Prioritization of IoT Challenges in Multi-Specialist Hospital: Pythagorean Fuzzy MCDM Based Approach

Authors

DOI:

https://doi.org/10.2298/YJOR251015008G

Keywords:

IoT, Multi-specialist hospital, Pythagorean fuzzy set, Score function, AHP, WASPAS, CoCoSo

Abstract

The Internet of Things (IoT) is a rapidly growing technology that connects devices and enables data exchange, allowing for advanced automation and optimization in hospitals. Despite its potential benefits, IoT adoption faces several challenges that need to be addressed. In this context, we reviewed the existing literature on IoT adoption challenges and identified the most critical factors that affect the adoption of IoT. Furthermore, the Analytic Hierarchy Process (AHP) is applied to determine the comparative weight of each challenge. Additionally, a comparative analysis of two novel Multi-Criteria Decision Making (MCDM) techniques, namely Weighted Aggregates Sum Product Assessment (WASPAS) and Combined Compromise Solution (CoCoSo) are applied to evaluate the rank of all the alternatives in the Pythagorean fuzzy environment. Furthermore, the sensitivity analysis was conducted to check the model’s suitability and reliability in a scientific manner. The results of the evaluation indicate that the most critical challenges in the adoption of IoT devices in hospitals are privacy and security of patient data, IT infrastructure, top management support, partner collaboration and implementation cost. The findings can help policymakers, industry practitioners and other stakeholders to make informed decisions in their efforts to enhance the adoption and deployment of IoT technology.

References

G. B¨uy¨uk¨ozkan and F. G¨oc¸er, “Digital supply chain: Literature review and a proposed framework for future research,” Computers in Industry, vol. 97, pp. 157–177, 2018. doi: https://doi.org/10.1016/j.compind.2018.02.010

E. Mart´ınez-Caro, J. G. Cegarra-Navarro, A. Garc´ı´ıa-P´e´erez, and M. Fait, “Healthcare service evolution towards the internet of things: An end-user perspective,” Technological Forecasting and Social Change, vol. 136, pp. 268–276, 2018. doi: https://doi.org/10.1016/j.techfore.2018.03.025

S. Luthra, D. Garg, S. K. Mangla, and Y. P. S. Berwal, “Analyzing challenges to internet of things (iot) adoption and diffusion: An indian context,” Procedia Computer Science, vol. 125, pp. 733–739, 2018. doi: https://doi.org/10.1016/j.procs.2017.12.094

H. Alemdar and C. Ersoy, “Wireless sensor networks for healthcare: A survey,” Computer Networks, vol. 54, no. 15, pp. 2688–2710, 2010. doi: https://doi.org/10.1016/j.comnet.2010.05.003

“The tcs global trend study 2015; internet of things: The complete reimaginative force,” Tata Consultancy Services, vol. (accessed on 14 April 2023), p. Available online, 2015. doi: https://www.slideshare.net/tataconsultancyservices/io-t-trendstudy2015-50594380

“Gartner says the internet of things will transform the data center; iot- internet of things,” iot.do, vol. (accessed on 14 April 2023), p. Available online, 2014. doi: https://iot.do/gartnersays-internet-things-will-transform-data-center-2014-03

S. S. Kamble, A. Gunasekaran, and R. Sharma, “Analysis of the driving and dependence power of barriers to adopt industry 4.0 in indian manufacturing industry,” Computers in Industry, vol. 101, pp. 107–119, 2018. doi: https://doi.org/10.1016/j.compind.2018.06.004

K. Rose, S. Eldridge, and L. Chapin, “The internet of things: An overview,” The internet society (ISOC), vol. 80, pp. 1–50, 2015.

K.-S. Wong and M. H. Kim, “Privacy protection for data-driven smart manufacturing systems,” International Journal of Web Services Research, vol. 4, no. 3, pp. 17–32, 2017. doi: https://doi.org/10.4018/IJWSR.2017070102

G. Baldini, M. Botterman, R. Neisse, and M. Tallacchini, “Ethical design in the internet of things,” Science and Engineering Ethics, vol. 24, p. 905–925, 2018. doi: https://doi.org/10.1007/s11948-016-9754-5

K. Azbeg, O. Ouchetto, S. Andaloussi, and L. Fetjah, “A taxonomic review of the use of iot and blockchain in healthcare applications,” IRBM, vol. 43, no. 5, pp. 511–519, 2022. doi: https://doi.org/10.1016/j.irbm.2021.05.003

T. Shakeel, S. Habib, W. Boulila, A. Koubaa, A. R. Javed, M. Rizwan, T. R. Gadekallu, and M. Sufiyan, “A survey on covid-19 impact in the healthcare domain: worldwide market implementation, applications, security and privacy issues, challenges and future prospects,” Complex & Intelligent Systems, vol. 9, p. 1027–1058, 2023. doi: https://doi.org/10.1007/s40747-022-00767-w

T. G. Stavropoulos, A. Papastergiou, L. Mpaltadoros, S. Nikolopoulos, and I. Kompatsiaris, “Iot wearable sensors and devices in elderly care: A literature review,” Sensors, vol. 20, no. 10, pp. 1–22, 2020. doi: https://doi.org/10.3390/s20102826

