A Refined Fuzzy MARCOS Approach with Quasi-D-Overlap Functions for Intuitive, Consistent, and Flexible Sensor Selection in IoT-Based Healthcare Systems

Mahmut Baydaş (), Safiye Turgay, Mert Kadem Ömeroğlu, Abdulkadir Aydin, Gıyasettin Baydaş, Željko Stević (), Enes Emre Başar, Murat İnci and Mehmet Selçuk
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Mahmut Baydaş: Faculty of Applied Sciences, Necmettin Erbakan University, Konya 42140, Türkiye
Safiye Turgay: Department of Industrial Engineering, Sakarya University, Sakarya 54050, Türkiye
Mert Kadem Ömeroğlu: Department of Industrial Engineering, Sakarya University, Sakarya 54050, Türkiye
Abdulkadir Aydin: Department of Industrial Engineering, Sakarya University, Sakarya 54050, Türkiye
Gıyasettin Baydaş: Faculty of Medicine, Istanbul Medeniyet University, Istanbul 34700, Türkiye
Željko Stević: Faculty of Transport and Traffic Engineering, University of East Sarajevo, 74000 Doboj, Bosnia and Herzegovina
Enes Emre Başar: Faculty of Economics and Administrative Sciences, Anadolu University, Eskisehir 26470, Türkiye
Murat İnci: First and Emergency Aid Program, Department of Medical Services and Techniques, Ermenek Uysal and Hasan Kalan Vocational School of Health Services, Karamanoglu Mehmetbey University, Karaman 70400, Türkiye
Mehmet Selçuk: Faculty of Medicine, Nigde Omer Halisdemir University, Nigde 51240, Türkiye

Mathematics, 2025, vol. 13, issue 15, 1-34

Abstract: Sensor selection in IoT-based smart healthcare systems is a complex fuzzy decision-making problem due to the presence of numerous uncertain and interdependent evaluation criteria. Traditional fuzzy multi-criteria decision-making (MCDM) approaches often assume independence among criteria and rely on aggregation operators that impose sharp transitions between preference levels. These assumptions can lead to decision outcomes with insufficient differentiation, limited discriminatory capacity, and potential issues in consistency and sensitivity. To overcome these limitations, this study proposes a novel fuzzy decision-making framework by integrating Quasi-D-Overlap functions into the fuzzy MARCOS (Measurement of Alternatives and Ranking According to Compromise Solution) method. Quasi-D-Overlap functions represent a generalized extension of classical overlap operators, capable of capturing partial overlaps and interdependencies among criteria while preserving essential mathematical properties such as associativity and boundedness. This integration enables a more intuitive, flexible, and semantically rich modeling of real-world fuzzy decision problems. In the context of real-time health monitoring, a case study is conducted using a hybrid edge–cloud architecture, involving sensor tasks such as heartrate monitoring and glucose level estimation. The results demonstrate that the proposed method provides greater stability, enhanced discrimination, and improved responsiveness to weight variations compared to traditional fuzzy MCDM techniques. Furthermore, it effectively supports decision-makers in identifying optimal sensor alternatives by balancing critical factors such as accuracy, energy consumption, latency, and error tolerance. Overall, the study fills a significant methodological gap in fuzzy MCDM literature and introduces a robust fuzzy aggregation strategy that facilitates interpretable, consistent, and reliable decision making in dynamic and uncertain healthcare environments.

Keywords: Quasi-D-Overlap functions; Multi-Criteria Decision-Making (MCDM); fuzzy aggregation; IoT-based healthcare; sensor performance evaluation; fuzzy linguistic variables; healthcare monitoring; uncertainty modeling (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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