ItemArticle

Quantum-enhanced neuro-fusion framework for intelligent decision-making in smart home IoT surveillance

Title
Quantum-enhanced neuro-fusion framework for intelligent decision-making in smart home IoT surveillance
Creator
Eugine Prince, M.; Rathi Devi, T.; Anu Disney, D.; Kannan, K.; Ramesh, K.; Sujatha Therese, P.
Description
Smart-home surveillance systems increasingly rely on heterogeneous IoT data streams, requiring efficient fusion, scalability, and robustness under noisy sensing conditions. This paper proposes a Quantum-Inspired Deep Neuro-Fusion Architecture (QDNFA) for anomaly detection in edgecloud IoT environments. The framework integrates modular encoders, temporal alignment, and a quantum-inspired optimisation mechanism to support multi-modal data processing while maintaining real-time performance. Experimental evaluation is conducted on the CASAS Smart Home dataset to validate sensor-centric anomaly detection, scalability across multiple devices, and edgecloud inference efficiency. While the architecture is designed to support audio and video modalities, the present study focuses on low-dimensional sensor data, and large-scale benchmarking on audiovisual surveillance datasets is identified as future work. Results demonstrate improved detection accuracy and reduced latency compared to baseline methods in sensor-driven smart-home scenarios. 2026 The Author(s).
Source
Franklin Open;Volume;15;Issue;;Article No.;100561;
Date
01-01-2026
Publisher
Elsevier B.V.
Subject
Adversarial resilience; AI-driven IoT systems; Anomaly detection; Deep neuro-fusion; Edge computing; Internet of things; Multi-modal data fusion; Quantum-inspired optimization; Real-time surveillance; Smart home security
Coverage
Eugine Prince M., Department of Physics, S. T. Hindu College, Tamil Nadu, Nagercoil, India; Rathi Devi T., Department of Computer Science and Engineering, Christ University, Karnataka, Bengaluru, India; Anu Disney D., Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Tamil Nadu, Chennai, India; Kannan K., Department of Electronics and Communication Engineering, R. M. K. College of Engineering and Technology, Tamil Nadu, Thiruvallur, India; Ramesh K., Department of Electronics and Communication Engineering, Dhanalakshmi Srinivasan College of Engineering and Technology, Tamil Nadu, Chennai, India; Sujatha Therese P., Department of Electrical and Electronics Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Tamil Nadu, India
Rights
All Open Access; Gold Open Access; Green Open Access
Relation
ISSN: 27731871;
Format
online
Language
English
Type
Article

Citation

Eugine Prince, M.; Rathi Devi, T.; Anu Disney, D.; Kannan, K.; Ramesh, K.; Sujatha Therese, P., “Quantum-enhanced neuro-fusion framework for intelligent decision-making in smart home IoT surveillance,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/22262.

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