Z. Ashfaq, A. Rafay, R. Mumtaz, S. M. H. Zaidi, H. Saleem, S. A. R. Zaidi, S. Mumtaz, and A. Haque, “A review of enabling technologies for internet of medical things (iomt) ecosystem,” Ain Shams Engineering Journal, vol. 13, no. 4, p. 101660, 2022. doi: https://doi.org/10.1016/j.asej.2021.101660

J. O. Olmedo-Aguirre, J. Reyes-Campos, G. Alor-Hern´andez, I. Machorro-Cano, L. Rodr´ıguez-Mazahua, and J. L. S´anchez-Cervantes, “Remote healthcare for elderly people using wearables: A review,” Biosensors, vol. 12, no. 2(73), pp. 1–31, 2022. doi: https://doi.org/10.3390/bios12020073

V. Desingh and R. Baskaran, “Internet of things adoption barriers in the indian healthcare supply chain: An ism-fuzzy micmac approach,” International Journal of Health Planning and Management, vol. 37, no. 1, pp. 318–351, 2021. doi: https://doi.org/10.1002/hpm.3331

S. Ahmetoglu, Z. C. Cob, and N. Ali, “A systematic review of internet of things adoption in organizations: Taxonomy, benefits, challenges and critical factors,” Applied Sciences, vol. 22, no. 9, 4117, pp. 1–39, 2022. doi: https://doi.org/10.3390/app12094117

J. H. Nord, A. Koohang, and J. Paliszkiewicz, “The internet of things: Review and theoretical framework,” Expert Systems with Applications, vol. 133, pp. 97–108, 2019. doi: https://doi.org/10.1016/j.eswa.2019.05.014

R. Mishra, B. K. R. Naik, R. D. Raut, and M. Kumar, “Internet of things (iot) adoption challenges in renewable energy: A case study from a developing economy,” Journal of Cleaner Production, vol. 371, no. 133595, 2022. doi: https://doi.org/10.1016/j.jclepro.2022.133595

V. Desingh and B. R, “Internet of things adoption barriers in the indian healthcare supply chain: An ism-fuzzy micmac approach,” International Journal of Health Planning and Management, vol. 37, no. 1, pp. 318–351, 2022. doi: https://doi.org/10.1002/hpm.3331

L. Liu and A. R. Mishra, “Enabling technologies challenges of green internet of things (iot) towards sustainable development in the era of industry 4.0,” Technological and Economic Development of Economy, vol. 1, pp. 1–2, 2022. doi: 10.3846/tede.2022.16520

M. Janssen, S. Luthra, S. Mangla, N. P. Rana, and Y. K. Dwivedi, “Challenges for adopting and implementing iot in smart cities: An integrated micmac-ism approach,” An integrated MICMAC-ISM approach, vol. 29, no. 6, pp. 1589–1616, 2019. doi: https://doi.org/10.1108/INTR-06-2018-0252

Y. Cui, W. Liu, P. Rani, and M. Alrasheedi, “Internet of things (iot) adoption barriers for the circular economy using pythagorean fuzzy swara-cocoso decision-making approach in the manufacturing sector,” Technological Forecasting and Social Change, vol. 171, no. 120951, pp. 1–16, 2021. doi: https://doi.org/10.1016/j.techfore.2021.120951

M. I. Tariq, N. A. Mian, A. Sohail, T. Alyas, and R. Ahmad, “Evaluation of the challenges in the internet of medical things with multicriteria decision making (ahp and topsis) to overcome its obstruction under fuzzy environment,” Personal Communication Technologies for Smart Spaces, vol. 2020, no. 8815651, pp. 1–19, 2020. doi: https://doi.org/10.1155/2020/8815651

A. E. Oke, V. Arowoiya, and O. T. Akomolafe, “An empirical study on challenges to the adoption of the internet of things in the nigerian construction industry,” African Journal of Science Technology Innovation and Development, vol. 14, no. 1, pp. 1–8, 2020. doi: https://doi.org/10.1080/20421338.2020.1819117

S. S. Kamble, A. Gunasekaran, H. Parekh, and S. Joshi, “Modeling the internet of things adoption barriers in food retail supply chains,” Journal of Retailing and Consumer Services, vol. 48, pp. 154–168, 2019. doi: https://doi.org/10.1016/j.jretconser.2019.02.020

S. Luthra, D. Garg, S. K. Mangla, and Y. P. S. Berwal, “Analyzing challenges to internet of things (iot) adoption and diffusion: An indian context,” Procedia Computer Science, vol. 125, pp. 733–739, 2018. doi: https://doi.org/10.1016/j.procs.2017.12.094

C.-W. Hsu and C.-C. Yeh, “Understanding the factors affecting the adoption of the internet of things,” Technology Analysis & Strategic Management, vol. 29, no. 9, pp. 1–15, 2017. doi: https://doi.org/10.1080/09537325.2016.1269160

M. Al-rawashdeh, P. Keikhosrokiani, B. Belaton, M. Alawida, and A. Zwiri, “Iot adoption and application for smart healthcare: A systematic review,” Sensors, vol. 22(14), no. 5377, pp. 1–28, 2022. doi: https://doi.org/10.3390/s22145377

S. Aisyah and M. Dachyar, “Internet of things technology selection for psychotherapy services in psychiatric hospital,” Annual International Conference on Industrial Engineering and Operations Management, Singapore, pp. 1605–1612, 2021.

