Self-Organizing Complex Networks with AI-Driven Adaptive Nodes for Optimized Connectivity and Energy Efficiency

Abstract

High connectivity and robustness are critical requirements in distributed networks, as they ensure resilience, efficient communication, and adaptability in dynamic environments. Additionally, optimizing energy consumption is also paramount for ensuring sustainability of networks composed of energy-constrained devices and prolonging their operational lifespan. In this study, we introduce an Artificial Intelligence (AI)-enhanced self-organizing network model, where each adaptive node autonomously adjusts its transmission power to optimize network connectivity and redundancy while lowering energy consumption. Building on our previous Hamiltonian-based methodology, which is designed to lead networks toward globally optimized states of complete connectivity and minimal energy usage, this research integrates a Multi-Layer Perceptron (MLP)-based decision-making model at each node. By leveraging a dataset from the Hamiltonian approach, each node independently learns and adapts its transmission power in response to local conditions, resulting in emergent global behaviors marked by high connectivity and resilience against structural disruptions. This distributed, MLP-driven adaptability allows nodes to make context-aware power adjustments autonomously, enabling the network to maintain its optimized state over time. Simulation results show that the proposed AI-driven adaptive nodes collectively achieve stable complete connectivity, significant robustness, and optimized energy usage under various conditions, including static and mobile network scenarios. This work contributes to the growing field of self-organizing networks by illustrating the potential of AI to enhance complex network design, supporting the development of scalable, resilient, and energy-efficient distributed systems across diverse applications.