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Fruchterman–Reingold Hexagon Empowered Node Deployment in Wireless Sensor Network Application
Internet of Things (IoT) and Big Data technologies are becoming increasingly significant parts of national defense and the military, as well as in the civilian usage. The proper deployment of large-scale wireless sensor network (WSN) provides the foundation for these advanced technologies. Based on...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9319457/ https://www.ncbi.nlm.nih.gov/pubmed/35890858 http://dx.doi.org/10.3390/s22145179 |
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author | Li, Jiahao Tao, Yuhao Yuan, Kai Tang, Rongxin Hu, Zhiming Yan, Weichao Liu, Shiyun |
author_facet | Li, Jiahao Tao, Yuhao Yuan, Kai Tang, Rongxin Hu, Zhiming Yan, Weichao Liu, Shiyun |
author_sort | Li, Jiahao |
collection | PubMed |
description | Internet of Things (IoT) and Big Data technologies are becoming increasingly significant parts of national defense and the military, as well as in the civilian usage. The proper deployment of large-scale wireless sensor network (WSN) provides the foundation for these advanced technologies. Based on the Fruchterman–Reingold graph layout, we propose the Fruchterman–Reingold Hexagon (FR-HEX) algorithm for the deployment of WSNs. By allocating edges of hexagonal topology to sensor nodes, the network forms hexagonal network topology. A comprehensive evaluation of 50 simulations is conducted, which utilizes three evaluation metrics: average moving distance, pair correlation diversion (PCD), and system coverage rate. The FR-HEX algorithm performs consistently, the WSN topologies are properly regulated, the PCD values are below 0.05, and the WSN system coverage rate reaches 94%. Simulations involving obstacles and failed nodes are carried out to explore the practical applicability of the FR-HEX algorithm. In general, the FR-HEX algorithm can take full advantage of sensors’ hardware capabilities in the deployment. It may be a viable option for some IoT and Big Data applications in the near future. |
format | Online Article Text |
id | pubmed-9319457 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93194572022-07-27 Fruchterman–Reingold Hexagon Empowered Node Deployment in Wireless Sensor Network Application Li, Jiahao Tao, Yuhao Yuan, Kai Tang, Rongxin Hu, Zhiming Yan, Weichao Liu, Shiyun Sensors (Basel) Article Internet of Things (IoT) and Big Data technologies are becoming increasingly significant parts of national defense and the military, as well as in the civilian usage. The proper deployment of large-scale wireless sensor network (WSN) provides the foundation for these advanced technologies. Based on the Fruchterman–Reingold graph layout, we propose the Fruchterman–Reingold Hexagon (FR-HEX) algorithm for the deployment of WSNs. By allocating edges of hexagonal topology to sensor nodes, the network forms hexagonal network topology. A comprehensive evaluation of 50 simulations is conducted, which utilizes three evaluation metrics: average moving distance, pair correlation diversion (PCD), and system coverage rate. The FR-HEX algorithm performs consistently, the WSN topologies are properly regulated, the PCD values are below 0.05, and the WSN system coverage rate reaches 94%. Simulations involving obstacles and failed nodes are carried out to explore the practical applicability of the FR-HEX algorithm. In general, the FR-HEX algorithm can take full advantage of sensors’ hardware capabilities in the deployment. It may be a viable option for some IoT and Big Data applications in the near future. MDPI 2022-07-11 /pmc/articles/PMC9319457/ /pubmed/35890858 http://dx.doi.org/10.3390/s22145179 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 Li, Jiahao Tao, Yuhao Yuan, Kai Tang, Rongxin Hu, Zhiming Yan, Weichao Liu, Shiyun Fruchterman–Reingold Hexagon Empowered Node Deployment in Wireless Sensor Network Application |
title | Fruchterman–Reingold Hexagon Empowered Node Deployment in Wireless Sensor Network Application |
title_full | Fruchterman–Reingold Hexagon Empowered Node Deployment in Wireless Sensor Network Application |
title_fullStr | Fruchterman–Reingold Hexagon Empowered Node Deployment in Wireless Sensor Network Application |
title_full_unstemmed | Fruchterman–Reingold Hexagon Empowered Node Deployment in Wireless Sensor Network Application |
title_short | Fruchterman–Reingold Hexagon Empowered Node Deployment in Wireless Sensor Network Application |
title_sort | fruchterman–reingold hexagon empowered node deployment in wireless sensor network application |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9319457/ https://www.ncbi.nlm.nih.gov/pubmed/35890858 http://dx.doi.org/10.3390/s22145179 |
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