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EMO-PEGASIS: Un protocolo de aprendizaje automático de doble fase para la optimización del retardo energético en WSN

Abdulla Juwaied1

  • 1Institute of Applied Computer Science, Lodz University of Technology, ul. Stefanowskiego 18, 90-537 Lodz, Poland.

Sensors (Basel, Switzerland)
|January 28, 2026
PubMed
Resumen

Enhanced Multi-Objective PEGASIS (EMO-PEGASIS) mejora las redes de sensores inalámbricos mediante el uso de aprendizaje automático para una mayor eficiencia energética y una reducción del retardo. Este protocolo aumenta significativamente la vida útil y la estabilidad de la red.

Palabras clave:
Vecinos más cercanos (K-NN)K-meansPEGASISeficiencia energéticaaprendizaje automáticooptimización multiobjetivoretraso de transmisiónredes de sensores inalámbricos (WSN)

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Área de la Ciencia:

  • Ciencias de la Computación
  • Ingeniería Eléctrica
  • Ingeniería de Redes

Sus antecedentes:

  • Las redes de sensores inalámbricos (WSN) se enfrentan a una compensación crítica entre la conservación de energía y el retardo de la transmisión de datos.
  • Los protocolos existentes como PEGASIS ofrecen eficiencia energética pero sufren de alta latencia y distribución desequilibrada de la carga.
  • La formación de clústeres subóptima en los protocolos WSN tradicionales limita el rendimiento general de la red.

Objetivo del estudio:

  • Introducir un protocolo Enhanced Multi-Objective PEGASIS (EMO-PEGASIS) para WSN.
  • Abordar las limitaciones de los protocolos existentes en la gestión del consumo de energía y el retardo de la transmisión de datos.
  • Aprovechar el aprendizaje automático para optimizar el rendimiento de las WSN en términos de energía, retardo y vida útil de la red.

Principales métodos:

  • Se empleó una estrategia de aprendizaje automático de doble fase para el diseño e implementación del protocolo.
  • Se utilizó la agrupación K-means para la partición espacial robusta de la red.
  • Se utilizó la clasificación K-Nearest Neighbours (K-NN) para el enrutamiento adaptativo e inteligente.

Principales resultados:

  • EMO-PEGASIS logró una reducción del 45% en el consumo promedio de energía en comparación con PEGASIS.
  • El retardo de extremo a extremo se redujo en un 38% y la vida útil de la red aumentó en un 67%.
  • El protocolo demostró una estabilidad mejorada, un equilibrio de carga efectivo y una tasa de entrega de paquetes del 96,8%.

Conclusiones:

  • El protocolo EMO-PEGASIS aborda eficazmente el problema de la optimización multiobjetivo en WSN.
  • La integración de técnicas de aprendizaje automático mejora significativamente el rendimiento de las WSN.
  • EMO-PEGASIS proporciona una optimización multiobjetivo fiable para entornos WSN con restricciones de energía y retardo.