Q-Learning-Based Medium Access Technology for Minimizing AoI in LoRa Wireless Relay Networks
LoRaWAN has emerged as a leading communication protocol for Low Power Wide Area Networks (LPWANs), gaining widespread adoption across diverse Internet of Things (IoT) deployments. Our approach integrates LoRa relay devices and Age of Information (AoI) metrics to enhance network performance. The algo...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10776963/ |
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| author | Dowon Kim Geonha Hwang Ohyun Jo Kyungseop Shin |
| author_facet | Dowon Kim Geonha Hwang Ohyun Jo Kyungseop Shin |
| author_sort | Dowon Kim |
| collection | DOAJ |
| description | LoRaWAN has emerged as a leading communication protocol for Low Power Wide Area Networks (LPWANs), gaining widespread adoption across diverse Internet of Things (IoT) deployments. Our approach integrates LoRa relay devices and Age of Information (AoI) metrics to enhance network performance. The algorithm dynamically adjusts Spreading Factors (SFs) based on network conditions, utilizing reinforcement learning techniques for optimal SF selection. Key innovations in this paper include the strategic use of LoRa relays, which is particularly effective for mitigating signal attenuation in long-distance communication scenarios. Another significant advancement is the utilization of AoI in two crucial aspects: as a component of the reinforcement learning algorithm and as an evaluation metric. This novel approach prioritizes data freshness in transmission decisions, enabling the algorithm to optimize communication based on the timeliness of information. Simulations demonstrate significant performance improvements over baseline algorithms, achieving average AoI reductions of 23% in high-density scenarios and 31% in high data transfer environments. These results highlight the effectiveness of combining AoI metrics and intelligent relay selection in improving LoRaWAN performance for IoT applications. |
| format | Article |
| id | doaj-art-1f9c8f392acd4073a858bdda53cd8950 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-1f9c8f392acd4073a858bdda53cd89502024-12-11T00:05:52ZengIEEEIEEE Access2169-35362024-01-011218302418303710.1109/ACCESS.2024.351077610776963Q-Learning-Based Medium Access Technology for Minimizing AoI in LoRa Wireless Relay NetworksDowon Kim0https://orcid.org/0009-0003-9155-7890Geonha Hwang1https://orcid.org/0009-0009-2353-2759Ohyun Jo2Kyungseop Shin3https://orcid.org/0000-0002-3867-1921Department of Computer Science, Sangmyung University, Seoul, South KoreaDepartment of Computer Science, Sangmyung University, Seoul, South KoreaDepartment of Computer Science, Chungbuk National University, Cheongju, South KoreaDepartment of Computer Science, Sangmyung University, Seoul, South KoreaLoRaWAN has emerged as a leading communication protocol for Low Power Wide Area Networks (LPWANs), gaining widespread adoption across diverse Internet of Things (IoT) deployments. Our approach integrates LoRa relay devices and Age of Information (AoI) metrics to enhance network performance. The algorithm dynamically adjusts Spreading Factors (SFs) based on network conditions, utilizing reinforcement learning techniques for optimal SF selection. Key innovations in this paper include the strategic use of LoRa relays, which is particularly effective for mitigating signal attenuation in long-distance communication scenarios. Another significant advancement is the utilization of AoI in two crucial aspects: as a component of the reinforcement learning algorithm and as an evaluation metric. This novel approach prioritizes data freshness in transmission decisions, enabling the algorithm to optimize communication based on the timeliness of information. Simulations demonstrate significant performance improvements over baseline algorithms, achieving average AoI reductions of 23% in high-density scenarios and 31% in high data transfer environments. These results highlight the effectiveness of combining AoI metrics and intelligent relay selection in improving LoRaWAN performance for IoT applications.https://ieeexplore.ieee.org/document/10776963/Age of InformationIoT systemLoRamedium access controlrelay networksreinforcement learning |
| spellingShingle | Dowon Kim Geonha Hwang Ohyun Jo Kyungseop Shin Q-Learning-Based Medium Access Technology for Minimizing AoI in LoRa Wireless Relay Networks IEEE Access Age of Information IoT system LoRa medium access control relay networks reinforcement learning |
| title | Q-Learning-Based Medium Access Technology for Minimizing AoI in LoRa Wireless Relay Networks |
| title_full | Q-Learning-Based Medium Access Technology for Minimizing AoI in LoRa Wireless Relay Networks |
| title_fullStr | Q-Learning-Based Medium Access Technology for Minimizing AoI in LoRa Wireless Relay Networks |
| title_full_unstemmed | Q-Learning-Based Medium Access Technology for Minimizing AoI in LoRa Wireless Relay Networks |
| title_short | Q-Learning-Based Medium Access Technology for Minimizing AoI in LoRa Wireless Relay Networks |
| title_sort | q learning based medium access technology for minimizing aoi in lora wireless relay networks |
| topic | Age of Information IoT system LoRa medium access control relay networks reinforcement learning |
| url | https://ieeexplore.ieee.org/document/10776963/ |
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