Optimized Data Presentation Strategies for Efficient Retrieval and Analysis: A Semantic and Structured Approach

Abstract

In the majority of domains such as public health, industrial safety, and weather forecasting, monitoring of the environment is essential. The legacy monitoring systems do not have real-time data capture and cannot provide structured, queryable data.  Sensors, cloud storage, and semantic web technologies are all employed in IoT-based solutions to address these issues. A system for environmental monitoring based on IoT for capturing, processing, and semantically representing real-time temperature and humidity information is suggested in this research. Semantic technologies drive the transformation of IoT sensor data into an even more valuable and useful form to support smart applications in different domains. Raw sensor data is augmented with context-dependent meaning through ontologies and RDF-based representation, improving machine comprehension and interoperability. It improves data exchange, analysis, and reasoning and is particularly useful for smart cities, industrial control, healthcare, and environmental monitoring. In smart cities, semantic IoT data supports efficient resource management, in industrial automation, process control and predictive maintenance are maximized and Context-aware technologies can provide individualized patient monitoring in the medical field. Semantic web technologies simplify IoT information, making it more structured, accessible, and actionable, paving the way for complex, intelligent systems.