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Investigación sobre la estrategia de descarga de computación y asignación de recursos basada en el MADDPG para la red
1College of Information Engineering, Shanghai Maritime University, Shanghai 200135, China.
Entropy (Basel, Switzerland)
|August 28, 2025
Resumen
Este estudio introduce un nuevo algoritmo para optimizar la descarga de computación en las redes marítimas. El enfoque de gradiente de política determinista profunda de múltiples agentes (MADDPG) reduce efectivamente los costos del sistema para los dispositivos de Internet de las Cosas Marítimas (M-IoT).
Área de la Ciencia:
- Ciencias de la computación
- Ingeniería eléctrica
- Ingeniería de redes
Sus antecedentes:
- Las redes marítimas de Internet de las Cosas (M-IoT) se enfrentan a desafíos en la descarga de computación y la asignación de recursos debido a entornos complejos y dinámicos.
- Los métodos de optimización tradicionales luchan con la no convexidad y la complejidad combinatoria en estas redes integradas espacio-aire-mar.
Objetivo del estudio:
- Desarrollar un algoritmo de optimización eficiente para la descarga de computación y la asignación de recursos en redes integradas espacio-aire-mar.
- Minimizar los costos totales del sistema, equilibrando el consumo de energía y la latencia de los dispositivos M-IoT.
Principales métodos:
- Se propuso un algoritmo de optimización basado en el gradiente de política determinista profunda de múltiples agentes (MADDPG).
- El sistema fue modelado como un proceso de decisión de Markov parcialmente observable (POMDP), integrando la asociación, el poder, la asignación de recursos informáticos y la distribución de tareas.
- Se empleó una capacitación centralizada con un marco de ejecución descentralizado, con dispositivos M-IoT y UAV actuando como agentes inteligentes.
Principales resultados:
- El algoritmo propuesto basado en el MADDPG demostró una rápida convergencia.
- Las simulaciones numéricas mostraron un rendimiento superior significativo en comparación con los métodos de referencia.
- El algoritmo logró una reducción en el costo total del sistema del 15-60%.
Conclusiones:
- El enfoque basado en el MADDPG es eficaz para optimizar la descarga de computación y la asignación de recursos en redes integradas espacio-aire-mar.
- El algoritmo equilibra con éxito el consumo de energía y la latencia a través de la descarga parcial de tareas.
- Este método ofrece una solución robusta para mejorar el rendimiento de los dispositivos M-IoT en entornos marítimos difíciles.
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