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A Collaborative Data Collection Scheme Based on Optimal Clustering for Wireless Sensor Networks
In recent years, energy-efficient data collection has evolved into the core problem in the resource-constrained Wireless Sensor Networks (WSNs). Different from existing data collection models in WSNs, we propose a collaborative data collection scheme based on optimal clustering to collect the sensed...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6111701/ https://www.ncbi.nlm.nih.gov/pubmed/30071592 http://dx.doi.org/10.3390/s18082487 |
_version_ | 1783350711231184896 |
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author | Li, Guorui Chen, Haobo Peng, Sancheng Li, Xinguang Wang, Cong Yu, Shui Yin, Pengfei |
author_facet | Li, Guorui Chen, Haobo Peng, Sancheng Li, Xinguang Wang, Cong Yu, Shui Yin, Pengfei |
author_sort | Li, Guorui |
collection | PubMed |
description | In recent years, energy-efficient data collection has evolved into the core problem in the resource-constrained Wireless Sensor Networks (WSNs). Different from existing data collection models in WSNs, we propose a collaborative data collection scheme based on optimal clustering to collect the sensed data in an energy-efficient and load-balanced manner. After dividing the data collection process into the intra-cluster data collection step and the inter-cluster data collection step, we model the optimal clustering problem as a separable convex optimization problem and solve it to obtain the analytical solutions of the optimal clustering size and the optimal data transmission radius. Then, we design a Cluster Heads (CHs)-linking algorithm based on the pseudo Hilbert curve to build a CH chain with the goal of collecting the compressed sensed data among CHs in an accumulative way. Furthermore, we also design a distributed cluster-constructing algorithm to construct the clusters around the virtual CHs in a distributed manner. The experimental results show that the proposed method not only reduces the total energy consumption and prolongs the network lifetime, but also effectively balances the distribution of energy consumption among CHs. By comparing it o the existing compression-based and non-compression-based data collection schemes, the average reductions of energy consumption are 17.9% and 67.9%, respectively. Furthermore, the average network lifetime extends no less than 20-times under the same comparison. |
format | Online Article Text |
id | pubmed-6111701 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-61117012018-08-30 A Collaborative Data Collection Scheme Based on Optimal Clustering for Wireless Sensor Networks Li, Guorui Chen, Haobo Peng, Sancheng Li, Xinguang Wang, Cong Yu, Shui Yin, Pengfei Sensors (Basel) Article In recent years, energy-efficient data collection has evolved into the core problem in the resource-constrained Wireless Sensor Networks (WSNs). Different from existing data collection models in WSNs, we propose a collaborative data collection scheme based on optimal clustering to collect the sensed data in an energy-efficient and load-balanced manner. After dividing the data collection process into the intra-cluster data collection step and the inter-cluster data collection step, we model the optimal clustering problem as a separable convex optimization problem and solve it to obtain the analytical solutions of the optimal clustering size and the optimal data transmission radius. Then, we design a Cluster Heads (CHs)-linking algorithm based on the pseudo Hilbert curve to build a CH chain with the goal of collecting the compressed sensed data among CHs in an accumulative way. Furthermore, we also design a distributed cluster-constructing algorithm to construct the clusters around the virtual CHs in a distributed manner. The experimental results show that the proposed method not only reduces the total energy consumption and prolongs the network lifetime, but also effectively balances the distribution of energy consumption among CHs. By comparing it o the existing compression-based and non-compression-based data collection schemes, the average reductions of energy consumption are 17.9% and 67.9%, respectively. Furthermore, the average network lifetime extends no less than 20-times under the same comparison. MDPI 2018-08-01 /pmc/articles/PMC6111701/ /pubmed/30071592 http://dx.doi.org/10.3390/s18082487 Text en © 2018 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Li, Guorui Chen, Haobo Peng, Sancheng Li, Xinguang Wang, Cong Yu, Shui Yin, Pengfei A Collaborative Data Collection Scheme Based on Optimal Clustering for Wireless Sensor Networks |
title | A Collaborative Data Collection Scheme Based on Optimal Clustering for Wireless Sensor Networks |
title_full | A Collaborative Data Collection Scheme Based on Optimal Clustering for Wireless Sensor Networks |
title_fullStr | A Collaborative Data Collection Scheme Based on Optimal Clustering for Wireless Sensor Networks |
title_full_unstemmed | A Collaborative Data Collection Scheme Based on Optimal Clustering for Wireless Sensor Networks |
title_short | A Collaborative Data Collection Scheme Based on Optimal Clustering for Wireless Sensor Networks |
title_sort | collaborative data collection scheme based on optimal clustering for wireless sensor networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6111701/ https://www.ncbi.nlm.nih.gov/pubmed/30071592 http://dx.doi.org/10.3390/s18082487 |
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