Blockchain Powered Edge Intelligence for U-Healthcare in Privacy Critical and Time Sensitive Environment

Abstract

Edge Intelligence (EI) serves as a critical enabler for privacy-preserving systems, providing artificial intelligence(AI) powered computation and distributed caching services at the edge, thereby minimizing latency and enhancing data privacy. The integration of blockchain technology further strengthens these frameworks by ensuring transactional transparency, auditability, and system-wide reliability through a decentralised model. However, this operational architecture introduces inherent vulnerabilities, primarily due to the extensive data interactions between edge gateways (EGs) and the distributed nature of information storage during service provisioning. To address these challenges, we propose an autonomous computing pipeline along with its interaction topologies tailored for privacy-critical and time-sensitive health applications. The proposed system supports continuous monitoring, real-time heart rate rythm analysis, alert notifications, and robust data processing and aggregation at the edge. It incorporates a dedicated data transaction handler and privacy assurance mechanisms within the EGs. Furthermore, a resource-efficient one-dimensional convolutional neural network (1D-CNN) is proposed for the multiclass classification of arrhythmia, enabling accurate and real-time analysis utilising EGs. A secure access scheme is also defined to manage both off-chain and on-chain data sharing and storage. The proposed model is validated through comprehensive security, performance, and cost analyses, which demonstrate the efficiency and reliability of its fine-grained access control system.