Seungmin Kim, Sang-Ha K-Means Clustering-Based Safety System in Large-Scale Industrial Site Using Industrial Wireless Sensor Networks |
title | K-Means Clustering-Based Safety System in Large-Scale Industrial Site Using Industrial Wireless Sensor Networks |
title_full | K-Means Clustering-Based Safety System in Large-Scale Industrial Site Using Industrial Wireless Sensor Networks |
title_fullStr | K-Means Clustering-Based Safety System in Large-Scale Industrial Site Using Industrial Wireless Sensor Networks |
title_full_unstemmed | K-Means Clustering-Based Safety System in Large-Scale Industrial Site Using Industrial Wireless Sensor Networks |
title_short | K-Means Clustering-Based Safety System in Large-Scale Industrial Site Using Industrial Wireless Sensor Networks |
title_sort | k-means clustering-based safety system in large-scale industrial site using industrial wireless sensor networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9028440/ https://www.ncbi.nlm.nih.gov/pubmed/35458881 http://dx.doi.org/10.3390/s22082897 |
work_keys_str_mv | AT seodongyeong kmeansclusteringbasedsafetysysteminlargescaleindustrialsiteusingindustrialwirelesssensornetworks AT kimsangdae kmeansclusteringbasedsafetysysteminlargescaleindustrialsiteusingindustrialwirelesssensornetworks AT ohseungmin kmeansclusteringbasedsafetysysteminlargescaleindustrialsiteusingindustrialwirelesssensornetworks AT kimsangha kmeansclusteringbasedsafetysysteminlargescaleindustrialsiteusingindustrialwirelesssensornetworks |