TITLE:
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
AUTHORS:
Wiysenyuy Louis Nyuydzeran
KEYWORDS:
Internet of Things, IoTiZATION, Matrix Point Network, Human-Thing Interaction, Graph Neural Networks, Predictive Analytics, Constrained Reinforcement Learning, Human Autonomy, Sustainable Computing
JOURNAL NAME:
Advances in Internet of Things,
Vol.16 No.3,
July
31,
2026
ABSTRACT: 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=[
A,B;
B
⊤
,C ]
. Building on the author’s original behavioral relation
H=f(
T,U,SI,E
)
, 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
γ
c
, beyond which a self-reinforcing dependence regime emerges. We introduce an IoTiZATION index Ω and a complementary autonomy index
A=1−Ω
, 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 reinforcement-learning controller designs device policies that maximize service utility subject to bounded imposition. A multi-objective formulation links the theory to sustainable development through a utility-imposition-energy Pareto frontier. All quantitative results reported here are illustrative computer simulations on synthetically generated networks—not empirical observations of IoT user behavior. They verify the internal consistency of the model: convergence to equilibrium, a sharp IoTiZATION phase transition at
γ
c
, accurate re-identification of the driver coefficients by regression, and a favorable autonomy-aware operating point. The AI engine is presented as an architectural specification whose training and empirical validation are left to future field deployment. The framework offers a principled, human-centric foundation for the responsible design, prediction, and governance of human-thing interaction.