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Market Model for Resource Allocation in Emerging Sensor Networks with Reinforcement Learning

Emerging sensor networks (ESNs) are an inevitable trend with the development of the Internet of Things (IoT), and intend to connect almost every intelligent device. Therefore, it is critical to study resource allocation in such an environment, due to the concern of efficiency, especially when resour...

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Detalles Bibliográficos
Autores principales: Zhang, Yue, Song, Bin, Zhang, Ying, Du, Xiaojiang, Guizani, Mohsen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5191002/
https://www.ncbi.nlm.nih.gov/pubmed/27916841
http://dx.doi.org/10.3390/s16122021
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author Zhang, Yue
Song, Bin
Zhang, Ying
Du, Xiaojiang
Guizani, Mohsen
author_facet Zhang, Yue
Song, Bin
Zhang, Ying
Du, Xiaojiang
Guizani, Mohsen
author_sort Zhang, Yue
collection PubMed
description Emerging sensor networks (ESNs) are an inevitable trend with the development of the Internet of Things (IoT), and intend to connect almost every intelligent device. Therefore, it is critical to study resource allocation in such an environment, due to the concern of efficiency, especially when resources are limited. By viewing ESNs as multi-agent environments, we model them with an agent-based modelling (ABM) method and deal with resource allocation problems with market models, after describing users’ patterns. Reinforcement learning methods are introduced to estimate users’ patterns and verify the outcomes in our market models. Experimental results show the efficiency of our methods, which are also capable of guiding topology management.
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spelling pubmed-51910022017-01-03 Market Model for Resource Allocation in Emerging Sensor Networks with Reinforcement Learning Zhang, Yue Song, Bin Zhang, Ying Du, Xiaojiang Guizani, Mohsen Sensors (Basel) Article Emerging sensor networks (ESNs) are an inevitable trend with the development of the Internet of Things (IoT), and intend to connect almost every intelligent device. Therefore, it is critical to study resource allocation in such an environment, due to the concern of efficiency, especially when resources are limited. By viewing ESNs as multi-agent environments, we model them with an agent-based modelling (ABM) method and deal with resource allocation problems with market models, after describing users’ patterns. Reinforcement learning methods are introduced to estimate users’ patterns and verify the outcomes in our market models. Experimental results show the efficiency of our methods, which are also capable of guiding topology management. MDPI 2016-11-29 /pmc/articles/PMC5191002/ /pubmed/27916841 http://dx.doi.org/10.3390/s16122021 Text en © 2016 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
Zhang, Yue
Song, Bin
Zhang, Ying
Du, Xiaojiang
Guizani, Mohsen
Market Model for Resource Allocation in Emerging Sensor Networks with Reinforcement Learning
title Market Model for Resource Allocation in Emerging Sensor Networks with Reinforcement Learning
title_full Market Model for Resource Allocation in Emerging Sensor Networks with Reinforcement Learning
title_fullStr Market Model for Resource Allocation in Emerging Sensor Networks with Reinforcement Learning
title_full_unstemmed Market Model for Resource Allocation in Emerging Sensor Networks with Reinforcement Learning
title_short Market Model for Resource Allocation in Emerging Sensor Networks with Reinforcement Learning
title_sort market model for resource allocation in emerging sensor networks with reinforcement learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5191002/
https://www.ncbi.nlm.nih.gov/pubmed/27916841
http://dx.doi.org/10.3390/s16122021
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