Selection of IoT service provider for sustainable transport using q-rung orthopair fuzzy CRADIS and unknown weights

• Published in: Applied soft computing Vol. 132; p. 109870
• Main Authors: Krishankumar, Raghunathan, Ecer, Fatih
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2023
• Subjects: CRADIS; Internet-of-things (IoT); IoT service provider selection; Pollution monitoring and control; q-ROFSs; Sustainable Transport; Pollution monitoring and control; CRADIS; IoT service provider selection; Sustainable Transport; Internet-of-things (IoT); q-ROFSs
• ISSN: 1568-4946
• DOI: 10.1016/j.asoc.2022.109870

Internet-of-things (IoT) is a fast-growing technology and sustainable transportation is a crucial application area of IoTs. Selecting an appropriate IoT service provider (IoTSP) is cumbersome and viewed as a multi-criteria decision problem. Recent studies on IoTSP selection inferred that uncertainty and subjectivity during preference elicitation are not flexibly handled. Moreover, they mentioned hesitation and interrelationship are crucial behaviors to be modeled. Motivated by the inferences, this research aims to develop an integrated framework with q-rung orthopair fuzzy sets (q-ROFSs) to evaluate IoTSPs. In this line, the CRITIC method is presented for experts’ importance determination under the q-ROF context. Later, Cronbach’s measure is used for criteria weight calculation with q-ROFSs. Also, an algorithm for prioritization is developed by extending the CRADIS formulation. Lastly, the proposed framework is testified through a case example of IoTSP selection for smart city utilities in India. Results infer that availability, total cost, and security/privacy are crucial criteria for evaluating IoTSPs. Sensitivity and comparison analysis is further conducted to determine the framework’s robustness. This study can assist urban planners, politicians, and other stakeholders in selecting proper IoTSPs when forming sustainable smart cities. •q-Rung orthopair fuzzy information is used for IoT service provider selection.•Weights of experts are determined interactively by using the CRITIC approach.•Criteria weights are consistently determined by using Cronbach’s coefficient.•Ranking algorithm based on CRADIS is developed with q-rung fuzzy context.•Theoretical and statistical metric-based comparison is presented.