M. R. Alfarisi and M. Dachyar, “An analysis of internet of things technology selection for hospital laboratory maintenance,” 11th Annual International Conference on Industrial Engineering and Operations Management Singapore, vol. IEOM 2021, no. IEOM Society, pp. 1594–1604, 2021.

A. N. Sukma and M. Dachyar, “Priority design of the telehealth-based internet of things implementation for hospital pulmonology unit,” 11th Annual International Conference on Industrial Engineering and Operations Management Singapore, vol. 9781792361241, pp. 1584–1893, 2021.

X. Huang and S. Nazir, “Evaluating security of internet of medical things using the analytic network process method,” Machine Learning and Applied Cryptography, vol. 2020, no. 8829595, pp. 1–14, 2020. doi: https://doi.org/10.1155/2020/8829595

C. Z. R˜adulescu, A. Alexandru, and L. B˘ajenaru, “Health parameters correlation in an iot monitoring, evaluation and analysis framework for elderly,” 23rd International Conference on System Theory, Control and Computing (ICSTCC), vol. 19167941, pp. 531–536, 2019. doi: https://doi.org/10.1109/ICSTCC.2019.8886117

M. Dachyar and U. Azizia, “Design of unit selection in indonesian hospital to implement internet of things (iot) using dematel-based anp and vikorrug,” e International Conference on Industrial Engineering and Operations Management Pilsen, Czech Republic, vol. July, no. 23-26, pp. 113–119, 2019.

M. N. Bhuiyan, M. M. Rahman, M. M. Billah, and D. Saha, “Internet of things (iot): A review of its enabling technologies in healthcare applications, standards protocols, security, and market opportunities,” IEEE Internet of Things Journal, vol. 8, no. 13, pp. 1047410498, 2021. doi: https://doi.org/10.1109/JIOT.2021.3062630

M. Sharma, S. Joshi, D. Kannan, K. Govindan, R. Singh, and H. C. Purohit, “Internet of things (iot) adoption barriers of smart cities’ waste management: An indian context,” Journal of Cleaner Production, vol. 270, no. 122047, 2020. doi: https://doi.org/10.1016/j.jclepro.2020.122047

S.-P. Wan, Z. Jin, and J.-Y. Dong, “A new order relation for pythagorean fuzzy numbers and application to multi-attribute group decision making,” Knowledge and Information Systems, vol. 62, p. 751–785, 2020. doi: https://doi.org/10.1007/s10115-019-01369-8

L. Wang, Y. Ali, S. Nazir, and M. Niazi, “Isa evaluation framework for security of internet of health things system using ahp-topsis methods,” IEEE Access, vol. 8, no. 19975070, pp. 152316–152332, 2020. doi: https://doi.org/10.1109/ACCESS.2020.3017221

B. Y. Kavus, E. Ayyildiz, P. G. Tas, and A. Taskin, “A hybrid bayesian bwm and pythagorean fuzzy waspas-based decision-making framework for parcel locker location selection problem,” Environmental Science and Pollution Research, 2022. doi: https://doi.org/10.1007/s11356-022-23965-y

M. Deveci, D. Pamucar, I. Gokasar, D. Delen, and L. Mart´ınez, “A fuzzy einsteinbased decision support system for public transportation management at times of pandemic,” Knowledge-Based Systems, vol. 252, no. 109414, pp. 1–18, 2022. doi: https://doi.org/10.1016/j.knosys.2022.109414

R. Singh and N. Bhanot, “An integrated dematel-mmde-ism based approach for analysing the barriers of iot implementation in the manufacturing industry,” International Journal of Production Research, vol. 58, no. 8, pp. 2454–2476, 2019. doi: https://doi.org/10.1080/00207543.2019.1675915

K. Wang, Y. Zhao, R. K. Gangadhari, and Z. Li, “Analyzing the adoption challenges of the internet of things (iot) and artificial intelligence (ai) for smart cities in china,” Sustainability, vol. 13(19), no. 10983, pp. 1–35, 2021. doi: https://doi.org/10.3390/su131910983

V. S. Narwane, A. Gunasekaran, and B. B. Gardas, “Unlocking adoption challenges of iot in indian agricultural and food supply chain,” Smart Agricultural Technology, vol. 2, no. 100035, 2022. doi: https://doi.org/10.1016/j.atech.2022.100035

V. Kumar, P. Vrat, and R. Shankar, “Mcdm model to rank the performance outcomes in the implementation of industry 4.0,” Benchmarking: An International Journal, vol. 31, no. 5, p. 1453–1491, 2024. doi: https://doi.org/10.1108/BIJ-04-2022-0273 45

M. Q. J. Al-Zaidawi and M. C¸evik, “Advanced deep learning models for improved iot network monitoring using hybrid optimization and mcdm techniques,” Symmetry, vol. 17, no. 3, p. 388, 2025. doi: https://doi.org/10.3390/sym17030388

B. Kim and S. Kim, “An ahp-based interface and channel selection for multi-channel mac protocol in iot ecosystem,” Wireless Personal Communications: An International Journal, vol. 93, no. 1, p. 97–118, 2017. doi: https://doi.org/10.1007/s11277-016-3493-4

I. Lokshina and C. J. Lanting, “A qualitative evaluation of iot-driven ehealth: Knowledge management, business models and opportunities, deployment and evolution,” 51st Hawaii International Conference on System Sciences, vol. 978-0-9981331-1-9, pp. 4123–4132, 2018.

