Deep Learning-Based Integration of IoT and Intelligent Infrastructure: Enabling Real-Time Decision-Making in Smart Environments

Abstract

The convergence of Internet of Things (IoT) technology with intelligent infrastructure is transforming urban environments into smart cities capable of dynamic, real-time decision-making. Deep learning plays a pivotal role in this transformation by enabling the analysis of vast and complex IoT data streams to support responsive and adaptive infrastructure systems. This paper explores the deep learning-based integration of IoT and intelligent infrastructure, highlighting the methodologies and technologies that facilitate real-time decision-making in smart environments. We discuss various deep learning architectures, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Graph Neural Networks (GNNs), and their applications in processing IoT data for predictive analytics, anomaly detection, and real-time control. We address challenges related to data heterogeneity, latency, and scalability, and propose solutions for effective data fusion and model deployment. With using deep learning, IoT, and intelligent infrastructure, smart environments can achieve enhanced efficiency, resilience, and adaptability.