A Novel Approach to Multi-Task Prediction in IoT Environments Using Dynamic Heterogeneous Graph Neural Networks

Abstract

The rapid proliferation of Internet of Things (IoT) devices and digital services has created densely interconnected socio-technical networks that challenge conventional risk management. Traditional machine learning models struggle to capture multi-hop relational structure, temporal drift, and severe class imbalance, while static Graph Neural Networks (GNNs) ignore dynamic graph evolution and high-frequency IoT telemetry. This paper proposes an IoT-driven dynamic prediction framework that conceptualizes financial and social-protection infrastructures as assets requiring predictive maintenance. We introduce a multi-task dynamic heterogeneous GNN that jointly estimates credit risk in financial networks and early-warning scores for high-risk social-work cases over evolving graphs linking clients, accounts, devices, households, and practitioners. The architecture combines relation-aware message passing with a Gated Recurrent Unit (GRU) temporal encoder to integrate streaming IoT signals with longitudinal administrative data. We specify an evaluation protocol that compares the framework with logistic regression, gradient boosting, temporal sequence models, and static GCN/GraphSAGE using AUC-ROC, AUC-PR, Brier score, and operational metrics under time-based splits and rolling-window validation; future empirical studies should also employ appropriate significance tests, such as DeLong’s test and bootstrap confidence intervals. This work does not report new experiments on proprietary datasets; instead, it synthesizes recent findings in credit risk, anti-money laundering, IoT-based predictive maintenance, and child-protection analytics to argue that dynamic multi-task graph modeling can better support timely, ethically governed interventions in complex socio-technical systems.