Self-organizing complex networks with AI-driven adaptive nodes for optimized connectivity and energy efficiency.

Area of Science:

• Computer Science
• Network Engineering
• Artificial Intelligence

Background:

• Distributed networks require high connectivity and robustness for resilience and efficient communication.
• Energy efficiency is critical for the sustainability and lifespan of energy-constrained networks.
• Self-organizing networks aim for autonomous adaptation and optimization.

Purpose of the Study:

• To introduce an Artificial Intelligence (AI)-enhanced self-organizing network model.
• To enable adaptive nodes to autonomously adjust transmission range for optimized connectivity and reduced energy consumption.
• To enhance network resilience and energy efficiency in distributed systems.

Main Methods:

• Integration of a Multi-Layer Perceptron (MLP)-based decision-making model into adaptive network nodes.
• Leveraging a Hamiltonian-based methodology for globally optimized states of connectivity and energy usage.
• Nodes learn and adapt transmission range autonomously based on local conditions and provided datasets.

Main Results:

• AI-driven adaptive nodes achieve stable and high network connectivity.
• The model demonstrates enhanced robustness against structural disruptions.
• Significant improvements in energy efficiency were observed across various network conditions.
• Emergent global behaviors leading to optimized network states were noted.

Conclusions:

• AI integration significantly enhances the design of complex, self-organizing networks.
• The proposed model fosters scalable, resilient, and energy-efficient distributed systems.
• Autonomous, context-aware range adjustments by nodes maintain network optimization over time.