Explainable Hybrid IoT–Fog–Cloud Framework Leveraging Consumer Electronics for Intelligent and Interpretable Connected Healthcare Systems

초록

The rapid proliferation of IoT-enabled devices in healthcare has enabled real-time monitoring and integrated care, but challenges in data interpretation, security, and scalability hinder clinician trust. This research presents XHIFC: an Explainable Hybrid IoT–Fog–Cloud Healthcare Framework for intelligent and secure hydatid cyst diagnosis. The primary innovation lies in combining explainable deep learning, dynamic resource allocation, and privacy-preserving data management within a unified framework. Specifically, the framework leverages a hybrid ensemble of CBAM-enhanced DenseNet169 and ResNet152V2 at the fog layer with gradient-based attention and feature attribution for interpretability, federated cloud learning for secure model updates, and a reinforcement learning–based Server Utilization Balancing Algorithm (SUBA) to optimize computational efficiency. Blockchain-enabled lightweight cryptographic mechanisms ensure tamper-proof healthcare record management. Experimental evaluations on hydatid cyst imaging datasets demonstrate 94.59% classification accuracy, a 45% reduction in latency, and a 30% improvement in server utilization compared to baseline approaches. The integration of explainability and resource-aware design enhances transparency, clinician trust, and decision verification, providing a secure and scalable foundation for next-generation connected healthcare systems. © 1975-2011 IEEE.

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