Teymuri B, Serati R, Anagnostopoulos NA, Rasti M. LP-MAB: Improving the Energy Efficiency of LoRaWAN Using a Reinforcement-Learning-Based Adaptive Configuration Algorithm. Sensors. 2023; 23(4):2363. https://doi.org/10.3390/s23042363
LP-MAB : improving the energy efficiency of LoRaWAN using a reinforcement-learning-based adaptive configuration algorithm
|Author:||Teymuri, Benyamin1; Serati, Reza1; Anagnostopoulos, Nikolaos Athanasios2;|
1Department of Computer Engineering, Amirkabir University of Technology, Tehran P.O. Box 15875-4413, Iran
2Faculty of Computer Science and Mathematics, University of Passau, 94032 Passau, Germany
3Centre for Wireless Communications, University of Oulu, 90570 Oulu, Finland
|Online Access:||PDF Full Text (PDF, 2.7 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2023050440937
Multidisciplinary Digital Publishing Institute,
|Publish Date:|| 2023-05-04
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.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This research was funded by the German Research Foundation–Deutsche Forschungsgemeinschaft (DFG), as part of Project 439892735 of the Priority Program 2253, and by the University of Oulu and the Academy of Finland Profi6 336449. The APC was funded by the Open Access Publication Fund of the University Library Passau.
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).