An AI-Driven Framework for Intelligent Decision Making in IoT-Based Smart Systems

Abstract

The rapid proliferation of Internet of Things (IoT) devices has led to the generation of massive volumes of heterogeneous data across smart environments such as smart cities, healthcare, agriculture, transportation, and industrial automation. Traditional rule-based and static decision-making mechanisms are increasingly inadequate to handle the scale, complexity, and dynamic nature of IoT ecosystems. Artificial Intelligence (AI), particularly machine learning and deep learning techniques, has emerged as a transformative enabler for intelligent, autonomous, and adaptive decision-making in IoT-based smart systems. This paper proposes a comprehensive AI-driven framework for intelligent decision making in IoT-based smart systems, integrating data acquisition, preprocessing, intelligent analytics, and automated action layers. The framework leverages supervised, unsupervised, and reinforcement learning models to extract actionable insights from real-time and historical IoT data. A modular architecture is presented, supporting scalability, interoperability, and real-time responsiveness. Experimental evaluation across representative smart system use cases demonstrates improved decision accuracy, reduced latency, and enhanced system efficiency. The study further discusses challenges related to data privacy, model interpretability, and resource constraints, and outlines future research directions toward explainable AI and edge intelligence.