Enhanced Reinforcement Learning Algorithm Based-Transmission Parameter Selection for Optimization of Energy Consumption and Packet Delivery Ratio in LoRa Wireless Networks
Wireless communication technologies (WSN) are pivotal for the successful deployment of the Internet of Things (IoT). Among them, long-range (LoRa) and long-range wide-area network (LoRaWAN) technologies have been widely adopted due to their ability to provide long-distance communication, low energy...
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MDPI AG
2024-12-01
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| Series: | Journal of Sensor and Actuator Networks |
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| Online Access: | https://www.mdpi.com/2224-2708/13/6/89 |
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| author | Batyrbek Zholamanov Askhat Bolatbek Ahmet Saymbetov Madiyar Nurgaliyev Evan Yershov Kymbat Kopbay Sayat Orynbassar Gulbakhar Dosymbetova Ainur Kapparova Nurzhigit Kuttybay Nursultan Koshkarbay |
| author_facet | Batyrbek Zholamanov Askhat Bolatbek Ahmet Saymbetov Madiyar Nurgaliyev Evan Yershov Kymbat Kopbay Sayat Orynbassar Gulbakhar Dosymbetova Ainur Kapparova Nurzhigit Kuttybay Nursultan Koshkarbay |
| author_sort | Batyrbek Zholamanov |
| collection | DOAJ |
| description | Wireless communication technologies (WSN) are pivotal for the successful deployment of the Internet of Things (IoT). Among them, long-range (LoRa) and long-range wide-area network (LoRaWAN) technologies have been widely adopted due to their ability to provide long-distance communication, low energy consumption (EC), and cost-effectiveness. One of the critical issues in the implementation of wireless networks is the selection of optimal transmission parameters to minimize EC while maximizing the packet delivery ratio (PDR). This study introduces a reinforcement learning (RL) algorithm, Double Deep Q-Network with Prioritized Experience Replay (DDQN-PER), designed to optimize network transmission parameter selection, particularly the spreading factor (SF) and transmission power (TP). This research explores a variety of network scenarios, characterized by different device numbers and simulation times. The proposed approach demonstrates the best performance, achieving a 17.2% increase in the packet delivery ratio compared to the traditional Adaptive Data Rate (ADR) algorithm. The proposed DDQN-PER algorithm showed PDR improvement in the range of 6.2–8.11% compared to other existing RL and machine-learning-based works. |
| format | Article |
| id | doaj-art-f5fc0d04f77f43f4bc0c1939c856464c |
| institution | DOAJ |
| issn | 2224-2708 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Sensor and Actuator Networks |
| spelling | doaj-art-f5fc0d04f77f43f4bc0c1939c856464c2025-08-20T02:53:19ZengMDPI AGJournal of Sensor and Actuator Networks2224-27082024-12-011368910.3390/jsan13060089Enhanced Reinforcement Learning Algorithm Based-Transmission Parameter Selection for Optimization of Energy Consumption and Packet Delivery Ratio in LoRa Wireless NetworksBatyrbek Zholamanov0Askhat Bolatbek1Ahmet Saymbetov2Madiyar Nurgaliyev3Evan Yershov4Kymbat Kopbay5Sayat Orynbassar6Gulbakhar Dosymbetova7Ainur Kapparova8Nurzhigit Kuttybay9Nursultan Koshkarbay10Faculty of Physics and Technology, Al-Farabi Kazakh National University, 71 Al-Farabi, Almaty 050040, KazakhstanFaculty of Physics and Technology, Al-Farabi Kazakh National University, 71 Al-Farabi, Almaty 050040, KazakhstanFaculty of Physics and Technology, Al-Farabi Kazakh National University, 71 Al-Farabi, Almaty 050040, KazakhstanFaculty of Physics and Technology, Al-Farabi Kazakh National University, 71 Al-Farabi, Almaty 050040, KazakhstanFaculty of Physics and Technology, Al-Farabi Kazakh National University, 71 Al-Farabi, Almaty 050040, KazakhstanFaculty of Physics and Technology, Al-Farabi Kazakh National University, 71 Al-Farabi, Almaty 050040, KazakhstanFaculty of Physics and Technology, Al-Farabi