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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.show moreshow less

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Metadaten
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):License LogoCreative Commons - CC BY - Namensnennung 4.0 International