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Flexible-Clustering Based on Application Priority to Improve IoMT Efficiency and Dependability

Author

Listed:
  • Amir Masoud Rahmani

    (Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou 64002, Taiwan)

  • Seyedeh Yasaman Hosseini Mirmahaleh

    (Department of Electrical Engineering, Science and Technology, Lille University, 59000 Lille, France)

Abstract

The Internet of Medical Things (IoMT) has overcome the privacy challenges of E-healthcare-based Internet of Things (IoT) systems to protect the joined people’s private records to IoMT infrastructures and support their information security in different layers. By deploying various medical applications, security and privacy are challenging for the IoMT via rising communications between its layers and nodes. Some case studies aimed to solve the issues and provided various methods and protocols to identify the malicious data and information, which had almost overlooked application and service priority to targeting the research and satisfying security. We addressed the dependability and privacy problems of IoMT-based applications by presenting an intelligent algorithm for node mapping and flexible clustering (NFC) via defining a graph and employing a neural network (NN). This work proposes a flexible clustering method to categorize the healthcare service providers for timely detecting faults and identifying the proper servers to join the cluster by considering service and application priority. We improve the application dependability and privacy by about 77.3–83.2% via pruning the defective nodes and employing the neighbor components to support faulty devices’ role. By removing the failed or faulty nodes, the study reduces communication delay and energy consumption, approximately 19.3–21.7% and 10.3–11.8%, respectively.

Suggested Citation

  • Amir Masoud Rahmani & Seyedeh Yasaman Hosseini Mirmahaleh, 2022. "Flexible-Clustering Based on Application Priority to Improve IoMT Efficiency and Dependability," Sustainability, MDPI, vol. 14(17), pages 1-31, August.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:17:p:10666-:d:898843
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    Citations

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    Cited by:

    1. Firuz Kamalov & Behrouz Pourghebleh & Mehdi Gheisari & Yang Liu & Sherif Moussa, 2023. "Internet of Medical Things Privacy and Security: Challenges, Solutions, and Future Trends from a New Perspective," Sustainability, MDPI, vol. 15(4), pages 1-22, February.
    2. Anand Singh Rajawat & S. B. Goyal & Pradeep Bedi & Tony Jan & Md Whaiduzzaman & Mukesh Prasad, 2023. "Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT)," Future Internet, MDPI, vol. 15(8), pages 1-21, August.

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