Ensuring the integrity assessment of IoT medical sensors using hesitant fuzzy sets.

Abstract

Objective: The Internet of Medical Things (IoMT) is transforming healthcare systems, but concerns about device integrity and sensitive data are growing. The study aims to develop a framework for evaluating and prioritizing integrity schemes in healthcare for IoT-based medical sensor devices, addressing the challenges of selecting the right authentication solution due to its complexity and intricacy. Methods: A unified health-hesitant fuzzy expert system for IoMT sensor integrity assessment in Saudi Arabia is described in this paper. Medical sensor integrity literature and professionals are contacted first. Delphi is used to gather attributes of integrity approaches while an Internet of Things medical sensor integrity specialist supervises the operation. After collecting characteristics, good assessment criteria are created and the hesitant fuzzy analytic network procedure is used to assess integrity. Results: Functional integrity and measurement accuracy are the biggest factors in IoMT sensor security and integrity, according to assessment. The framework achieves 93%, 94%, and 95% precision, accuracy, and recall compared to current approaches. The framework helps healthcare integrity security professionals and stakeholders assess and resolve IoT medical sensor authentication issues. Conclusion: This health-hesitant fuzzy expert system will let Saudi Arabian and international healthcare stakeholders safely deploy IoMT sensors in the changing healthcare landscape.