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Dynamic Resource Optimization for Energy-Efficient 6G-IoT Ecosystems

The problem of energy optimization for Internet of Things (IoT) devices is crucial for two reasons. Firstly, IoT devices powered by renewable energy sources have limited energy resources. Secondly, the aggregate energy requirement for these small and low-powered devices is translated into significan...

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Detalles Bibliográficos
Autores principales: Ansere, James Adu, Kamal, Mohsin, Khan, Izaz Ahmad, Aman, Muhammad Naveed
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10223501/
https://www.ncbi.nlm.nih.gov/pubmed/37430624
http://dx.doi.org/10.3390/s23104711
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author Ansere, James Adu
Kamal, Mohsin
Khan, Izaz Ahmad
Aman, Muhammad Naveed
author_facet Ansere, James Adu
Kamal, Mohsin
Khan, Izaz Ahmad
Aman, Muhammad Naveed
author_sort Ansere, James Adu
collection PubMed
description The problem of energy optimization for Internet of Things (IoT) devices is crucial for two reasons. Firstly, IoT devices powered by renewable energy sources have limited energy resources. Secondly, the aggregate energy requirement for these small and low-powered devices is translated into significant energy consumption. Existing works show that a significant portion of an IoT device’s energy is consumed by the radio sub-system. With the emerging sixth generation (6G), energy efficiency is a major design criterion for significantly increasing the IoT network’s performance. To solve this issue, this paper focuses on maximizing the energy efficiency of the radio sub-system. In wireless communications, the channel plays a major role in determining energy requirements. Therefore, a mixed-integer nonlinear programming problem is formulated to jointly optimize power allocation, sub-channel allocation, user selection, and the activated remote radio units (RRUs) in a combinatorial approach according to the channel conditions. Although it is an NP-hard problem, the optimization problem is solved through fractional programming properties, converting it into an equivalent tractable and parametric form. The resulting problem is then solved optimally by using the Lagrangian decomposition method and an improved Kuhn–Munkres algorithm. The results show that the proposed technique significantly improves the energy efficiency of IoT systems as compared to the state-of-the-art work.
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spelling pubmed-102235012023-05-28 Dynamic Resource Optimization for Energy-Efficient 6G-IoT Ecosystems Ansere, James Adu Kamal, Mohsin Khan, Izaz Ahmad Aman, Muhammad Naveed Sensors (Basel) Article The problem of energy optimization for Internet of Things (IoT) devices is crucial for two reasons. Firstly, IoT devices powered by renewable energy sources have limited energy resources. Secondly, the aggregate energy requirement for these small and low-powered devices is translated into significant energy consumption. Existing works show that a significant portion of an IoT device’s energy is consumed by the radio sub-system. With the emerging sixth generation (6G), energy efficiency is a major design criterion for significantly increasing the IoT network’s performance. To solve this issue, this paper focuses on maximizing the energy efficiency of the radio sub-system. In wireless communications, the channel plays a major role in determining energy requirements. Therefore, a mixed-integer nonlinear programming problem is formulated to jointly optimize power allocation, sub-channel allocation, user selection, and the activated remote radio units (RRUs) in a combinatorial approach according to the channel conditions. Although it is an NP-hard problem, the optimization problem is solved through fractional programming properties, converting it into an equivalent tractable and parametric form. The resulting problem is then solved optimally by using the Lagrangian decomposition method and an improved Kuhn–Munkres algorithm. The results show that the proposed technique significantly improves the energy efficiency of IoT systems as compared to the state-of-the-art work. MDPI 2023-05-12 /pmc/articles/PMC10223501/ /pubmed/37430624 http://dx.doi.org/10.3390/s23104711 Text en © 2023 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
Ansere, James Adu
Kamal, Mohsin
Khan, Izaz Ahmad
Aman, Muhammad Naveed
Dynamic Resource Optimization for Energy-Efficient 6G-IoT Ecosystems
title Dynamic Resource Optimization for Energy-Efficient 6G-IoT Ecosystems
title_full Dynamic Resource Optimization for Energy-Efficient 6G-IoT Ecosystems
title_fullStr Dynamic Resource Optimization for Energy-Efficient 6G-IoT Ecosystems
title_full_unstemmed Dynamic Resource Optimization for Energy-Efficient 6G-IoT Ecosystems
title_short Dynamic Resource Optimization for Energy-Efficient 6G-IoT Ecosystems
title_sort dynamic resource optimization for energy-efficient 6g-iot ecosystems
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10223501/
https://www.ncbi.nlm.nih.gov/pubmed/37430624
http://dx.doi.org/10.3390/s23104711
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