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<p>The Internet of Things (IoT) and artificial intelligence (AI) are rapidly advancing technologies with significant implications for healthcare. This study aims to develop and evaluate a remote healthcare monitoring (RHM) model that integrates an auricular therapy device with Software Defined Networking (SDN) and cloud networks to enhance neurostimulator care in smart cities. The auricular therapy device collects brain signal data non-invasively through the outer ear and communicates via Bluetooth between patient and doctor smartphones. The collected data is processed to eliminate noise and normalized before classification using an adaptive fuzzy based Bayesian metasalp neural network (AFBBMNN) combined with levy flight secure offloading analysis within an SDN framework. The processed data is then transmitted to doctors via a cloud-SDN module, consisting of a communication phase, cloud server, and cloud database. Results demonstrate significant improvements in remote health monitoring, enabling early detection of neurological conditions and enhancing healthcare provision within a smart city framework. The proposed method shows promise as an efficient tool for early neurological disease detection and treatment.</p>

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Revolutionizing neurostimulator care: enhancing remote health monitoring through SDN-cloud networks

  • 期刊论文

  • 发表日期：

    2025年1月4日

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  • L. Leo Prasanth,
  • E. Uma

摘要

The Internet of Things (IoT) and artificial intelligence (AI) are rapidly advancing technologies with significant implications for healthcare. This study aims to develop and evaluate a remote healthcare monitoring (RHM) model that integrates an auricular therapy device with Software Defined Networking (SDN) and cloud networks to enhance neurostimulator care in smart cities. The auricular therapy device collects brain signal data non-invasively through the outer ear and communicates via Bluetooth between patient and doctor smartphones. The collected data is processed to eliminate noise and normalized before classification using an adaptive fuzzy based Bayesian metasalp neural network (AFBBMNN) combined with levy flight secure offloading analysis within an SDN framework. The processed data is then transmitted to doctors via a cloud-SDN module, consisting of a communication phase, cloud server, and cloud database. Results demonstrate significant improvements in remote health monitoring, enabling early detection of neurological conditions and enhancing healthcare provision within a smart city framework. The proposed method shows promise as an efficient tool for early neurological disease detection and treatment.