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 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-PowerIn 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.…


| Author: | Nikolaos Athanasios AnagnostopoulosORCiD, Benyamin Teymuri, Reza Serati, Mehdi Rasti |
|---|---|
| URN: | urn:nbn:de:bvb:739-opus4-11853 |
| DOI: | https://doi.org/10.3390/s23042363 |
| ISSN: | 1424-8220 |
| Parent Title (English): | Sensors |
| Publisher: | MDPI |
| Place of publication: | Basel, Switzerland |
| Editor: | Bin Xie, Ning Wang, Yi Gu, Angelos Stefanidis |
| Document Type: | Article |
| Language: | English |
| Year of Completion: | 2023 |
| Year of Completion: | 2023 |
| Date of first Publication: | 2023/02/20 |
| Publishing Institution: | Universität Passau |
| Release Date: | 2023/03/27 |
| Tag: | Internet of Things (IoT); LoRaWAN; adaptive configuration; machine learning; reinforcement learning |
| Volume: | 23 |
| Issue: | 4 |
| Page Number: | 22 Seiten |
| Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 000 Informatik, Informationswissenschaft, allgemeine Werke |
| open_access (DINI-Set): | open_access |
| Funding Acknowledgement: | Gefördert durch den Open-Access-Publikationsfonds der Universitätsbibliothek Passau. |
| Licence (German): | Creative Commons - CC BY - Namensnennung 4.0 International |

