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LoRa is widely used in Internet of Things (IoT) applications due to its low power consumption and extended range. However, its performance can be significantly degraded in dynamic interference environments, impacting data transmission delay and reliability. Existing methods struggle to balance energy consumption and communication quality under variable interference conditions. This paper introduces IAE-LoRa, a reinforcement learning-based approach to optimize LoRa communication under varying interference conditions. IAE-LoRa dynamically adjusts the transmission power and data rate of LoRa modules in real time, optimizing delay, delivered bytes, and power consumption based on channel interference levels. Experimental evaluations, including simulations and real-world tests, show that IAE-LoRa effectively adapts to different interference levels, achieving lower average power consumption while maintaining competitive performance compared to existing baseline strategies. This approach provides a more adaptive and energy-efficient solution for optimizing LoRa communication across diverse operational environments.

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IAE-LoRa: Interference-Aware and Energy-Efficient LoRa Optimization Using Reinforcement Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Hongyu Tian,
  • Kaitong Zheng,
  • Yanying Lin,
  • Changhao Yuan,
  • Kejiang Ye

摘要

LoRa is widely used in Internet of Things (IoT) applications due to its low power consumption and extended range. However, its performance can be significantly degraded in dynamic interference environments, impacting data transmission delay and reliability. Existing methods struggle to balance energy consumption and communication quality under variable interference conditions. This paper introduces IAE-LoRa, a reinforcement learning-based approach to optimize LoRa communication under varying interference conditions. IAE-LoRa dynamically adjusts the transmission power and data rate of LoRa modules in real time, optimizing delay, delivered bytes, and power consumption based on channel interference levels. Experimental evaluations, including simulations and real-world tests, show that IAE-LoRa effectively adapts to different interference levels, achieving lower average power consumption while maintaining competitive performance compared to existing baseline strategies. This approach provides a more adaptive and energy-efficient solution for optimizing LoRa communication across diverse operational environments.