The BigWALL Matrix IoTiZATION Theory: A Graph-Theoretic and AI-Driven Framework for Modeling Human-Thing Interaction, Predictive Behavior, and Autonomy-Preserving Optimization in the Internet of Things

The proliferation of connected devices is reshaping how humans perceive, trust, and ultimately defer to “things”. This paper develops the BigWALL Matrix IoTiZATION Theory into a complete, mathematically rigorous, and artificial-intelligence (AI)-driven framework for modeling the evolving relationship between humans and connected things in the Internet of Things (IoT) era. We formalize human society and the device ecosystem as a heterogeneous multilayer Matrix Point Network and encode their coupling in a single symmetric block operator—the BigWALL matrix $W=\left[A,B;B^{？},C\right]$ . Building on the author’s original behavioral relation $H=f\left(T,U,SI,E\right)$ , we derive a generalized, network-coupled, nonlinear dynamical model of human reliance on things and prove an IoTiZATION Equilibrium Theorem establishing existence, uniqueness, and geometric convergence under an explicit critical-coupling condition ${\gamma }_c$ , beyond which a self-reinforcing dependence regime emerges. We introduce an IoTiZATION index Ω and a complementary autonomy index $\mathcal{A}=1？\Omega$ , and quantify “imposed intelligence” information-theoretically via conditional mutual information. To meet modern technological growth, we embed a predictive-prescriptive AI engine: a relational attention graph neural network (BigWALL-GNN) with temporal recurrence forecasts human behavior, while an autonomy-preserving constrained