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 (...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/6/2375 |
| _version_ | 1797442251388878848 |
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| author | Fawzy Habeeb Tomasz Szydlo Lukasz Kowalski Ayman Noor Dhaval Thakker Graham Morgan Rajiv Ranjan |
| author_facet | Fawzy Habeeb Tomasz Szydlo Lukasz Kowalski Ayman Noor Dhaval Thakker Graham Morgan Rajiv Ranjan |
| author_sort | Fawzy Habeeb |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T12:39:07Z |
| format | Article |
| id | doaj.art-a8cf7a8512534a19a7e345fee46d8935 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T12:39:07Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a8cf7a8512534a19a7e345fee46d89352023-11-30T22:20:16ZengMDPI AGSensors1424-82202022-03-01226237510.3390/s22062375Dynamic Data Streams for Time-Critical IoT Systems in Energy-Aware IoT Devices Using Reinforcement LearningFawzy Habeeb0Tomasz Szydlo1Lukasz Kowalski2Ayman Noor3Dhaval Thakker4Graham Morgan5Rajiv Ranjan6School of Computing, Newcastle University, Newcastle upon Tyne NE1 7RU, UKInstitute of Computer Science, AGH University of Science and Technology, 30-059 Krakow, PolandInstitute of Computer Science, AGH University of Science and Technology, 30-059 Krakow, PolandCollege of Computer Science and Engineering, Taibah University, Madinah 42353, Saudi ArabiaDepartment of Computer Science, University of Bradford, Bradford BD7 1DP, UKSchool of Computing, Newcastle University, Newcastle upon Tyne NE1 7RU, UKSchool of Computing, Newcastle University, Newcastle upon Tyne NE1 7RU, UKThousands 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.https://www.mdpi.com/1424-8220/22/6/2375osmotic computingInternet of Thingsreinforcement learning |
| spellingShingle | Fawzy Habeeb Tomasz Szydlo Lukasz Kowalski Ayman Noor Dhaval Thakker Graham Morgan Rajiv Ranjan Dynamic Data Streams for Time-Critical IoT Systems in Energy-Aware IoT Devices Using Reinforcement Learning Sensors osmotic computing Internet of Things 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 | osmotic computing Internet of Things reinforcement learning |
| url | https://www.mdpi.com/1424-8220/22/6/2375 |
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