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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2016
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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. |
format | Online Article Text |
id | pubmed-5191002 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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