AI‑enabled resource management for 6G‑IoT hybrid systems: a unified simulation platform

Abstract The integration of Artificial Intelligence (AI) with sixth‑generation (6G) communication technologies is expected to transform resource management in Internet of Things (IoT) systems, where reliability, latency, and adaptability are critical. However, hybrid 6G–IoT environments combine two fundamentally different subsystems, resource‑constrained IoT devices and ultra‑high‑performance 6G infrastructure, creating a highly complex operational space with a multitude of interacting parameters. This results in severe heterogeneity across frequency bands, latency requirements, traffic behaviors, and computational capabilities. Such heterogeneity makes end‑to‑end modeling, resource management, and optimization extremely challenging when capturing the dynamics of both IoT endpoints and 6G networks. To address these challenges, this work presents 6G‑IoT‑Sim, a modular 6G‑enabled IoT simulator that provides a unified and extensible platform for implementing and analyzing 6G‑IoT networks. The platform incorporates 6G architectural capabilities, a configurable network‑design interface, a diverse dataset generator tool, and a real‑time monitoring dashboard. Additionally, it integrates a hybrid AI-based framework comprising different AI models and optimization techniques for intelligent resource management. A multidimensional performance evaluation demonstrates the effectiveness of the framework for intelligent resource management across diverse 6G‑IoT conditions. The results further highlight the potential of the proposed simulator as a flexible and extendable platform for advancing adaptive solutions in 6G‑enabled IoT systems.