A Proposed Multifaceted Approach for IoT Data Integration in Education Sector for Dissimilar Network

In this heterogeneous scenario, there are multiple sources of multimedia due to the diversity of IoT platforms. The Effective data integration is severely hampered by the growth of multimedia data produced by a wide range of Internet of Things (IoT) devices in heterogeneous contexts. To tackle this problem, a novel approach is designed. This is a complete approach to the smooth integration of multimedia data into the Internet of Things environment by utilizing adaptive processing, semantic data modeling, and effective data fusion. The method is reliable, scalable, and flexible enough to handle a wide range of data kinds, such as sensor readings, photos, audio, and videos [1]. The major challenges are associated with heterogeneous environments in the education sector are: 1) Data is completely in unstructured manner 2) Mapping various streaming data sets from various sources to a single, common schema 3) The problem of Real time data collection, representations and visualization of large datasets during data streaming in the IoT Ecosystem[11]. The goal of this proposed multifaceted approach is to maximize resource utilization, improve educational outcomes, and offer individualized learning experiences by utilizing IoT technologies [2]. A Unified schema is designed in context to perform mapping among different heterogeneous entities. By providing a comprehensive framework and critical analysis for integrating IoT data across dissimilar networks, it seeks to unlock new opportunities for personalized learning, operational efficiency, and data-driven decision-making in the education sector.