LP-MAB: Improving the Energy Efficiency of LoRaWAN Using a Reinforcement-Learning-Based Adaptive Configuration Algorithm
In the Internet of Things (IoT), Low-Power Wide-Area Networks (LPWANs) are designed to provide low energy consumption while maintaining a long communications’ range for End Devices (EDs). LoRa is a communication protocol that can cover a wide range with low energy consumption. To evaluate the effici...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/4/2363 |
| _version_ | 1797618238054465536 |
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| author | Benyamin Teymuri Reza Serati Nikolaos Athanasios Anagnostopoulos Mehdi Rasti |
| author_facet | Benyamin Teymuri Reza Serati Nikolaos Athanasios Anagnostopoulos Mehdi Rasti |
| author_sort | Benyamin Teymuri |
| collection | DOAJ |
| description | In the Internet of Things (IoT), Low-Power Wide-Area Networks (LPWANs) are designed to provide low energy consumption while maintaining a long communications’ range for End Devices (EDs). LoRa is a communication protocol that can cover a wide range with low energy consumption. To evaluate the efficiency of the LoRa Wide-Area Network (LoRaWAN), three criteria can be considered, namely, the Packet Delivery Rate (PDR), Energy Consumption (EC), and coverage area. A set of transmission parameters have to be configured to establish a communication link. These parameters can affect the data rate, noise resistance, receiver sensitivity, and EC. The Adaptive Data Rate (ADR) algorithm is a mechanism to configure the transmission parameters of EDs aiming to improve the PDR. Therefore, we introduce a new algorithm using the Multi-Armed Bandit (MAB) technique, to configure the EDs’ transmission parameters in a centralized manner on the Network Server (NS) side, while improving the EC, too. The performance of the proposed algorithm, the Low-Power Multi-Armed Bandit (LP-MAB), is evaluated through simulation results and is compared with other approaches in different scenarios. The simulation results indicate that the LP-MAB’s EC outperforms other algorithms while maintaining a relatively high PDR in various circumstances. |
| first_indexed | 2024-03-11T08:10:15Z |
| format | Article |
| id | doaj.art-d198c4950139430ea000bfce1c367f55 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T08:10:15Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d198c4950139430ea000bfce1c367f552023-11-16T23:13:49ZengMDPI AGSensors1424-82202023-02-01234236310.3390/s23042363LP-MAB: Improving the Energy Efficiency of LoRaWAN Using a Reinforcement-Learning-Based Adaptive Configuration AlgorithmBenyamin Teymuri0Reza Serati1Nikolaos Athanasios Anagnostopoulos2Mehdi Rasti3Department of Computer Engineering, Amirkabir University of Technology, Tehran P.O. Box 15875-4413, IranDepartment of Computer Engineering, Amirkabir University of Technology, Tehran P.O. Box 15875-4413, IranFaculty of Computer Science and Mathematics, University of Passau, 94032 Passau, GermanyDepartment of Computer Engineering, Amirkabir University of Technology, Tehran P.O. Box 15875-4413, IranIn the Internet of Things (IoT), Low-Power Wide-Area Networks (LPWANs) are designed to provide low energy consumption while maintaining a long communications’ range for End Devices (EDs). LoRa is a communication protocol that can cover a wide range with low energy consumption. To evaluate the efficiency of the LoRa Wide-Area Network (LoRaWAN), three criteria can be considered, namely, the Packet Delivery Rate (PDR), Energy Consumption (EC), and coverage area. A set of transmission parameters have to be configured to establish a communication link. These parameters can affect the data rate, noise resistance, receiver sensitivity, and EC. The Adaptive Data Rate (ADR) algorithm is a mechanism to configure the transmission parameters of EDs aiming to improve the PDR. Therefore, we introduce a new algorithm using the Multi-Armed Bandit (MAB) technique, to configure the EDs’ transmission parameters in a centralized manner on the Network Server (NS) side, while improving the EC, too. The performance of the proposed algorithm, the Low-Power Multi-Armed Bandit (LP-MAB), is evaluated through simulation results and is compared with other approaches in different scenarios. The simulation results indicate that the LP-MAB’s EC outperforms other algorithms while maintaining a relatively high PDR in various circumstances.https://www.mdpi.com/1424-8220/23/4/2363Internet of Things (IoT)LoRaWANadaptive configurationmachine learningreinforcement learning |
| spellingShingle | Benyamin Teymuri Reza Serati Nikolaos Athanasios Anagnostopoulos Mehdi Rasti LP-MAB: Improving the Energy Efficiency of LoRaWAN Using a Reinforcement-Learning-Based Adaptive Configuration Algorithm Sensors Internet of Things (IoT) LoRaWAN adaptive configuration machine learning reinforcement learning |
| title | LP-MAB: Improving the Energy Efficiency of LoRaWAN Using a Reinforcement-Learning-Based Adaptive Configuration Algorithm |
| title_full | LP-MAB: Improving the Energy Efficiency of LoRaWAN Using a Reinforcement-Learning-Based Adaptive Configuration Algorithm |
| title_fullStr | LP-MAB: Improving the Energy Efficiency of LoRaWAN Using a Reinforcement-Learning-Based Adaptive Configuration Algorithm |
| title_full_unstemmed | LP-MAB: Improving the Energy Efficiency of LoRaWAN Using a Reinforcement-Learning-Based Adaptive Configuration Algorithm |
| title_short | LP-MAB: Improving the Energy Efficiency of LoRaWAN Using a Reinforcement-Learning-Based Adaptive Configuration Algorithm |
| title_sort | lp mab improving the energy efficiency of lorawan using a reinforcement learning based adaptive configuration algorithm |
| topic | Internet of Things (IoT) LoRaWAN adaptive configuration machine learning reinforcement learning |
| url | https://www.mdpi.com/1424-8220/23/4/2363 |
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