DESIGN OF AN ITERATIVE METHOD FOR ENHANCED IOT DATA MANAGEMENT AND PROCESSING IN HEALTHCARE APPLICATIONS

This study addresses the critical need for advanced frameworks in the realm of Internet of Things (IoT) data storage and processing, particularly within the healthcare sector. Traditional approaches often grapple with the complexity and diversity of IoT-generated data, leading to inefficiencies in data management and utilization. Existing methodologies fall short in accurately capturing the intricate relationships among heterogeneous data sources, including medical devices, patient information, and environmental parameters. Moreover, they struggle to process semantic aspects of data effectively, limiting the potential for nuanced analysis and interpretation operations. To address these gaps, this study introduces a novel IoT-ML (Internet of Things - Machine Learning) framework that integrates ontology-based data modeling, semantic Natural Language Processing (NLP), and adaptive Machine Learning (ML) for enhanced healthcare data processing. The proposed model includes a deep learning-based ontology generation engine, Transformer-based semantic interpretation (using BERT), and an adaptive, resource-aware ML schema to support real-time, energy-efficient data analysis.. A unified IoT-ML schema is also developed, integrating semantic and numeric data processing capabilities to support efficient data querying and interoperability across varied IoT platforms. The implementation of machine learning models introduces efficient data encoding strategies, reducing storage requirements while preserving data integrity levels. Adaptive learning algorithms are designed to accommodate the heterogeneity of IoT data, optimizing computational complexity. Furthermore, our models are resource-aware, dynamically adjusting to the computational and storage limitations of IoT devices & scenarios. Empirical evaluation using healthcare datasets from IEEE Data Port and Kaggle demonstrates the superiority of our proposed framework. Results reveal significant improvements over existing methods [4,5,16], including a 4.9% rise in precision, 4.5% improvement in accuracy, 5.9% rise in recall, 10.4% reduce in delay, 8.3% growth in AUC, and 5.5% improvement in specificity for classification tasks. These advancements underscore the potential of our proposed IoT-ML standard in revolutionizing healthcare data management, promising significant impacts on the precision, efficiency, and scalability of IoT data processing and storage sets.