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Power-Aware Fog Supported IoT Network for Healthcare Infrastructure Using Swarm Intelligence-Based Algorithms
Healthcare services become increasingly technology dependent every passing day such as the Internet of Things (IoT), Fog Computing, 5th generation (5G) and beyond communications, etc. They enable the processing and exchange of huge volumes of healthcare data whose integrity and real-time delivery ar...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9984132/ http://dx.doi.org/10.1007/s11036-023-02107-9 |
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author | Ali, Hafiz Munsub Bomgni, Alain Bertrand Bukhari, Syed Ahmad Chan Hameed, Tahir Liu, Jun |
author_facet | Ali, Hafiz Munsub Bomgni, Alain Bertrand Bukhari, Syed Ahmad Chan Hameed, Tahir Liu, Jun |
author_sort | Ali, Hafiz Munsub |
collection | PubMed |
description | Healthcare services become increasingly technology dependent every passing day such as the Internet of Things (IoT), Fog Computing, 5th generation (5G) and beyond communications, etc. They enable the processing and exchange of huge volumes of healthcare data whose integrity and real-time delivery are critical for healthcare services. Optimal power consumption in such essential healthcare infrastructure is critical for the well-being of patients and crucial to reduce the operational cost of healthcare facilities. In this paper, a Fog node has been introduced in an IoT healthcare infrastructure with power consumption as a key deciding factor. This work proposed a mathematical formulation to decide the deployment of two heterogeneous gateways in the healthcare infrastructure. The target of optimization is to minimize transmission power and infrastructure costs. Two swarm intelligence-based algorithms have been used to solve the computationally challenging optimization problem. These evolutionary algorithms are a discrete fireworks algorithm and a discrete artificial bee colony algorithm with an ensemble of local search methods. Their performance is compared against the genetic algorithm. The simulation results demonstrate a saving of up to 33 percent in power consumption in the proposed healthcare infrastructure that can significantly improve healthcare data communications and its operational costs. |
format | Online Article Text |
id | pubmed-9984132 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-99841322023-03-03 Power-Aware Fog Supported IoT Network for Healthcare Infrastructure Using Swarm Intelligence-Based Algorithms Ali, Hafiz Munsub Bomgni, Alain Bertrand Bukhari, Syed Ahmad Chan Hameed, Tahir Liu, Jun Mobile Netw Appl Article Healthcare services become increasingly technology dependent every passing day such as the Internet of Things (IoT), Fog Computing, 5th generation (5G) and beyond communications, etc. They enable the processing and exchange of huge volumes of healthcare data whose integrity and real-time delivery are critical for healthcare services. Optimal power consumption in such essential healthcare infrastructure is critical for the well-being of patients and crucial to reduce the operational cost of healthcare facilities. In this paper, a Fog node has been introduced in an IoT healthcare infrastructure with power consumption as a key deciding factor. This work proposed a mathematical formulation to decide the deployment of two heterogeneous gateways in the healthcare infrastructure. The target of optimization is to minimize transmission power and infrastructure costs. Two swarm intelligence-based algorithms have been used to solve the computationally challenging optimization problem. These evolutionary algorithms are a discrete fireworks algorithm and a discrete artificial bee colony algorithm with an ensemble of local search methods. Their performance is compared against the genetic algorithm. The simulation results demonstrate a saving of up to 33 percent in power consumption in the proposed healthcare infrastructure that can significantly improve healthcare data communications and its operational costs. Springer US 2023-03-03 /pmc/articles/PMC9984132/ http://dx.doi.org/10.1007/s11036-023-02107-9 Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Ali, Hafiz Munsub Bomgni, Alain Bertrand Bukhari, Syed Ahmad Chan Hameed, Tahir Liu, Jun Power-Aware Fog Supported IoT Network for Healthcare Infrastructure Using Swarm Intelligence-Based Algorithms |
title | Power-Aware Fog Supported IoT Network for Healthcare Infrastructure Using Swarm Intelligence-Based Algorithms |
title_full | Power-Aware Fog Supported IoT Network for Healthcare Infrastructure Using Swarm Intelligence-Based Algorithms |
title_fullStr | Power-Aware Fog Supported IoT Network for Healthcare Infrastructure Using Swarm Intelligence-Based Algorithms |
title_full_unstemmed | Power-Aware Fog Supported IoT Network for Healthcare Infrastructure Using Swarm Intelligence-Based Algorithms |
title_short | Power-Aware Fog Supported IoT Network for Healthcare Infrastructure Using Swarm Intelligence-Based Algorithms |
title_sort | power-aware fog supported iot network for healthcare infrastructure using swarm intelligence-based algorithms |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9984132/ http://dx.doi.org/10.1007/s11036-023-02107-9 |
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