Kazakh National University, 71 Al-Farabi, Almaty 050040, KazakhstanFaculty of Physics and Technology, Al-Farabi Kazakh National University, 71 Al-Farabi, Almaty 050040, KazakhstanFaculty of Physics and Technology, Al-Farabi Kazakh National University, 71 Al-Farabi, Almaty 050040, KazakhstanFaculty of Physics and Technology, Al-Farabi Kazakh National University, 71 Al-Farabi, Almaty 050040, KazakhstanFaculty of Physics and Technology, Al-Farabi Kazakh National University, 71 Al-Farabi, Almaty 050040, KazakhstanWireless communication technologies (WSN) are pivotal for the successful deployment of the Internet of Things (IoT). Among them, long-range (LoRa) and long-range wide-area network (LoRaWAN) technologies have been widely adopted due to their ability to provide long-distance communication, low energy consumption (EC), and cost-effectiveness. One of the critical issues in the implementation of wireless networks is the selection of optimal transmission parameters to minimize EC while maximizing the packet delivery ratio (PDR). This study introduces a reinforcement learning (RL) algorithm, Double Deep Q-Network with Prioritized Experience Replay (DDQN-PER), designed to optimize network transmission parameter selection, particularly the spreading factor (SF) and transmission power (TP). This research explores a variety of network scenarios, characterized by different device numbers and simulation times. The proposed approach demonstrates the best performance, achieving a 17.2% increase in the packet delivery ratio compared to the traditional Adaptive Data Rate (ADR) algorithm. The proposed DDQN-PER algorithm showed PDR improvement in the range of 6.2–8.11% compared to other existing RL and machine-learning-based works.https://www.mdpi.com/2224-2708/13/6/89LoRaWANwireless sensor networkspacket delivery ratioreinforcement learningDouble Deep Q-Network with Prioritized Experience Replay (DDQN-PER)transmission parameter selection |
| spellingShingle | Batyrbek Zholamanov Askhat Bolatbek Ahmet Saymbetov Madiyar Nurgaliyev Evan Yershov Kymbat Kopbay Sayat Orynbassar Gulbakhar Dosymbetova Ainur Kapparova Nurzhigit Kuttybay Nursultan Koshkarbay Enhanced Reinforcement Learning Algorithm Based-Transmission Parameter Selection for Optimization of Energy Consumption and Packet Delivery Ratio in LoRa Wireless Networks Journal of Sensor and Actuator Networks LoRaWAN wireless sensor networks packet delivery ratio reinforcement learning Double Deep Q-Network with Prioritized Experience Replay (DDQN-PER) transmission parameter selection |
| title | Enhanced Reinforcement Learning Algorithm Based-Transmission Parameter Selection for Optimization of Energy Consumption and Packet Delivery Ratio in LoRa Wireless Networks |
| title_full | Enhanced Reinforcement Learning Algorithm Based-Transmission Parameter Selection for Optimization of Energy Consumption and Packet Delivery Ratio in LoRa Wireless Networks |
| title_fullStr | Enhanced Reinforcement Learning Algorithm Based-Transmission Parameter Selection for Optimization of Energy Consumption and Packet Delivery Ratio in LoRa Wireless Networks |
| title_full_unstemmed | Enhanced Reinforcement Learning Algorithm Based-Transmission Parameter Selection for Optimization of Energy Consumption and Packet Delivery Ratio in LoRa Wireless Networks |
| title_short | Enhanced Reinforcement Learning Algorithm Based-Transmission Parameter Selection for Optimization of Energy Consumption and Packet Delivery Ratio in LoRa Wireless Networks |
| title_sort | enhanced reinforcement learning algorithm based transmission parameter selection for optimization of energy consumption and packet delivery ratio in lora wireless networks |
| topic | LoRaWAN wireless sensor networks packet delivery ratio reinforcement learning Double Deep Q-Network with Prioritized Experience Replay (DDQN-PER) transmission parameter selection |
| url | https://www.mdpi.com/2224-2708/13/6/89 |
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