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LoRaWAN Meets ML: A Survey on Enhancing Performance with Machine Learning.

Arshad Farhad1, Jae-Young Pyun1

  • 1Wireless and Mobile Communication System Laboratory, Department of Information and Communication Engineering, Chosun University, Gwangju 61452, Republic of Korea.

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Summary
This summary is machine-generated.

Machine learning (ML) optimizes resource management in Long Range Wide Area Networks (LoRaWAN) by efficiently allocating spreading factors and transmission power. This survey guides researchers on applying ML for enhanced LoRaWAN performance and resource utilization.

Keywords:
Internet of Things (IoT)LoRaLoRaWANartificial intelligencedatasetdeep learningmachine learning (ML)reinforcement learningresource managementsimulationspreading factor (SF)transmission power (TP)

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Area of Science:

  • Wireless Communication Technologies
  • Internet of Things (IoT)

Background:

  • Long Range Wide Area Network (LoRaWAN) is crucial for low-power, long-range IoT communication.
  • Efficient radio resource utilization (spreading factor, transmission power) is a key challenge in LoRaWAN.

Purpose of the Study:

  • To survey and analyze machine learning (ML) methods for LoRaWAN resource management.
  • To identify ML frameworks, datasets, and features for optimizing LoRaWAN performance.

Main Methods:

  • Review of state-of-the-art ML techniques applied to LoRaWAN resource allocation.
  • Exploration of publicly available LoRaWAN frameworks and datasets.
  • Evaluation of Network Simulator-3-based ML frameworks.

Main Results:

  • ML methods effectively address resource allocation challenges in LoRaWAN.
  • Identification of suitable ML approaches and necessary features for efficient management.
  • Overview of existing datasets and simulation tools for ML-driven LoRaWAN research.

Conclusions:

  • ML offers significant potential for improving LoRaWAN efficiency and performance.
  • Provides a comprehensive guide for researchers and practitioners in applying ML to LoRaWAN.
  • Highlights future research directions for ML in wireless IoT networks.