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Esquema de asignación de recursos de eficiencia energética basado en el aprendizaje por refuerzo en redes LoRa

Ryota Ariyoshi¹, Aohan Li¹, Mikio Hasegawa²

¹Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo 182-8585, Japan.

Sensors (Basel, Switzerland)

|August 28, 2025

Resumen

Este estudio introduce un método de aprendizaje por refuerzo energéticamente eficiente para las redes de largo alcance (LoRa). El enfoque optimiza los parámetros de transmisión del dispositivo, mejorando tanto la eficiencia energética como las tasas de éxito en redes congestionadas.

Palabras clave:

Internet de las Cosas LoRa también.asignación de recursos distribuidos eficiencia energética Aprendizaje por refuerzo

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Área de la Ciencia:

Comunicaciones inalámbricas
Internet de las cosas (IoT)
Aprendizaje automático

Sus antecedentes:

La rápida expansión de los dispositivos de largo alcance (LoRa) provoca congestión de la red, disminución del espectro y la eficiencia energética.
Los métodos existentes luchan por equilibrar el rendimiento y el consumo de energía en despliegues densos de LoRa.

Objetivo del estudio:

Desarrollar un método de aprendizaje por refuerzo distribuido y eficiente desde el punto de vista energético para las redes LoRa.
Para permitir que los dispositivos LoRa individuales optimicen de forma autónoma los parámetros de transmisión (canal, potencia de transmisión, ancho de banda).

Principales métodos:

Utilizó el algoritmo sintonizado de límite de confianza superior (UCB) 1 para la selección de parámetros.
Métricas integradas de consumo de energía en la función de recompensa de aprendizaje por refuerzo.
Diseñó un algoritmo ligero adecuado para dispositivos IoT con recursos limitados.

Principales resultados:

Se lograron reducciones significativas en el consumo de energía en comparación con los métodos de referencia.
Se han demostrado altas tasas de éxito de transmisión incluso en escenarios de red densa.
Superó a los enfoques de asignación fija, ADR-Lite y epsilon.

Conclusiones:

El método de aprendizaje por refuerzo propuesto mejora efectivamente la eficiencia energética y el éxito de la transmisión en las redes LoRa.
Esta solución ligera es práctica para aplicaciones de IoT de recursos limitados en el mundo real.
El método ofrece una alternativa superior a las estrategias de asignación de parámetros existentes para los dispositivos LoRa.