Enhancing data management and real-time decision making with IoT, cloud, and fog computing

Abstract

The convergence of Internet of Things (IoT), Cloud computing, and Fog computing, termed as Interconnected Intelligence (II), has revolutionised data management and real-time decision-making across various industries. This study introduces a hybrid architecture that integrates these technologies to optimise resource allocation, reduce latency, and improve decision accuracy. Unlike traditional models that rely heavily on centralised Cloud computing, our approach distributes computational tasks between IoT devices, Fog nodes, and Cloud servers, ensuring efficient real-time processing closer to the data source. The proposed system demonstrated a 20%–30% reduction in latency compared to Cloud-only architectures, and a 25% improvement in resource utilisation through dynamic load balancing between Fog and Cloud layers. Additionally, the system showed an increase in decision accuracy by 15%, enhancing real-time decision-making capabilities in critical applications such as industrial automation, healthcare, and smart urban environments. Data security and privacy were also significantly improved, achieving a 20% reduction in energy consumption by reducing reliance on centralised Cloud resources. These results were validated using real-world datasets from industrial, healthcare, and urban environments, underscoring the architecture's capability to support large-scale IoT deployments. Future research will focus on real-world validation and the development of enhanced dynamic resource management techniques.