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Related Experiment Videos

A hierarchical neuromorphic multi agent framework for energy aware and secure 6G resource optimization using Neuro6G

Ahmed Al Nuaim1, Junaid Qayyum2,3

  • 1Department of Management Information System, School of Business, King Faisal University, 31982, Al Ahsa, Saudi Arabia. aaalnuaim@kfu.edu.sa.

Scientific Reports
|May 24, 2026
PubMed
Summary

Neuro6G-Agent, a neuromorphic framework, enhances Sixth-Generation (6G) networks by integrating energy-aware spiking neural networks (EA-SNNs) and multi-agent reinforcement learning for efficient resource management.

Keywords:
6G networksEdge computingEnergy optimizationMulti-agent reinforcement learningNeuromorphic computingResource allocationSpiking neural networksTrust-aware security

Related Experiment Videos

Area of Science:

  • * Artificial Intelligence and Machine Learning
  • * Wireless Communication Systems
  • * Computer Engineering and Architecture

Background:

  • * Sixth-Generation (6G) wireless networks demand intelligent, energy-efficient resource management in distributed systems.
  • * Neuromorphic computing offers event-driven, low-power processing suitable for complex network tasks.
  • * Existing 6G resource management faces challenges in energy sustainability, latency, and security.

Purpose of the Study:

  • * To introduce Neuro6G-Agent, a hierarchical neuromorphic agentic intelligence framework.
  • * To enable energy-conscious cognitive collaboration across cloud-edge-end 6G deployments.
  • * To address energy sustainability, latency, and security in distributed 6G resource management.

Main Methods:

  • * Integration of Energy-Aware Spiking Neural Networks (EA-SNNs) with multi-agent reinforcement learning.
  • * Implementation of a three-tier architecture (cloud, edge, end devices) with dedicated neuromorphic agents.
  • * Utilized adaptive threshold EA-SNNs, distributed trust computation, and hierarchical resource optimization.

Main Results:

  • * Demonstrated a 34.7% reduction in energy consumption.
  • * Achieved a 28.3% decrease in end-to-end latency.
  • * Reached 95.6% security threat detection accuracy, validated across benchmark datasets.

Conclusions:

  • * Neuro6G-Agent effectively addresses key challenges in 6G resource management.
  • * Neuromorphic intelligence is viable for complex optimization in next-generation wireless architectures.
  • * The framework shows significant improvements in energy efficiency, latency, and security.