Reinforcement Learning Approach for SF Allocation in LoRa Network

Hong, Shengguang; Yao, Fang; Zhang, Fengyun; Ding, Yulong; Yang, Shuang–Hua

doi:10.1109/jiot.2023.3279429

Cited by 9 publications

References 32 publications

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Machine learning deployment for energy monitoring of Internet of Things nodes in smart agriculture

John,

Sarkar,

Davis

2024

Int J Communication

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SummaryLow‐Power Wide‐Area Network technologies, such as LoRa, are gaining popularity in the agricultural sector for field deployment. The crucial factors in these devices are their range and power efficiency. The energy consumption of a LoRa wireless sensor network is predominantly affected by transmission parameters like carrier frequency, bandwidth, transmit power, spreading factor, and coding rate. Incorrect chosen transmission parameters can lead to a reduction in the battery life of end nodes, requiring frequent battery replacements—a situation undesirable for field deployment. This study introduces a machine learning deployment in the form of a web application designed to monitor the energy consumption of end nodes in LoRa wireless sensor networks. The research initially employs 12 regression models, including Linear, Random Forest, K‐Nearest Neighbours, Decision Tree, Support Vector, Lasso, Ridge, AdaBoost, Gradient Boost, XGBoost, CatBoost, and LightGBM models. The findings of the study reveal that the LightGBM model surpasses other models in accurately predicting the energy consumption of Internet of Things (IoT) nodes, leading to its selection for the web application. This machine learning web application can be implemented in a programmable Long Range Wide Area Network (LoRaWAN) gateway to effectively monitor the energy consumption of IoT end nodes in the agricultural sector.

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Machine learning deployment for energy monitoring of Internet of Things nodes in smart agriculture

John,

Sarkar,

Davis

2024

Int J Communication

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Enhanced LoRaWAN performance through advanced spread factor allocation empowered by machine learning

Rao,

Sundar

2024

Eng. Res. Express

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In Wide Area Networks (WANs), optimal resource allocation is crucial for enhancing computational efficiency, particularly in Low Power Wide Area Networks (LPWANs). This work introduces a machine learning-based system to optimize data transfer rates while minimizing power consumption in LPWANs. The focus is on LoRa, a notable LPWAN technology for long-range communication and interference resilience. Existing LoRa networks experience performance degradation due to interference and congestion caused by the Internet of Things (IoT). To address this, advanced Spreading Factor (SF) allocation techniques are employed, using a metaheuristic optimization algorithm (Particle Swarm Optimization) and an ensemble machine learning algorithm based on gradient boosting (XGBoost), alongside Decision Tree Classifier (DTC) and Random Forest (RF). Simulation results reveal that these approaches significantly enhance Packet Delivery Ratio (PDR) and reduce transmit energy consumption across various distances, outperforming traditional SF schemes. The RF method, for instance, achieves up to 6.32% higher PDR and reduces energy consumption by up to 16.67% compared to the Lowest SF method. Additionally, these techniques improve throughput by up to 14.9% over classical methods. The study also examines the effects of gateways, network distance, and SF on PDR and energy utilization, demonstrating that the proposed methods adapt effectively to different network conditions. The findings highlight the potential of these advanced methods to enhance LoRa network performance, making them suitable for large-scale IoT deployments.

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Q-Learning-Based Medium Access Technology for Minimizing AoI in LoRa Wireless Relay Networks

Kim,

Hwang,

et al. 2024

IEEE Access

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Reinforcement Learning Approach for SF Allocation in LoRa Network

Cited by 9 publications

References 32 publications

Machine learning deployment for energy monitoring of Internet of Things nodes in smart agriculture

Machine learning deployment for energy monitoring of Internet of Things nodes in smart agriculture

Enhanced LoRaWAN performance through advanced spread factor allocation empowered by machine learning

Q-Learning-Based Medium Access Technology for Minimizing AoI in LoRa Wireless Relay Networks

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