AI-DRIVEN DECISION MAKING IN INDUSTRIAL IOT NETWORKS: A REVIEW OF TECHNIQUES AND FRAMEWORKS

Abstract

The integration of the Industrial Internet of Things (IIoT) with AI-based decision-making frameworks is reshaping industrial operations by enabling continuous monitoring, intelligent automation, and context-aware optimization. As IIoT environments produce high-frequency, heterogeneous sensor data, industries increasingly depend on advanced machine learning models and data-driven reasoning systems to extract actionable insights, anticipate system failures, and support real-time operational decisions. Current research shows considerable progress in areas such as predictive maintenance, anomaly detection, and adaptive control, where AI models enhance accuracy, responsiveness, and system reliability. However, several challenges remain, including severe data imbalance, sensor degradation, dynamic operating conditions, limited edge-level computation, and concerns regarding transparency and trust in automated decisions. Addressing these limitations requires scalable AI architectures, interpretable models, and efficient fusion of multivariate sensor signals to support robust decision pipelines. Emerging approaches such as edge-cloud collaborative intelligence, reinforcement-driven industrial control, and federated analytics demonstrate increasing potential to elevate situational awareness and autonomous responses in complex IIoT networks. By synthesizing current developments, technological gaps, and practical constraints, the discussion highlights how AI-enabled decision-making continues to evolve as a central component for building intelligent, efficient, and resilient industrial ecosystems.