A Real-Time Based System for Personalized Processing Using Fog Computing: A Complete Architecture

The convergence of data and Internet of Things (IoT) has created immense opportunities for personalized recommendation and real-time data insights. However, processing personalized recommendation in real-time from IoT devices presents significant challenges, including high data volume, heterogeneity and latency issues. To address these challenges, Fog Computing model has emerged as a powerful solution. Fog Computing extends the capabilities of cloud computing by bringing computation, storage, and networking resources closer to the data source and end-users. This paper explores the potential of Fog Computing model in handling personalized recommendation by analyzing heterogeneous data from IoT devices. We discuss the key concepts and advantages of Fog Computing, highlighting its suitability for real-time data processing. With its distributed architecture and proximity to IoT devices, Fog Computing model optimizes resource utilization and provides low-latency, real-time insights. By leveraging Fog Computing's model, we could analyze and personalize the heterogeneous data of recommender system in real-time propelling the era of IoT towards a more efficient and responsive future.