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

TL;DR

This paper introduces a blockchain-enabled edge intelligence framework for privacy-critical U-healthcare, combining autonomous computing, real-time disease detection, and secure data sharing to enhance reliability, privacy, and efficiency in sensitive health environments.

Contribution

It proposes a novel autonomous computing model with a resource-efficient 1D-CNN for arrhythmia classification and a secure access scheme for privacy-preserving data sharing in blockchain-powered edge healthcare.

Findings

Demonstrates high accuracy of 1D-CNN in arrhythmia detection.

Shows improved security and privacy in data transactions.

Validates system efficiency through security and performance analyses.

Abstract

Edge Intelligence (EI) serves as a critical enabler for privacy-preserving systems by providing AI-empowered computation and distributed caching services at the edge, thereby minimizing latency and enhancing data privacy. The integration of blockchain technology further augments EI frameworks by ensuring transactional transparency, auditability, and system-wide reliability through a decentralized network model. However, the operational architecture of such systems introduces inherent vulnerabilities, particularly 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 model along with its interaction topologies tailored for privacy-critical and time-sensitive health applications. The system supports continuous monitoring, real-time alert notifications, disease detection, and robust data processing and aggregation. It also includes a data transaction handler and mechanisms for ensuring privacy at the EGs. Moreover, a resource-efficient one-dimensional convolutional neural network (1D-CNN) is proposed for the multiclass classification of arrhythmia, enabling accurate and real-time analysis of constrained EGs. Furthermore, a secure access scheme is defined to manage both off-chain and on-chain data sharing and storage. To validate the proposed model, comprehensive security, performance, and cost analyses are conducted, demonstrating the efficiency and reliability of the fine-grained access control scheme.