On the Synergy of IoMT Devices and Ceiling-Mounted Systems for Advanced Medical Data Analytics
The article explores a novel framework that combines Ceiling-Mounted Systems (CMS) and Internet of Medical Things (IoMT) devices to address critical challenges in healthcare data management. By leveraging the capabilities of IoMT devices for real-time data collection, the proposed CMS’s r...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10906484/ |
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| author | Andreas Andreou Constandinos X. Mavromoustakis Evangelos K. Markakis Athina Bourdena George Mastorakis |
| author_facet | Andreas Andreou Constandinos X. Mavromoustakis Evangelos K. Markakis Athina Bourdena George Mastorakis |
| author_sort | Andreas Andreou |
| collection | DOAJ |
| description | The article explores a novel framework that combines Ceiling-Mounted Systems (CMS) and Internet of Medical Things (IoMT) devices to address critical challenges in healthcare data management. By leveraging the capabilities of IoMT devices for real-time data collection, the proposed CMS’s robust sensing, storage, and processing features support efficient resource allocation in hospital environments. The proposed approach achieves approximately 35% reduction in latency, a 25% improvement in energy efficiency, and a 40% decrease in Age of Information (AoI) compared to traditional frameworks. The multi-objective optimization problem minimizes energy consumption and latency while ensuring fairness and timely data collection, which is particularly critical in patient monitoring and time-sensitive diagnostics scenarios. Deep Reinforcement Learning (DRL) methods solve the resource allocation challenge under realistic constraints. Specifically, Twin-Delayed Deep Deterministic Policy Gradients (TD3) and Soft Actor-Critic (SAC) algorithms are adopted to optimize task scheduling and system decisions in dynamic, resource-constrained settings. Simulation results demonstrate that SAC achieves approximately 20% faster convergence and 15% better adaptability in dynamic hospital environments compared to TD3, making it more suitable for real-time healthcare applications. These findings underscore the benefits of integrating IoMT devices with the proposed CMS infrastructures to meet healthcare requirements, such as robust security, high reliability, and real-time responsiveness. |
| format | Article |
| id | doaj-art-db01b7acdeb947a397020c9bb421d6a7 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-db01b7acdeb947a397020c9bb421d6a72025-08-20T03:01:22ZengIEEEIEEE Access2169-35362025-01-0113382553826710.1109/ACCESS.2025.354649510906484On the Synergy of IoMT Devices and Ceiling-Mounted Systems for Advanced Medical Data AnalyticsAndreas Andreou0https://orcid.org/0000-0002-9432-916XConstandinos X. Mavromoustakis1https://orcid.org/0000-0003-0333-8034Evangelos K. Markakis2https://orcid.org/0000-0003-0959-598XAthina Bourdena3https://orcid.org/0009-0008-2049-3910George Mastorakis4https://orcid.org/0000-0002-6733-5652Department of Computer Science, University of Nicosia, Nicosia, CyprusDepartment of Computer Science, University of Nicosia, Nicosia, CyprusDepartment of Electrical and Computer Engineering, Hellenic Mediterranean University, Heraklion, GreeceDepartment of Business Administration and Tourism, Hellenic Mediterranean University Heraklion, Heraklion, GreeceDepartment of Management Science and Technology, Hellenic Mediterranean University, Agios Nikolaos, GreeceThe article explores a novel framework that combines Ceiling-Mounted Systems (CMS) and Internet of Medical Things (IoMT) devices to address critical challenges in healthcare data management. By leveraging the capabilities of IoMT devices for real-time data collection, the proposed CMS’s robust sensing, storage, and processing features support efficient resource allocation in hospital environments. The proposed approach achieves approximately 35% reduction in latency, a 25% improvement in energy efficiency, and a 40% decrease in Age of Information (AoI) compared to traditional frameworks. The multi-objective optimization problem minimizes energy consumption and latency while ensuring fairness and timely data collection, which is particularly critical in patient monitoring and time-sensitive diagnostics scenarios. Deep Reinforcement Learning (DRL) methods solve the resource allocation challenge under realistic constraints. Specifically, Twin-Delayed Deep Deterministic Policy Gradients (TD3) and Soft Actor-Critic (SAC) algorithms are adopted to optimize task scheduling and system decisions in dynamic, resource-constrained settings. Simulation results demonstrate that SAC achieves approximately 20% faster convergence and 15% better adaptability in dynamic hospital environments compared to TD3, making it more suitable for real-time healthcare applications. These findings underscore the benefits of integrating IoMT devices with the proposed CMS infrastructures to meet healthcare requirements, such as robust security, high reliability, and real-time responsiveness.https://ieeexplore.ieee.org/document/10906484/Low latency communicationdeep reinforcement learningInternet of Medical Thingsresource allocationage of information |
| spellingShingle | Andreas Andreou Constandinos X. Mavromoustakis Evangelos K. Markakis Athina Bourdena George Mastorakis On the Synergy of IoMT Devices and Ceiling-Mounted Systems for Advanced Medical Data Analytics IEEE Access Low latency communication deep reinforcement learning Internet of Medical Things resource allocation age of information |
| title | On the Synergy of IoMT Devices and Ceiling-Mounted Systems for Advanced Medical Data Analytics |
| title_full | On the Synergy of IoMT Devices and Ceiling-Mounted Systems for Advanced Medical Data Analytics |
| title_fullStr | On the Synergy of IoMT Devices and Ceiling-Mounted Systems for Advanced Medical Data Analytics |
| title_full_unstemmed | On the Synergy of IoMT Devices and Ceiling-Mounted Systems for Advanced Medical Data Analytics |
| title_short | On the Synergy of IoMT Devices and Ceiling-Mounted Systems for Advanced Medical Data Analytics |
| title_sort | on the synergy of iomt devices and ceiling mounted systems for advanced medical data analytics |
| topic | Low latency communication deep reinforcement learning Internet of Medical Things resource allocation age of information |
| url | https://ieeexplore.ieee.org/document/10906484/ |
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