Quantum-enhanced digital twin IoT for efficient healthcare task offloading
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| Publicado en: | SN Applied Sciences vol. 7, no. 6 (Jun 2025), p. 525 |
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Springer Nature B.V.
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 024 | 7 | |a 10.1007/s42452-025-07101-2 |2 doi | |
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| 045 | 2 | |b d20250601 |b d20250630 | |
| 100 | 1 | |a Jameil, Ahmed K. |u Brunel University of London, College of Engineering, Design and Physical Sciences, London, UK (GRID:grid.7728.a) (ISNI:0000 0001 0724 6933); University of Diyala, Department of Computer Engineering, College of Engineering, Baqubah, Iraq (GRID:grid.442846.8) (ISNI:0000 0004 0417 5115) | |
| 245 | 1 | |a Quantum-enhanced digital twin IoT for efficient healthcare task offloading | |
| 260 | |b Springer Nature B.V. |c Jun 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Task offloading frameworks play a crucial role in modern healthcare by optimizing resource utilization, reducing computational burdens, and enabling real-time medical decision-making. However, existing Digital Twin (DT)-based healthcare models suffer from high latency, inefficient resource allocation, cybersecurity vulnerabilities, and computational limitations when processing large-scale patient data. These constraints pose significant risks in time-sensitive applications such as ICU monitoring, robotic-assisted surgeries, and telemedicine. To address these limitations, this paper introduces a Quantum-Enhanced DT-IoT framework, integrating Artificial Intelligence (AI), Quantum Computing (QC), DT, and the Internet of Things (IoT) for real-time, secure, and efficient healthcare task offloading. The proposed system introduces two key optimization algorithms: (1) DTH-ATB-MAPPO, which dynamically adjusts task scheduling and resource distribution, and (2) AQDT-IoT, which enhances computational efficiency and cybersecurity compliance in 6 G-enabled IoT networks. By leveraging Approximate Amplitude Encoding (AAE) and Grover’s search, the framework enhances task offloading efficiency, enabling faster decision-making and optimized resource distribution across 6 G-enabled IoT networks. Empirical evaluations show that quantum preprocessing improved Task Offloading Success Rate (TOSR) by 32% and reduced the Error Rate (ER) by 80%, significantly outperforming traditional DT-based healthcare models. These enhancements enable. Additionally, theoretical analysis demonstrates computational speed enhancements, adaptive cybersecurity mechanisms, and improved system scalability, positioning this framework as a viable candidate for future cloud-based quantum healthcare infrastructures, even in resource-constrained hospital environments.Article Highlights<list list-type="bullet"><list-item></list-item>The integration of quantum computing in healthcare accelerates operational tasks, allowing for smoother task delegation and a reduction in computational faults.<list-item>Advanced quantum models optimize resource allocation, decrease expenses, and prolong the operational lifespan of wearable medical technologies.</list-item><list-item>A robust and scalable quantum architecture fortifies AI-enhanced healthcare, guaranteeing instantaneous diagnostics and remote patient care.</list-item> | |
| 653 | |a Quantum computing | ||
| 653 | |a Task scheduling | ||
| 653 | |a Predictive analytics | ||
| 653 | |a Internet of Things | ||
| 653 | |a Theoretical analysis | ||
| 653 | |a Telemedicine | ||
| 653 | |a Health care | ||
| 653 | |a Optimization | ||
| 653 | |a Resource allocation | ||
| 653 | |a Computer applications | ||
| 653 | |a Robotic surgery | ||
| 653 | |a Telerobotics | ||
| 653 | |a Medical technology | ||
| 653 | |a Efficiency | ||
| 653 | |a Scheduling | ||
| 653 | |a Decision making | ||
| 653 | |a Network latency | ||
| 653 | |a Computation offloading | ||
| 653 | |a Resource scheduling | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Algorithms | ||
| 653 | |a Compliance | ||
| 653 | |a Surveillance | ||
| 653 | |a Latency | ||
| 653 | |a Resource utilization | ||
| 653 | |a Patients | ||
| 653 | |a Real time | ||
| 653 | |a Life span | ||
| 653 | |a Cybersecurity | ||
| 653 | |a Mental task performance | ||
| 653 | |a Data processing | ||
| 653 | |a Digital twins | ||
| 653 | |a Cloud computing | ||
| 653 | |a Data collection | ||
| 653 | |a Data transmission | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Al-Raweshidy, Hamed |u Brunel University of London, College of Engineering, Design and Physical Sciences, London, UK (GRID:grid.7728.a) (ISNI:0000 0001 0724 6933) | |
| 773 | 0 | |t SN Applied Sciences |g vol. 7, no. 6 (Jun 2025), p. 525 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3208257343/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3208257343/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3208257343/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |