IoT driven healthcare monitoring with evolutionary optimization and game theory
Abstract In this paper the game theory procedures are applied for healthcare monitoring systems and it is analysed using two types of evolutionary algorithms that incorporate Artificial Intelligence (AI) based events. As most of the existing approaches face challenges in establishing real-time conne...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-99129-y |
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| author | Shitharth Selvarajan Hariprasath Manoharan Taher Al-Shehari Nasser A. Alsadhan Subav Singh |
| author_facet | Shitharth Selvarajan Hariprasath Manoharan Taher Al-Shehari Nasser A. Alsadhan Subav Singh |
| author_sort | Shitharth Selvarajan |
| collection | DOAJ |
| description | Abstract In this paper the game theory procedures are applied for healthcare monitoring systems and it is analysed using two types of evolutionary algorithms that incorporate Artificial Intelligence (AI) based events. As most of the existing approaches face challenges in establishing real-time connectivity, optimizing decision-making processes, and minimizing latency in Internet of Things (IoT)-based healthcare applications the limitations needs to be addressed. Hence with analytical equivalences that are crucial in game theory, a unique system model is developed using a deterministic framework where four key performers are strategically connected to improve decision-making and security against potential data breaches. By incorporating two evolutionary algorithms, the proposed approach optimizes the state of action for each participant while reducing energy consumption and processing delay. The model is validated through four case studies, demonstrating an average improvement of 60% over existing methodologies. These findings highlight the effectiveness of integrating game theory with evolutionary optimization to enhance real-time healthcare monitoring. |
| format | Article |
| id | doaj-art-d53fba7571f049e9a32068b01dbf421b |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-d53fba7571f049e9a32068b01dbf421b2025-08-20T01:48:50ZengNature PortfolioScientific Reports2045-23222025-04-0115112010.1038/s41598-025-99129-yIoT driven healthcare monitoring with evolutionary optimization and game theoryShitharth Selvarajan0Hariprasath Manoharan1Taher Al-Shehari2Nasser A. Alsadhan3Subav Singh4Department of Computer Science, Kebri Dehar UniversityDepartment of Electronics and Communication Engineering, Panimalar Engineering CollegeComputer Skills, Department of Self-Development Skill, Common First Year Deanship, King Saud UniversityComputer Science Department, College of Computer and Information Sciences, King Saud UniversityChitkara Centre for Research and Development, Chitkara UniversityAbstract In this paper the game theory procedures are applied for healthcare monitoring systems and it is analysed using two types of evolutionary algorithms that incorporate Artificial Intelligence (AI) based events. As most of the existing approaches face challenges in establishing real-time connectivity, optimizing decision-making processes, and minimizing latency in Internet of Things (IoT)-based healthcare applications the limitations needs to be addressed. Hence with analytical equivalences that are crucial in game theory, a unique system model is developed using a deterministic framework where four key performers are strategically connected to improve decision-making and security against potential data breaches. By incorporating two evolutionary algorithms, the proposed approach optimizes the state of action for each participant while reducing energy consumption and processing delay. The model is validated through four case studies, demonstrating an average improvement of 60% over existing methodologies. These findings highlight the effectiveness of integrating game theory with evolutionary optimization to enhance real-time healthcare monitoring.https://doi.org/10.1038/s41598-025-99129-yGame theoryEvolutionary algorithmsInternet of things (IoT)Health care |
| spellingShingle | Shitharth Selvarajan Hariprasath Manoharan Taher Al-Shehari Nasser A. Alsadhan Subav Singh IoT driven healthcare monitoring with evolutionary optimization and game theory Scientific Reports Game theory Evolutionary algorithms Internet of things (IoT) Health care |
| title | IoT driven healthcare monitoring with evolutionary optimization and game theory |
| title_full | IoT driven healthcare monitoring with evolutionary optimization and game theory |
| title_fullStr | IoT driven healthcare monitoring with evolutionary optimization and game theory |
| title_full_unstemmed | IoT driven healthcare monitoring with evolutionary optimization and game theory |
| title_short | IoT driven healthcare monitoring with evolutionary optimization and game theory |
| title_sort | iot driven healthcare monitoring with evolutionary optimization and game theory |
| topic | Game theory Evolutionary algorithms Internet of things (IoT) Health care |
| url | https://doi.org/10.1038/s41598-025-99129-y |
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