A Novel Semantic IoT Middleware for Secure Data Management: Blockchain and AI-Driven Context Awareness

Mahmoud Elkhodr (), Samiya Khan and Ergun Gide
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Mahmoud Elkhodr: School of Engineering and Technology, Central Queensland University, Sydney, NSW 2000, Australia
Samiya Khan: School of Computing and Mathematical Sciences, University of Greenwich, London SE10 9LS, UK
Ergun Gide: School of Engineering and Technology, Central Queensland University, Sydney, NSW 2000, Australia

Future Internet, 2024, vol. 16, issue 1, 1-31

Abstract: In the modern digital landscape of the Internet of Things (IoT), data interoperability and heterogeneity present critical challenges, particularly with the increasing complexity of IoT systems and networks. Addressing these challenges, while ensuring data security and user trust, is pivotal. This paper proposes a novel Semantic IoT Middleware (SIM) for healthcare. The architecture of this middleware comprises the following main processes: data generation, semantic annotation, security encryption, and semantic operations. The data generation module facilitates seamless data and event sourcing, while the Semantic Annotation Component assigns structured vocabulary for uniformity. SIM adopts blockchain technology to provide enhanced data security, and its layered approach ensures robust interoperability and intuitive user-centric operations for IoT systems. The security encryption module offers data protection, and the semantic operations module underpins data processing and integration. A distinctive feature of this middleware is its proficiency in service integration, leveraging semantic descriptions augmented by user feedback. Additionally, SIM integrates artificial intelligence (AI) feedback mechanisms to continuously refine and optimise the middleware’s operational efficiency.

Keywords: Internet of Things (IoT); blockchain; artificial intelligence (AI); semantic annotation; data security; user-centric operations; healthcare (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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