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Dynamic Data Streams for Time-Critical IoT Systems in Energy-Aware IoT Devices Using Reinforcement Learning

Thousands of energy-aware sensors have been placed for monitoring in a variety of scenarios, such as manufacturing, control systems, disaster management, flood control and so on, requiring time-critical energy-efficient solutions to extend their lifetime. This paper proposes reinforcement learning (...

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Detalles Bibliográficos
Autores principales: Habeeb, Fawzy, Szydlo, Tomasz, Kowalski, Lukasz, Noor, Ayman, Thakker, Dhaval, Morgan, Graham, Ranjan, Rajiv
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8949606/
https://www.ncbi.nlm.nih.gov/pubmed/35336544
http://dx.doi.org/10.3390/s22062375
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author Habeeb, Fawzy
Szydlo, Tomasz
Kowalski, Lukasz
Noor, Ayman
Thakker, Dhaval
Morgan, Graham
Ranjan, Rajiv
author_facet Habeeb, Fawzy
Szydlo, Tomasz
Kowalski, Lukasz
Noor, Ayman
Thakker, Dhaval
Morgan, Graham
Ranjan, Rajiv
author_sort Habeeb, Fawzy
collection PubMed
description Thousands of energy-aware sensors have been placed for monitoring in a variety of scenarios, such as manufacturing, control systems, disaster management, flood control and so on, requiring time-critical energy-efficient solutions to extend their lifetime. This paper proposes reinforcement learning (RL) based dynamic data streams for time-critical IoT systems in energy-aware IoT devices. The designed solution employs the Q-Learning algorithm. The proposed mechanism has the potential to adjust the data transport rate based on the amount of renewable energy resources that are available, to ensure collecting reliable data while also taking into account the sensor battery lifetime. The solution was evaluated using historical data for solar radiation levels, which shows that the proposed solution can increase the amount of transmitted data up to 23%, ensuring the continuous operation of the device.
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spelling pubmed-89496062022-03-26 Dynamic Data Streams for Time-Critical IoT Systems in Energy-Aware IoT Devices Using Reinforcement Learning Habeeb, Fawzy Szydlo, Tomasz Kowalski, Lukasz Noor, Ayman Thakker, Dhaval Morgan, Graham Ranjan, Rajiv Sensors (Basel) Article Thousands of energy-aware sensors have been placed for monitoring in a variety of scenarios, such as manufacturing, control systems, disaster management, flood control and so on, requiring time-critical energy-efficient solutions to extend their lifetime. This paper proposes reinforcement learning (RL) based dynamic data streams for time-critical IoT systems in energy-aware IoT devices. The designed solution employs the Q-Learning algorithm. The proposed mechanism has the potential to adjust the data transport rate based on the amount of renewable energy resources that are available, to ensure collecting reliable data while also taking into account the sensor battery lifetime. The solution was evaluated using historical data for solar radiation levels, which shows that the proposed solution can increase the amount of transmitted data up to 23%, ensuring the continuous operation of the device. MDPI 2022-03-19 /pmc/articles/PMC8949606/ /pubmed/35336544 http://dx.doi.org/10.3390/s22062375 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Habeeb, Fawzy
Szydlo, Tomasz
Kowalski, Lukasz
Noor, Ayman
Thakker, Dhaval
Morgan, Graham
Ranjan, Rajiv
Dynamic Data Streams for Time-Critical IoT Systems in Energy-Aware IoT Devices Using Reinforcement Learning
title Dynamic Data Streams for Time-Critical IoT Systems in Energy-Aware IoT Devices Using Reinforcement Learning
title_full Dynamic Data Streams for Time-Critical IoT Systems in Energy-Aware IoT Devices Using Reinforcement Learning
title_fullStr Dynamic Data Streams for Time-Critical IoT Systems in Energy-Aware IoT Devices Using Reinforcement Learning
title_full_unstemmed Dynamic Data Streams for Time-Critical IoT Systems in Energy-Aware IoT Devices Using Reinforcement Learning
title_short Dynamic Data Streams for Time-Critical IoT Systems in Energy-Aware IoT Devices Using Reinforcement Learning
title_sort dynamic data streams for time-critical iot systems in energy-aware iot devices using reinforcement learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8949606/
https://www.ncbi.nlm.nih.gov/pubmed/35336544
http://dx.doi.org/10.3390/s22062375
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