Fog and Mist Computing for Real-Time IOT Analytics

This paper presents a novel approach to real-time Internet of Things (IoT) analytics by integrating Fog and Mist Computing. By shifting data processing closer to the source, at the network edge, this methodology reduces latency and enhances resource efficiency. The study proposes an innovative architecture that utilizes advanced algorithms to enable real-time data analysis, facilitating faster decision-making without overburdening centralized cloud systems. The results demonstrate a significant improvement in system performance, addressing common challenges such as network congestion, bandwidth limitations, and energy consumption. By decentralizing computing tasks, the approach increases the scalability and responsiveness of IoT systems, particularly in sectors like healthcare, smart cities, and industrial automation. This work highlights the practical advantages of Fog and Mist Computing in optimizing large-scale IoT networks, offering a promising solution for real-time data processing in resource-constrained environments.