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Performance Analysis of IoT-Based Health and Environment WSN Deployment

With the development of Internet of Things (IoT) applications, applying the potential and benefits of IoT technology in the health and environment services is increasing to improve the service quality using sensors and devices. This paper aims to apply GIS-based optimization algorithms for optimizin...

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Autores principales: Shakeri, Maryam, Sadeghi-Niaraki, Abolghasem, Choi, Soo-Mi, Islam, S. M. Riazul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7590066/
https://www.ncbi.nlm.nih.gov/pubmed/33092224
http://dx.doi.org/10.3390/s20205923
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author Shakeri, Maryam
Sadeghi-Niaraki, Abolghasem
Choi, Soo-Mi
Islam, S. M. Riazul
author_facet Shakeri, Maryam
Sadeghi-Niaraki, Abolghasem
Choi, Soo-Mi
Islam, S. M. Riazul
author_sort Shakeri, Maryam
collection PubMed
description With the development of Internet of Things (IoT) applications, applying the potential and benefits of IoT technology in the health and environment services is increasing to improve the service quality using sensors and devices. This paper aims to apply GIS-based optimization algorithms for optimizing IoT-based network deployment through the use of wireless sensor networks (WSNs) and smart connected sensors for environmental and health applications. First, the WSN deployment research studies in health and environment applications are reviewed including fire monitoring, precise agriculture, telemonitoring, smart home, and hospital. Second, the WSN deployment process is modeled to optimize two conflict objectives, coverage and lifetime, by applying Minimum Spanning Tree (MST) routing protocol with minimum total network lengths. Third, the performance of the Bees Algorithm (BA) and Particle Swarm Optimization (PSO) algorithms are compared for the evaluation of GIS-based WSN deployment in health and environment applications. The algorithms were compared using convergence rate, constancy repeatability, and modeling complexity criteria. The results showed that the PSO algorithm converged to higher values of objective functions gradually while BA found better fitness values and was faster in the first iterations. The levels of stability and repeatability were high with 0.0150 of standard deviation for PSO and 0.0375 for BA. The PSO also had lower complexity than BA. Therefore, the PSO algorithm obtained better performance for IoT-based sensor network deployment.
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spelling pubmed-75900662020-10-29 Performance Analysis of IoT-Based Health and Environment WSN Deployment Shakeri, Maryam Sadeghi-Niaraki, Abolghasem Choi, Soo-Mi Islam, S. M. Riazul Sensors (Basel) Review With the development of Internet of Things (IoT) applications, applying the potential and benefits of IoT technology in the health and environment services is increasing to improve the service quality using sensors and devices. This paper aims to apply GIS-based optimization algorithms for optimizing IoT-based network deployment through the use of wireless sensor networks (WSNs) and smart connected sensors for environmental and health applications. First, the WSN deployment research studies in health and environment applications are reviewed including fire monitoring, precise agriculture, telemonitoring, smart home, and hospital. Second, the WSN deployment process is modeled to optimize two conflict objectives, coverage and lifetime, by applying Minimum Spanning Tree (MST) routing protocol with minimum total network lengths. Third, the performance of the Bees Algorithm (BA) and Particle Swarm Optimization (PSO) algorithms are compared for the evaluation of GIS-based WSN deployment in health and environment applications. The algorithms were compared using convergence rate, constancy repeatability, and modeling complexity criteria. The results showed that the PSO algorithm converged to higher values of objective functions gradually while BA found better fitness values and was faster in the first iterations. The levels of stability and repeatability were high with 0.0150 of standard deviation for PSO and 0.0375 for BA. The PSO also had lower complexity than BA. Therefore, the PSO algorithm obtained better performance for IoT-based sensor network deployment. MDPI 2020-10-20 /pmc/articles/PMC7590066/ /pubmed/33092224 http://dx.doi.org/10.3390/s20205923 Text en © 2020 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 Review
Shakeri, Maryam
Sadeghi-Niaraki, Abolghasem
Choi, Soo-Mi
Islam, S. M. Riazul
Performance Analysis of IoT-Based Health and Environment WSN Deployment
title Performance Analysis of IoT-Based Health and Environment WSN Deployment
title_full Performance Analysis of IoT-Based Health and Environment WSN Deployment
title_fullStr Performance Analysis of IoT-Based Health and Environment WSN Deployment
title_full_unstemmed Performance Analysis of IoT-Based Health and Environment WSN Deployment
title_short Performance Analysis of IoT-Based Health and Environment WSN Deployment
title_sort performance analysis of iot-based health and environment wsn deployment
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7590066/
https://www.ncbi.nlm.nih.gov/pubmed/33092224
http://dx.doi.org/10.3390/s20205923
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