F. Alsubaei, A. Abuhussein, V. Shandilya, and S. Shiva, “Iomt-saf: Internet of medical things security assessment framework,” Internet of Things, vol. 8, no. 100123, pp. 1–30, 2019. doi: https://doi.org/10.1016/j.iot.2019.100123

L. Wang, Y. Ali, S. Nazir, and M. Niazi, “Isa evaluation framework for security of internet of health things system using ahp-topsis methods,” IEEE Access, vol. 19975070, pp. 152316152332, 2020. doi: https://doi.org/10.1109/ACCESS.2020.3017221

A. Kiourtis, A. Mavrogiorgou, and D. Kyriazis, “Prioritization of iot devices healthcare data based on attribute scoring and metadata annotation,” 2021 IEEE International Conference on Smart Internet of Things (SmartIoT), no. 21226994, pp. 213–220, 2021. doi: https://doi.org/10.1109/SmartIoT52359.2021.00041

D. Xue, S. Nazir, Z. Peng, and H. Khattak, “Selection and ranking of fog computing-based iot for monitoring of health using the analytic network approach,” Complexity, vol. 2021, no. 9964303, pp. 1–11, 2021. doi: https://doi.org/10.1155/2021/9964303

M. Waqas, T. Alyas, M. M. Ajmal, F. Khan, T. Whangbo, and N. Mahmood, “Evaluation of smart city healthcare features (schf) through machine learning,” 2022 International Conference on Business Analytics for Technology and Security (ICBATS), vol. 21760809, pp. 1–6, 2022. doi: https://doi.org/10.1109/ICBATS54253.2022.9759060

S. Gumina, K. Patten, and J. Gerdes, “The evolution of iot education within an it curriculum,” Education and Information Technologies, vol. 29, p. 6723–6752, 2024. doi: https://doi.org/10.1007/s10639-023-12088-7

S. A. Diwan, “Optimizing guest experience in smart hospitality: Integrated fuzzy-ahp and machine learning for centralized hotel operations with iot,” Alexandria Engineering Journal, vol. 116, pp. 535–547, 2025. doi: https://doi.org/10.1016/j.aej.2024.11.051

A.Guleria and R. K.Bajaj, “Pythagorean fuzzy (r, s)-norm information measure for multicriteria decision-making problem,” Advances in Fuzzy Systems, vol. 8023013, pp. 1–11, 2018. doi: https://doi.org/10.1155/2018/8023013

A. A. Khan, S. Abdullah, M. Shakeel, F. Khan, N. ul Amin, and J. Luo, “A new ranking methodology for pythagorean trapezoidal uncertain linguistic fuzzy sets based on einstein operations,” Symmetry, MDPI, vol. 11, no. 3(440), pp. 1–29, 2019. doi: https://doi.org/10.3390/sym11030440

M. Tang, J. Wang, J. Lu, G. Wei, C. Wei, and Y. Wei, “Dual hesitant pythagorean fuzzy heronian mean operators in multiple attribute decision making,” Mathematics, vol. 7, no. 4(344), pp. 1–27, 2019. doi: https://doi.org/10.3390/math7040344

S. S. Geetha and K. Selvakumari, “A new method for solving pythagorean fuzzy transpotation problem,” PalArch’s Journal of Archaeology of Egypt / Egyptology, vol. 17, no. 7, pp. 4825–4834, 2020.

J. Mahanta and S. Panda, “Distance measure for pythagorean fuzzy sets with varied applications,” Neural Computing and Applications, vol. 33, no. 24, p. 17161–17171, 2021. doi: https://doi.org/10.1007/s00521-021-06308-9

M. Kiris¸ci and N. S¸ims¸ek, “Decision making method related to pythagorean fuzzy soft sets with infectious diseases application,” Journal of King Saud UniversityComputer and Information Sciences, vol. 38, no. 8B, pp. 5968–5978, 2022. doi: https://doi.org/10.1016/j.jksuci.2021.08.010

L. Pan, X. Gao, Y. Deng, and K. H. Cheong, “Constrained pythagorean fuzzy sets and its similarity measure,” IEEE Transactions on Fuzzy Systems, vol. 30, no. 4(21852217), pp. 1–13, 2022. doi: https://doi.org/10.1109/TFUZZ.2021.3052559

F. Labassi, U. U. Rehman, T. Alsuraiheed, T. Mahmood, and M. A. Khan, “A novel approach toward complex pythagorean fuzzy sets and their applications in visualization technology,” IEEE Access, vol. 12, pp. 65838–65855, 2024. doi: https://doi.org/10.1109/ACCESS.2024.3393138

