Use of Pervasive Framework and Refined Data Retrieving Techniques for Solving Heterogeneity Problems in IoT Based Information Systems For Healthcare Industry

Abstract

The exponential growth of the Internet of Things IoT has resulted in an unprecedented increase in data generation necessitating efficient storage representation, and analysis techniques that can sustain network lifetime while maintaining high Quality of Service QoS. This thesis explores a range of frameworks and methodologies designed to address the challenges associated with IoT data management. The scope of this work extends across multiple dimensions of IoT network performance, including energy efficiency data processing speed storage optimization security and the scalability of data representation techniques. By incorporating machine learning and blockchain technologies, this thesis aims to provide comprehensive solutions that enhance both the security and performance of IoT networks thereby contributing to the advancement of IoT network design and implementation. The core problem addressed in this thesis is the inherent trade-offs between various QoS parameters such as energy consumption data throughput and processing speed which are critical in IoT environments. Traditional techniques like sleep scheduling and data aggregation improve network lifetime but often compromise speed and energy efficiency. Additionally existing sidechaining and ontology generation methods either lack scalability or fail to incorporate robust security measures further complicating the data management process in IoT networks. Moreover the complexity of semantic analysis in medical event reporting systems compounded by the use of deep learning models that are not optimized for clinical scenarios poses significant challenges to real time data processing and security. In response to these challenges this thesis presents several innovative solutions including the Temporal Trust based Sidechaining Model TTSMSN for storage efficiency the Blockchain based Ontology Generation Model BOGMAS for secure and scalable data representation and a Bioinspired Blockchain based Semantic powered medical event analysis model.