A Resource Management Model for Healthcare Internet of Things Using Deep Learning and Bio-inspired Algorithms

A Resource Management Model for Healthcare Internet of Things Using Deep Learning and Bio-inspired Algorithms

Автор: Girish Wali, Chetan Bulla

Журнал: International Journal of Information Engineering and Electronic Business @ijieeb

Статья в выпуске: 1 vol.17, 2025 года.

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Healthcare IoT seeks to use technology to better patient care, optimize operational efficiency, and provide remote monitoring and management of health issues. Resource management is crucial in the context of Health Internet of Things (HIoT) since it enhances the performance of healthcare services. This research paper proposes a resource management model in healthcare Internet of Things (IoT) by using deep learning and bio-inspired algorithms. A deep learning model LSTM model is used to resource failure prediction and bio-inspired algorithms are used for resource allocation and load balancing. An accurate prediction of resource utilization and effective resource management algorithm will improve the overall performance of IoT services for Health care application. The proposed approach incorporates deep learning methods to identify and anticipate anomalies, enabling the proactive identification of future problems or resource failures and resource utilization. In addition, bio-inspired algorithms are used to dynamically distribute resources and optimize system performance in real-time. The efficacy of the proposed fault-tolerant method is proved by extensive simulations and performance tests. The experiment results show the improvement in performance parameters as compared to state-of-the-art resource management models.

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HIoT, resource allocation, management, fog computing, optimization, fault tolerance

Короткий адрес: https://sciup.org/15019661

IDR: 15019661   |   DOI: 10.5815/ijieeb.2025.01.05

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