Z. Hussain, S. Alam, R. Hussain, and S. ur Rahman, “New similarity measure of pythagorean fuzzy sets based on the jaccard index with its application to clustering,” Ain Shams Engineering Journal15, vol. 15, no. 1, p. 102294, 2024. doi: https://doi.org/10.1016/j.asej.2023.102294

G. Stoji´c, ˜ Z. Stevi´c, J. Antuchevi˜cien˙e, D. Pamu˜car, and M. Vasiljevi´c, “A novel rough waspas approach for supplier selection in a company manufacturing pvc carpentry products,” Information, MDPI, vol. 9, no. 5(121), pp. 1–16, 2018. doi: https://doi.org/10.3390/info9050121

N. Ghorui, A. Ghosh, E. A. Algehyne, S. P. Mondal, and A. K. Saha, “Ahp-topsis inspired shopping mall site selection problem with fuzzy data,” Mathematics, vol. 8(8), no. 1380, pp. 1–21, 2020. doi: https://doi.org/10.3390/math8081380

M. Zarour, M. T. J. Ansari, M. Alenezi, A. K. Sarkar, M. Faizan, A. Agrawal, R. Kumar, and R. A. Khan, “Evaluating the impact of blockchain models for secure and trustworthy electronic healthcare records,” IEEE Access, vol. 8, no. 19975481, pp. 157959–157973, 2020. doi: https://doi.org/10.1109/ACCESS.2020.3019829

R. S. Bharsakade, P. Acharya, L. Ganapathy, and M. K. Tiwari, “A lean approach to healthcare management using multi criteria decision making,” OPSEARCH, vol. 58, p. 610–635, 2021. doi: https://doi.org/10.1007/s12597-020-00490-5

S. Lahane and R. Kant, “A hybrid pythagorean fuzzy ahp– cocoso framework to rank the performance outcomes of circular supply chain due to adoption of its enablers,” Waste Management, vol. 130, pp. 48–60, 2021. doi: https://doi.org/10.1016/j.wasman.2021.05.013

J. A. Saucedo-Mart´ınez, T. E. Salais-Fierro, R. Rodriguez-Aguilar, and J. A. MarmolejoSaucedo, “Selecting the distribution system using ahp and fuzzy ahp methods,” Mobile Networks and Applications, vol. 29, p. 235–242, 2024. doi: https://doi.org/10.1007/s11036-02302290-9

A. F. Momena, A. Biswas, K. H. Gazi, and S. P. Mondal, “Adaptation challenges of electric vehicles in third world countries,” 2025 9th International Conference on Management Engineering, Software Engineering and Service Sciences, pp. 63–67, 2025. doi: https://doi.org/10.1109/ICMSS64503.2025.00027

M. Deveci, F. Canıtez, and I. G¨okas¸ar, “Waspas and topsis based interval type-2 fuzzy mcdm method for a selection of a car sharing station,” Sustainable Cities and Society, vol. 41, pp. 777–791, 2018. doi: https://doi.org/10.1016/j.scs.2018.05.034

Z. Turskis, N. Goranin, A. Nurusheva, and S. Boranbayev, “A fuzzy waspas-based approach to determine critical information infrastructures of eu sustainable development,” Sustainability, MDPI, vol. 11, no. 2(424), pp. 1–25, 2019. doi: https://doi.org/10.3390/su11020424

U. Bac¸, “An integrated swara-waspas group decision making framework to evaluate smart card systems for public transportation,” Mathematics, vol. 8, no. 10(1723), pp. 1–23, 2020. doi: https://doi.org/10.3390/math8101723 47

V. Simi´c, D. Lazarevi´c, and M. Dobrodolac, “Picture fuzzy waspas method for selecting last-mile delivery mode: a case study of belgrade,” European Transport Research Review, vol. 13, no. 43, pp. 1–22, 2021. doi: https://doi.org/10.1186/s12544-021-00501-6

D.Stanujki´c, D. Karaba˜ sevi´c, G. Popovi´c, E. K. Zavadskas, M. Sara˜cevi´c, P. S. Stanimirovi´c, A. Ulutas¸, V. N. Katsikis, and I. Meidute-Kavaliauskiene, “Comparative analysis of the simple wisp and some prominent mcdm methods: A python approach,” Axioms, MDPI, vol. 10, no. 4(347), pp. 1–14, 2021. doi: https://doi.org/10.3390/axioms10040347

S. Abdullah, H. Ali, A. A. Rahimzai, and S. Khan, “Novel concept of linguistic fractional fuzzy information for effective water filtration decision-making problem based on waspas method,” Scientific Reports, vol. 15, no. 33169, 2025. doi: https://doi.org/10.1038/s41598025-15817-9

A. K. Mukherjee, K. H. Gazi, N. Raisa, A. F. Momena, S. B. Mukherjee, A. Sobczak, S. Salahshour, S. P. Mondal, and A. Ghosh, “Review of alternative ranking methods in multicriteria decision analysis based on waspas and cocoso methodologies,” Yugoslav Journal of Operations Research, pp. 1–37, 2025. doi: https://doi.org/10.2298/YJOR241215037M

M. K. P. Naik, A. Jaiswal, A. Yadav, and T. Singh, “Iot integration for smart agriculture: Prioritizing key factors using waspas approach,” Artificial Intelligence Based Smart and Secured Applications, vol. 2429, p. 452–463, 2025. doi: https://doi.org/10.1007/978-3-03186305-9 31

