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Energy Versus Throughput Optimisation for Machine-to-Machine Communication

We investigate the trade-off between energy usage and (packet) throughput in wireless mesh networks performing machine-to-machine communication. For this we provide a novel mixed-integer programming formulation to maximise the throughput while maintaining minimal energy usage, together with an effec...

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Detalles Bibliográficos
Autores principales: Fitzgerald, Emma, Pióro, Michał, Tomaszewski, Artur
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7435706/
https://www.ncbi.nlm.nih.gov/pubmed/32722126
http://dx.doi.org/10.3390/s20154122
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author Fitzgerald, Emma
Pióro, Michał
Tomaszewski, Artur
author_facet Fitzgerald, Emma
Pióro, Michał
Tomaszewski, Artur
author_sort Fitzgerald, Emma
collection PubMed
description We investigate the trade-off between energy usage and (packet) throughput in wireless mesh networks performing machine-to-machine communication. For this we provide a novel mixed-integer programming formulation to maximise the throughput while maintaining minimal energy usage, together with an effective price-and-branch solution algorithm based on column generation. The resulting optimisation model is the main original contribution of the presented paper. We conducted a numerical study using network examples from 10 to 40 nodes, in which periodic sensor measurements are aggregated and disseminated to actuators. In almost all cases, we were able to achieve maximum throughput and minimum energy usage simultaneously, and in those cases where this was not possible, the costs incurred in one objective in order to achieve the other were typically low. The solution times for all network sizes were of the order of seconds, showing that our optimisation model is feasible to use in practice.
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spelling pubmed-74357062020-08-28 Energy Versus Throughput Optimisation for Machine-to-Machine Communication Fitzgerald, Emma Pióro, Michał Tomaszewski, Artur Sensors (Basel) Article We investigate the trade-off between energy usage and (packet) throughput in wireless mesh networks performing machine-to-machine communication. For this we provide a novel mixed-integer programming formulation to maximise the throughput while maintaining minimal energy usage, together with an effective price-and-branch solution algorithm based on column generation. The resulting optimisation model is the main original contribution of the presented paper. We conducted a numerical study using network examples from 10 to 40 nodes, in which periodic sensor measurements are aggregated and disseminated to actuators. In almost all cases, we were able to achieve maximum throughput and minimum energy usage simultaneously, and in those cases where this was not possible, the costs incurred in one objective in order to achieve the other were typically low. The solution times for all network sizes were of the order of seconds, showing that our optimisation model is feasible to use in practice. MDPI 2020-07-24 /pmc/articles/PMC7435706/ /pubmed/32722126 http://dx.doi.org/10.3390/s20154122 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 Article
Fitzgerald, Emma
Pióro, Michał
Tomaszewski, Artur
Energy Versus Throughput Optimisation for Machine-to-Machine Communication
title Energy Versus Throughput Optimisation for Machine-to-Machine Communication
title_full Energy Versus Throughput Optimisation for Machine-to-Machine Communication
title_fullStr Energy Versus Throughput Optimisation for Machine-to-Machine Communication
title_full_unstemmed Energy Versus Throughput Optimisation for Machine-to-Machine Communication
title_short Energy Versus Throughput Optimisation for Machine-to-Machine Communication
title_sort energy versus throughput optimisation for machine-to-machine communication
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7435706/
https://www.ncbi.nlm.nih.gov/pubmed/32722126
http://dx.doi.org/10.3390/s20154122
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