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 |
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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 |