X. Peng and H. Huang, “Fuzzy decision making method based on cocoso with critic for f inancial risk evaluation,” Technological and Economic Development of Economy, vol. 26, no. 4, pp. 695–724, 2020. doi: https://doi.org/10.3846/tede.2020.11920

X. Peng, X. Zhang, and Z. Luo, “Pythagorean fuzzy mcdm method based on cocoso and critic with score function for 5g industry evaluation,” Artificial Intelligence Review, vol. 53, p. 3813–3847, 2020. doi: https://doi.org/10.1007/s10462-019-09780-x

A.R.Mishra, P. Rani, A. Saha, I. M. Hezam, D.Pamucar, M.Marinovi´c, and K. Pandey, “Assessing the adaptation of internet of things (iot) barriers for smart cities’ waste management using fermatean fuzzy combined compromise solution approach,” IEEE Access, vol. 10, no. 21679210, pp. 37109–37130, 2022. doi: https://doi.org/10.1109/ACCESS.2022.3164096

D. Wei, D. Meng, Y. Rong, Y. Liu, H. Garg, and D. Pamucar, “Fermatean fuzzy schweizer–sklar operators and bwm-entropy-based combined compromise solution approach: An application to green supplier selection,” Entropy, MDPI, vol. 24, no. 6(776), pp. 1–32, 2022. doi: https://doi.org/10.3390/e24060776

T. M. H. Nguyen, V. P. Nguyen, and D. T. Nguyen, “A new hybrid pythagorean fuzzy ahp and cocoso mcdm based approach by adopting artificial intelligence technologies,” Journal of Experimental & Theoretical Artificial Intelligence, vol. 36, no. 7, pp. 1279–1305, 2024. doi: https://doi.org/10.1080/0952813X.2022.2143908

L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, no. 3, pp. 338–353, 1965. doi: https://doi.org/10.1016/S0019-9958(65)90241-X

S. Bakary, M. B. Bouraimar, and I. Badi, “A multi-criteria-decision making methodology to prioritizing telemedicine expansion opportunities,” Journal of Contemporary Decision Science, vol. 2, no. 1, pp. 55–63, 2026.

G. Gupta, P. Singh, J. Limbachiya, K. H. Gazi, M. N. A. Rabih, S. Nagappan, and S. P. Mondal, “Solution of bipolar fuzzy nonlinear equations by fuzzy adomian decomposition method and fuzzy newton-raphson method,” Journal of Uncertain Systems, no. 2550031, pp. 1–31, 2025. doi: https://doi.org/10.1142/S175289092550031X

K. Atanassov and G. Gargov, “Interval valued intuitionistic fuzzy sets,” Fuzzy Sets and Systems, vol. 31, no. 3, pp. 343–349, 1989. doi: https://doi.org/10.1016/0165-0114(89)90205-4

M. R. Khan, K. Ullah, Z. Ali, E. Rak, D. Pamucar, and Y. Shang, “The mcdm approach using preference ranking organization method for enrichment evaluation (promethee) for intuitionistic fuzzy sets with ahp-based weight information,” International Journal of Fuzzy Systems, 2025. doi: https://doi.org/10.1007/s40815-025-02142-6

R. R. Yager, “Pythagorean fuzzy subsets,” Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), no. 13799608, pp. 57–61, 2013. doi: 10.1109/IFSANAFIPS.2013.6608375

M. Sarfraz, “Solar energy system selection: A pythagorean fuzzy rough approach,” International Scientific Spectrum, vol. 2, no. 1, pp. 1–23, 2025.

M. S. Sakrwar, A. Ranadive, and D. Pamucar, “A novel gustafson–kessel based clustering algorithm using-pythagorean fuzzy sets,” Systems and Soft Computing, vol. 7, no. 200345, 2025. doi: https://doi.org/10.1016/j.sasc.2025.200345

S.-P. Wan, Z. Jin, F. Wang, and Z. Jin, “A new ranking method for pythagorean fuzzy numbers,” 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), vol. 17534953, pp. 1–6, 2017. doi: https://doi.org/10.1109/ISKE.2017.8258763

K.Rahman,S.Abdullah, M.S.A.Khan, M.Ibrar, andF.Hussain, “Somebasicoperations on pythagorean fuzzy sets,” Journal of Applied Environmental and Biological Sciences, vol. 7, no. 1, pp. 111–119, 2017.

