Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Thirunavukkarasan, M.; Remamany, Krishna Priya; Vaishnnave, M.P.; Mary, S.A. Sahaaya Arul; Devarajan, Ganesh Gopal; Mahapatra, Rajendra Prasad Title A name given to the resource Intelligent Retrieval and Secure Content Generation in Consumer Healthcare Electronics Using Quantum Blockchain and Edge-Fog-Cloud Intelligence Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived IEEE Transactions on Consumer Electronics;Volume;72;Issue;1;pp.2235-2242 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/TCE.2025.3650338" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/TCE.2025.3650338</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105026687691?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105026687691?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource consumer applications; Federated learning; privacy preserving; smart homes; spatiotemporal data analytics Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISSN: 983063; CODEN: ITCED Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online