DATA MANAGEMENT FRAMEWORKS IN INTERNET OF THINGS (IOT): A COMPREHENSIVE ANALYSIS OF COLLECTION, STORAGE, AND PROCESSING MECHANISMS

This comprehensive article examines the intricate dynamics of data management within Internet of Things (IoT) ecosystems, focusing on the critical aspects of data collection, storage, processing, and emerging challenges. The article analyzes the evolution of IoT architectures from traditional centralized systems to modern hybrid frameworks that leverage both edge and cloud computing capabilities. Through detailed examination of sensor networks, mobile device integration, and actuator-based systems, the study reveals complex interactions between data collection mechanisms and storage infrastructures. The investigation encompasses advanced processing methodologies, including real-time stream processing and machine learning applications, while addressing crucial aspects of security, privacy, and resource optimization. The analysis identifies significant challenges in integration complexity, latency management, and resource constraints, while highlighting promising solutions through emerging technologies and architectural approaches. Key findings demonstrate the importance of balancing distributed processing capabilities with centralized management systems, emphasizing the need for standardized protocols and improved interoperability. The article concludes by outlining future research directions in autonomous system management, advanced analytics integration, and sustainable computing practices, providing valuable insights for researchers and practitioners in the field of IoT data management.