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Un método de asignación de recursos del internet cognitivo de las cosas basado en un algoritmo de aprendizaje por
Rong Wang1, Yanjin Shen1, Dongtao Wang2
1Hunan Automotive Engineering Vocational University, Zhuzhou, 412000, China.
Scientific reports
|February 7, 2026
Resumen
Este estudio optimiza la asignación de recursos para el Internet cognitivo de las cosas (CIoT) en vehículos para minimizar la Edad de la Información (AoI). El algoritmo mejorado de optimización de políticas proximal multiagente (IMAPPO) reduce significativamente la latencia de los datos para los vehículos conectados.
Área de la Ciencia:
- Comunicaciones inalámbricas
- Internet de las cosas
- Inteligencia artificial
Sus antecedentes:
- La comunicación entre vehículos se enfrenta a desafíos de acceso dinámico al espectro y condiciones del canal.
- La puntualidad es fundamental para las redes de vehículos de alta velocidad, lo que convierte a la Edad de la Información (AoI) en una métrica de rendimiento clave.
Objetivo del estudio:
- Minimizar la Edad de la Información (AoI) en redes de Internet cognitivo de las cosas (CIoT) para vehículos.
- Abordar la selección conjunta de canales y el control de potencia para la asignación de recursos bajo movilidad de alta velocidad.
Principales métodos:
- Modelar el problema como un Proceso de Decisión de Markov (MDP) con una función de recompensa personalizada.
- Emplear un enfoque de aprendizaje por refuerzo multiagente con vehículos como agentes.
- Proponer un algoritmo mejorado de Optimización de Políticas Proximal Multiagente (IMAPPO) con redes Actor mejoradas para espacios de acción híbridos.
Principales resultados:
- El algoritmo IMAPPO propuesto gestiona eficazmente la asignación de recursos en entornos vehiculares dinámicos.
- Las simulaciones confirman la viabilidad y eficacia del algoritmo para reducir la AoI del sistema.
- El esquema de asignación de recursos CIoT supera significativamente a los métodos alternativos para reducir la AoI de los usuarios de vehículos.
Conclusiones:
- El algoritmo IMAPPO proporciona una solución eficaz para la asignación de recursos en redes vehiculares CIoT.
- La optimización de la AoI es crucial para aplicaciones en tiempo real en vehículos conectados y autónomos.
- Esta investigación avanza la gestión inteligente de recursos para futuros sistemas de comunicación vehicular.
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