X. Peng and Y. Yang, “Some results for pythagorean fuzzy sets,” International Journal of Intelligent Systems, vol. 30, no. 11, pp. 1133–1160, 2015. doi: https://doi.org/10.1002/int.21738

R. R. Yager, “Generalized orthopair fuzzy sets,” IEEE Transactions on Fuzzy Systems, vol. 25(5), no. 17240897, pp. 1–11, 2016. doi: https://doi.org/10.1109/TFUZZ.2016.2604005

G. Wei, H. Gao, and Y. Wei, “Some q-rung orthopair fuzzy heronian mean operators in multiple attribute decision making,” International Journal of Intelligent Systems, vol. 33, no. 7, pp. 1426–1458, 2018. doi: https://doi.org/10.1002/int.21985

P. Liu and P. Wang, “Some q-rung orthopair fuzzy aggregation operators and their applications to multiple-attribute decision making,” International Journal of Intelligent Systems, vol. 33, no. 2, pp. 259–280, 2018. doi: https://doi.org/10.1002/int.21927

X. Peng, J. Dai, and H. Garg, “Exponential operation and aggregation operator for qrung orthopair fuzzy set and their decision-making method with a new score function,” International Journal of Intelligent Systems, vol. 33, no. 11, pp. 2255–2282, 2018. doi: https://doi.org/10.1002/int.22028

B. Farhadinia and H. Liao, “Score-based multiple criteria decision making process by using p-rung orthopair fuzzy sets,” Informatica, vol. 32, no. 4, p. 709–739, 2021. doi: https://doi.org/10.15388/20-INFOR412

X. Peng and X. Ma, “Pythagorean fuzzy multi-criteria decision making method based on codas with new score function,” Journal of Intelligent & Fuzzy Systems, vol. 38, no. 3, pp. 3307–3318, 2020. doi: https://doi.org/10.3233/JIFS-190043

T. L. Saaty, “The analytic hierarchy process: Decision making in complex environments,” Quantitative Assessment in Arms Control, p. 285–308, 1980. doi: https://doi.org/10.1007/978-1-4613-2805-6 12

G. B¨uy¨uk¨ozkan and G. C¸ifc¸, “A combined fuzzy ahp and fuzzy topsis based strategic analysis of electronic service quality in healthcare industry,” Expert Systems with Applications, vol. 39, no. 3, pp. 2341–2354, 2012. doi: https://doi.org/10.1016/j.eswa.2011.08.061

K. Ahsan and S. Rahman, “Green public procurement implementation challenges in australian public healthcare sector,” Journal of Cleaner Production, vol. 152, pp. 181–197, 2017. doi: https://doi.org/10.1016/j.jclepro.2017.03.055

M. Sharma and R. Sehrawat, “A hybrid multi-criteria decision-making method for cloud adoption: Evidence from the healthcare sector,” Technology in Society, vol. 61, no. 101258, pp. 1–12, 2020. doi: https://doi.org/10.1016/j.techsoc.2020.101258

E. K. Zavadskas, Z. Turskis, J. Antucheviciene, and A. Zakarevi˜cius, “Optimization of weighted aggregated sum product assessment,” Elektronika ir Elektrotechnika, vol. 122, no. 6, pp. 3–6, 2012. doi: https://doi.org/10.5755/j01.eee.122.6.1810

S. H. Zolfani, M. H. Aghdaie, A. Derakhti, E. K. Zavadskas, and M. H. M. Varzandeh, “Decision making on business issues with foresight perspective; an application of new hybrid mcdm model in shopping mall locating,” Expert Systems with Applications, vol. 40, no. 17, pp. 7111–7121, 2013. doi: https://doi.org/10.1016/j.eswa.2013.06.040

P. Mic¸ and Z. F. Antmen, “A decision-making model based on topsis, waspas, and multimoora methods for university location selection problem,” SAGE Open, vol. 11, no. 3, pp. 1–18, 2021. doi: https://doi.org/10.1177/21582440211040115

S. Lashgari, J. Antuchevi˜cien˙e, A. Delavari, and O. Kheirkhah, “Using qspm and waspas methods for determining outsourcing strategies,” Journal of Business Economics and Management, vol. 15, no. 4, pp. 729–743, 2014. doi: https://doi.org/10.3846/16111699.2014.908789

A. Mardani, M. Nilashi, N. Zakuan, N. Loganathan, S. Soheilirad, M. Z. M. Saman, and O. Ibrahimb, “A systematic review and meta-analysis of swara and waspas methods: Theory and applications with recent fuzzy developments,” Applied Soft Computing, vol. 57, pp. 265292, 2017. doi: https://doi.org/10.1016/j.asoc.2017.03.045

M. Yazdani, P. Zarat´e, E. K. Zavadskas, and Z. Turskis, “A combined compromise solution (cocoso) method for multi-criteria decision-making problems,” Management Decision, vol. 57, no. 3, pp. 1–19, 2018. doi: https://doi.org/10.1108/MD-05-2017-0458

H. Lai, H. Liao, Z. Wen, E. K. Zavadskas, and A. Al-Barakati, “An improved cocoso method with a maximum variance optimization model for cloud service provider selection,” Engineering Economics, vol. 31, no. 4, pp. 411–424, 2020. doi: https://doi.org/10.5755/j01.ee.31.4.24990

M. Popovi´c, “An mcdm approach for personnel selection using the cocoso method,” Journal of Process Management New Technologies, vol. 9, no. 3-4, pp. 78–88, 2021. doi: https://doi.org/10.5937/jouproman2103078P

A.M.de-laHerran, B. Garcia-Zapirain, and A. Mendez-Zorrilla, “Gait analysis methods: An overview of wearable andnon-wearablesystems, highlighting clinical applications,” Sensors, vol. 14, no. 2, pp. 3362–3394, 2014. doi: https://doi.org/10.3390/s140203362

A. N. Sukma and M. Dachyar, “Priority design of the telehealth-based internet of things implementation for hospital pulmonology unit,” International Conference on Industrial Engineering and Operations Management, vol. 3, pp. 1584–1593, 2021.

