ItemArticle

Intelligent Retrieval and Secure Content Generation in Consumer Healthcare Electronics Using Quantum Blockchain and Edge-Fog-Cloud Intelligence

Title
Intelligent Retrieval and Secure Content Generation in Consumer Healthcare Electronics Using Quantum Blockchain and Edge-Fog-Cloud Intelligence
Creator
Thirunavukkarasan, M.; Remamany, Krishna Priya; Vaishnnave, M.P.; Mary, S.A. Sahaaya Arul; Devarajan, Ganesh Gopal; Mahapatra, Rajendra Prasad
Description
To address the growing need for intelligent retrieval and personalized content generation in consumer healthcare electronic devices, this work proposes a secure, scalable, and AI-enhanced framework integrating wearable IoMT devices with edgefogcloud infrastructures. The system leverages quantum blockchain with Quantum Key Distribution (QKD) for tamper-proof storage of sensor data and applies a hybrid Practical Byzantine Fault Tolerance (pBFT) and Proof of Work (PoW) consensus for low-latency validation. At the edge layer, consumer medical devices, such as smart watches, smart patches, and mobile health assistants perform preliminary anomaly detection using lightweight BiLSTM-CNN models integrated with Quantum Neural Networks (QNN). When emergencies or anomalies are detected, the fog layer handles intelligent data retrieval and prioritization based on task urgency, network quality, and energy constraints. The cloud layer supports long-term storage and AI-driven content generation, such as personalized health summaries, alerts, and predictive reports. The architecture enables fast retrieval of user-specific biomedical data across consumer platforms and generates real-time decision support notifications through smartphones, wearables, and connected home healthcare centers. The simulation results demonstrate improved responsiveness, security, and retrieval efficiency compared to traditional IoMT architectures. This framework positions consumer healthcare electronic devices as intelligent, context-aware, and secure systems capable of real-time predictive assistance, data retrieval, and adaptive content generation for smart living environments. 2026 IEEE. All rights reserved.
Source
IEEE Transactions on Consumer Electronics;Volume;72;Issue;1;pp.2235-2242
Date
01-01-2026
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
consumer applications; Federated learning; privacy preserving; smart homes; spatiotemporal data analytics
Coverage
Thirunavukkarasan M., Department of Computational Intelligence, School of Computer Science and Engineering, Vellore Institute of Technology, Tamil Nadu, Vellore, 632014, India; Remamany K.P., Department of Research and Consultancy, College of Engineering and Technology, University of Technology and Applied Sciences, Khasab, 811, Oman; Vaishnnave M.P., Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Deemed to be University), Chennai, 600119, India; Mary S.A.S.A., Department of AIML and Data Science, Faculty of Engineering and Technology, CHRIST University, Kengeri Campus, Karnataka, Bengaluru, 560074, India; Devarajan G.G., Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University, Uttar Pradesh, Noida, 201313, India; Mahapatra R.P., Department of Computer Science and Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Delhi NCR Campus, Uttar Pradesh, 201204, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 983063; CODEN: ITCED
Format
online
Language
English
Type
Article

Citation

Thirunavukkarasan, M.; Remamany, Krishna Priya; Vaishnnave, M.P.; Mary, S.A. Sahaaya Arul; Devarajan, Ganesh Gopal; Mahapatra, Rajendra Prasad, “Intelligent Retrieval and Secure Content Generation in Consumer Healthcare Electronics Using Quantum Blockchain and Edge-Fog-Cloud Intelligence,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/22964.

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