AIOT SYSTEM BASED ON GRAPH THEORY FOR THE MEDICAL FIELD

DOI 10.31673/2412-4338.2025.028526

Abstract

Abstract: This study presents a comprehensive mathematical model for applying graph theory in AIoT (Artificial Intelligence of Things) systems designed for health monitoring. The model accounts for the structure of the sensor network, the temporal dynamics of physiological parameters, and the integration of intelligent data processing methods based on graph neural networks. IoT devices (such as heart rate monitors, glucose sensors,

channels. The developed model includes an adjacency matrix, vector representations of sensor parameters, and forecasting mechanisms using GCN and Temporal GNN architectures. Special attention is given to anomaly detection algorithms based on deviations between actual and predicted values of physiological signals. Mathematical approaches to threshold interpretation of these deviations are proposed, allowing timely identification of potential health threats. The study also considers dynamic optimization of graph structure through adaptive learning from input data, enabling the system to refine inter-sensor dependencies in real time. The findings of this research can be applied in the development of intelligent health monitoring systems for clinical settings, remote patient care, and mobile health solutions aimed at chronic disease prevention. The proposed model forms a foundation for the advancement of adaptive, personalized AIoT platforms in healthcare, capable of self-learning, forecasting, and real-time decision-making.

Keywords: Internet of Things, architecture, optimisation, automation, sensor, modelling, information system, network, graph theory, artificial intelligence.