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Sensor Network Configuration Learning for Maximizing Application Performance

Numerous applications rely on data obtained from a wireless sensor network where application performance is of utmost importance. However, energy usage is also important, and oftentimes, a subset of sensors can be selected to maximize application performance. We cast the problem of sensor selection...

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Detalles Bibliográficos
Autores principales: Helkey, Joel, Holder, Lawrence
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6021864/
https://www.ncbi.nlm.nih.gov/pubmed/29865149
http://dx.doi.org/10.3390/s18061771
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author Helkey, Joel
Holder, Lawrence
author_facet Helkey, Joel
Holder, Lawrence
author_sort Helkey, Joel
collection PubMed
description Numerous applications rely on data obtained from a wireless sensor network where application performance is of utmost importance. However, energy usage is also important, and oftentimes, a subset of sensors can be selected to maximize application performance. We cast the problem of sensor selection as a local search optimization problem and solve it using a variant of stochastic hill climbing extended with novel heuristics. This paper introduces sensor network configuration learning, a feedback-based heuristic algorithm that dynamically reconfigures the sensor network to maximize the performance of the target application. The proposed algorithm is described in detail, along with experiments conducted and a scalability study. A quick method for launching the algorithm from a better starting point than random is also detailed. The performance of the algorithm is compared to that of two other well-known algorithms and randomness. Our simulation results obtained from running sensor network configuration learning on a number of scenarios show the effectiveness and scalability of our approach.
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spelling pubmed-60218642018-07-02 Sensor Network Configuration Learning for Maximizing Application Performance Helkey, Joel Holder, Lawrence Sensors (Basel) Article Numerous applications rely on data obtained from a wireless sensor network where application performance is of utmost importance. However, energy usage is also important, and oftentimes, a subset of sensors can be selected to maximize application performance. We cast the problem of sensor selection as a local search optimization problem and solve it using a variant of stochastic hill climbing extended with novel heuristics. This paper introduces sensor network configuration learning, a feedback-based heuristic algorithm that dynamically reconfigures the sensor network to maximize the performance of the target application. The proposed algorithm is described in detail, along with experiments conducted and a scalability study. A quick method for launching the algorithm from a better starting point than random is also detailed. The performance of the algorithm is compared to that of two other well-known algorithms and randomness. Our simulation results obtained from running sensor network configuration learning on a number of scenarios show the effectiveness and scalability of our approach. MDPI 2018-06-01 /pmc/articles/PMC6021864/ /pubmed/29865149 http://dx.doi.org/10.3390/s18061771 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
Helkey, Joel
Holder, Lawrence
Sensor Network Configuration Learning for Maximizing Application Performance
title Sensor Network Configuration Learning for Maximizing Application Performance
title_full Sensor Network Configuration Learning for Maximizing Application Performance
title_fullStr Sensor Network Configuration Learning for Maximizing Application Performance
title_full_unstemmed Sensor Network Configuration Learning for Maximizing Application Performance
title_short Sensor Network Configuration Learning for Maximizing Application Performance
title_sort sensor network configuration learning for maximizing application performance
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6021864/
https://www.ncbi.nlm.nih.gov/pubmed/29865149
http://dx.doi.org/10.3390/s18061771
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