M. R. Alfarisi and M. Dachyar, “An analysis of internet of things technology selection for hospital laboratory maintenance,” International Conference on Industrial Engineering and Operations Management, pp. 1594–1604, 2021.

S. Aisyah and M. Dachyar, “Internet of things technology selection for psychotherapy services in psychiatric hospital,” International Conference on Industrial Engineering and Operations Management, pp. 1605–1612, 2021.

M. I. Tariq, N. A. Mian, A. Sohail, T. Alyas, and R. Ahmad, “Evaluation of the challenges in the internet of medical things with multicriteria decision making (ahp and topsis) to overcome its obstruction under fuzzy environment,” Mobile Information Systems, no. 8815651, pp. 1–19, 2010. doi: https://doi.org/10.1155/2020/8815651

C. M. Parker and T. Castleman, “Small firm e-business adoption: A critical analysis of theory,” Journal of Enterprise Information Management, vol. 22, no. 1/2, pp. 167–182, 2009. doi: https://doi.org/10.1108/17410390910932812

A. A. Esfahani, H. Ahmadi, M. Nilashi, M. Alizadeh, A. Bashiri, M. A. Farajzadeh, L. Shahmoradi, M. Nobakht, and H. R. Rasouli, “An evaluation model for the implementation of hospital information system in public hospitals using multi-criteria-decision-making (mcdm) approaches,” International Journal of Engineering & Technology, vol. 7, no. 1, pp. 1–18, 2018. doi: https://doi.org/10.14419/ijet.v7i1.8404

K. Shahzad, Z. Jianqiu, A. Zubedi, W. Xin, L. Wang, and M. Hashim, “Danp-based method for determining the adoption of hospital information system,” International Journal of Computer Applications in Technology, vol. 62, no. 1, pp. 57–70, 2020. doi: https://doi.org/10.1504/IJCAT.2020.103900

L. G. Tornatzky and M. Fleischer, “The processes of technological innovation,” MA: D.C. Heath & Company, pp. 1–298, 1990.

A.N.Riyadh,M.S.Akter, andN.Islam, “Theadoptionofe-bankingindevelopingcountries: A theoretical model for smes,” International Review of Business Research Papers Journal, vol. 5, no. 6, pp. 212–230, 2009.

Y.-M. Wang, Y.-S. Wang, and Y.-F. Yang, “Understanding the determinants of rfid adoption in the manufacturing industry,” Technological Forecasting and Social Change, vol. 77, no. 5, pp. 803–815, 2010. doi: https://doi.org/10.1016/j.techfore.2010.03.006

H.Gangwar, H.Date, and R.Ramaswamy, “Understanding determinants of cloud computing adoption using an integrated tam-toe model,” Journal of Enterprise Information Management, vol. 28, no. 1, pp. 107–130, 2015. doi: https://doi.org/10.1108/JEIM-08-2013-0065

F. D. Davis, R. P. Bagozzi, and P. R. Warshaw, “User acceptance of computer technology: A comparison of two theoretical models,” Management Science, vol. 35, no. 8, pp. 982–1003, 1989. doi: https://doi.org/10.1287/mnsc.35.8.982

P. Ratta, A. Kaur, S. Sharma, M. Shabaz, and G. Dhiman, “Application of blockchain and internet of things in healthcare and medical sector: Applications, challenges, and future perspectives,” Artificial Intelligence in Food Quality Improvement, vol. 7608296, pp. 1–20, 2021. doi: https://doi.org/10.1155/2021/7608296

M. B. Haghparast, S. Berehlia, M. Akbari, and A. Sayadi, “Developing and evaluating a proposed health security framework in iot using fuzzy analytic network process method,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, p. 3121–3138, 2021. doi: https://doi.org/10.1007/s12652-020-02472-3

D.Lin, C.K.M.Lee,andW.C.Tai,“Applicationofinterpretive structural modelling for analyzing the factors of iot adoption on supply chains in the chinese agricultural industry,” IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), vol. 17578746, pp. 1347–1351, 2017. doi: https://doi.org/10.1109/IEEM.2017.8290112

Y. B. Zikria, R. Ali, M. K. Afzal, and S. W. Kim, “Next-generation internet of things (iot): Opportunities, challenges, and solutions,” Sensors, vol. 21, no. 4, p. 1174, 2021. doi: https://doi.org/10.3390/s21041174

T. Zhang, L. Gao, C. He, M. Zhang, B. Krishnamachari, and A. S. Avestimehr, “Federated learning for the internet of things: Applications, challenges, and opportunities,” IEEE Internet of Things Magazine, vol. 5, no. 1, pp. 24– 29, 2022. doi: https://doi.org/10.1109/IOTM.004.2100182

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2026-05-13

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Gazi, K. H., Salahshour, S., Behera, P. K., Ghosh, A., & Mondal, S. P. (2026). Prioritization of IoT Challenges in Multi-Specialist Hospital: Pythagorean Fuzzy MCDM Based Approach. Yugoslav Journal of Operations Research. https://doi.org/10.2298/YJOR251015008G

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