Toward an Adaptive Threshold on Cooperative Bandwidth Management Based on Hierarchical Reinforcement Learning

With the increase in Internet of Things (IoT) devices and network communications, but with less bandwidth growth, the resulting constraints must be overcome. Due to the network complexity and uncertainty of emergency distribution parameters in smart environments, using predetermined rules seems illo...

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Autores principales:	Motahareh Mobasheri, Yangwoo Kim, Woongsup Kim
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	internet of things fog computing fog fragment cooperation hierarchical reinforcement learning Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/de66238ca697460091a3b3690707a228
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spelling	oai:doaj.org-article:de66238ca697460091a3b3690707a2282021-11-11T19:04:44ZToward an Adaptive Threshold on Cooperative Bandwidth Management Based on Hierarchical Reinforcement Learning10.3390/s212170531424-8220https://doaj.org/article/de66238ca697460091a3b3690707a2282021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7053https://doaj.org/toc/1424-8220With the increase in Internet of Things (IoT) devices and network communications, but with less bandwidth growth, the resulting constraints must be overcome. Due to the network complexity and uncertainty of emergency distribution parameters in smart environments, using predetermined rules seems illogical. Reinforcement learning (RL), as a powerful machine learning approach, can handle such smart environments without a trainer or supervisor. Recently, we worked on bandwidth management in a smart environment with several fog fragments using limited shared bandwidth, where IoT devices may experience uncertain emergencies in terms of the time and sequence needed for more bandwidth for further higher-level communication. We introduced fog fragment cooperation using an RL approach under a predefined fixed threshold constraint. In this study, we promote this approach by removing the fixed level of restriction of the threshold through hierarchical reinforcement learning (HRL) and completing the cooperation qualification. At the first learning hierarchy level of the proposed approach, the best threshold level is learned over time, and the final results are used by the second learning hierarchy level, where the fog node learns the best device for helping an emergency device by temporarily lending the bandwidth. Although equipping the method to the adaptive threshold and restricting fog fragment cooperation make the learning procedure more difficult, the HRL approach increases the method’s efficiency in terms of time and performance.Motahareh MobasheriYangwoo KimWoongsup KimMDPI AGarticleinternet of thingsfog computingfog fragment cooperationhierarchical reinforcement learningChemical technologyTP1-1185ENSensors, Vol 21, Iss 7053, p 7053 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	internet of things fog computing fog fragment cooperation hierarchical reinforcement learning Chemical technology TP1-1185
spellingShingle	internet of things fog computing fog fragment cooperation hierarchical reinforcement learning Chemical technology TP1-1185 Motahareh Mobasheri Yangwoo Kim Woongsup Kim Toward an Adaptive Threshold on Cooperative Bandwidth Management Based on Hierarchical Reinforcement Learning
description	With the increase in Internet of Things (IoT) devices and network communications, but with less bandwidth growth, the resulting constraints must be overcome. Due to the network complexity and uncertainty of emergency distribution parameters in smart environments, using predetermined rules seems illogical. Reinforcement learning (RL), as a powerful machine learning approach, can handle such smart environments without a trainer or supervisor. Recently, we worked on bandwidth management in a smart environment with several fog fragments using limited shared bandwidth, where IoT devices may experience uncertain emergencies in terms of the time and sequence needed for more bandwidth for further higher-level communication. We introduced fog fragment cooperation using an RL approach under a predefined fixed threshold constraint. In this study, we promote this approach by removing the fixed level of restriction of the threshold through hierarchical reinforcement learning (HRL) and completing the cooperation qualification. At the first learning hierarchy level of the proposed approach, the best threshold level is learned over time, and the final results are used by the second learning hierarchy level, where the fog node learns the best device for helping an emergency device by temporarily lending the bandwidth. Although equipping the method to the adaptive threshold and restricting fog fragment cooperation make the learning procedure more difficult, the HRL approach increases the method’s efficiency in terms of time and performance.
format	article
author	Motahareh Mobasheri Yangwoo Kim Woongsup Kim
author_facet	Motahareh Mobasheri Yangwoo Kim Woongsup Kim
author_sort	Motahareh Mobasheri
title	Toward an Adaptive Threshold on Cooperative Bandwidth Management Based on Hierarchical Reinforcement Learning
title_short	Toward an Adaptive Threshold on Cooperative Bandwidth Management Based on Hierarchical Reinforcement Learning
title_full	Toward an Adaptive Threshold on Cooperative Bandwidth Management Based on Hierarchical Reinforcement Learning
title_fullStr	Toward an Adaptive Threshold on Cooperative Bandwidth Management Based on Hierarchical Reinforcement Learning
title_full_unstemmed	Toward an Adaptive Threshold on Cooperative Bandwidth Management Based on Hierarchical Reinforcement Learning
title_sort	toward an adaptive threshold on cooperative bandwidth management based on hierarchical reinforcement learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/de66238ca697460091a3b3690707a228
work_keys_str_mv	AT motaharehmobasheri towardanadaptivethresholdoncooperativebandwidthmanagementbasedonhierarchicalreinforcementlearning AT yangwookim towardanadaptivethresholdoncooperativebandwidthmanagementbasedonhierarchicalreinforcementlearning AT woongsupkim towardanadaptivethresholdoncooperativebandwidthmanagementbasedonhierarchicalreinforcementlearning
_version_	1718431675811102720

Toward an Adaptive Threshold on Cooperative Bandwidth Management Based on Hierarchical Reinforcement Learning

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