A Novel Generalization of Q-Rung Orthopair Fuzzy Aczel Alsina Aggregation Operators and Their Application in Wireless Sensor Networks
Q-rung orthopair fuzzy sets have been proven to be highly effective at handling uncertain data and have gained importance in decision-making processes. Torra’s hesitant fuzzy model, on the other hand, offers a more generalized approach to fuzzy sets. Both of these frameworks have demonstrated their...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/19/8105 |
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| author | Wajid Ali Tanzeela Shaheen Iftikhar Ul Haq Tmader Alballa Alhanouf Alburaikan Hamiden Abd El-Wahed Khalifa |
| author_facet | Wajid Ali Tanzeela Shaheen Iftikhar Ul Haq Tmader Alballa Alhanouf Alburaikan Hamiden Abd El-Wahed Khalifa |
| author_sort | Wajid Ali |
| collection | DOAJ |
| description | Q-rung orthopair fuzzy sets have been proven to be highly effective at handling uncertain data and have gained importance in decision-making processes. Torra’s hesitant fuzzy model, on the other hand, offers a more generalized approach to fuzzy sets. Both of these frameworks have demonstrated their efficiency in decision algorithms, with numerous scholars contributing established theories to this research domain. In this paper, recognizing the significance of these frameworks, we amalgamated their principles to create a novel model known as Q-rung orthopair hesitant fuzzy sets. Additionally, we undertook an exploration of Aczel–Alsina aggregation operators within this innovative context. This exploration resulted in the development of a series of aggregation operators, including Q-rung orthopair hesitant fuzzy Aczel–Alsina weighted average, Q-rung orthopair hesitant fuzzy Aczel–Alsina ordered weighted average, and Q-rung orthopair hesitant fuzzy Aczel–Alsina hybrid weighted average operators. Our research also involved a detailed analysis of the effects of two crucial parameters: <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>λ</mi></mrow></semantics></math></inline-formula>, associated with Aczel–Alsina aggregation operators, and N, related to Q-rung orthopair hesitant fuzzy sets. These parameter variations were shown to have a profound impact on the ranking of alternatives, as visually depicted in the paper. Furthermore, we delved into the realm of Wireless Sensor Networks (WSN), a prominent and emerging network technology. Our paper comprehensively explored how our proposed model could be applied in the context of WSNs, particularly in the context of selecting the optimal gateway node, which holds significant importance for companies operating in this domain. In conclusion, we wrapped up the paper with the authors’ suggestions and a comprehensive summary of our findings. |
| first_indexed | 2024-03-10T21:35:34Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T21:35:34Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4ac3445242be4db6bb654080e353a19d2023-11-19T15:02:48ZengMDPI AGSensors1424-82202023-09-012319810510.3390/s23198105A Novel Generalization of Q-Rung Orthopair Fuzzy Aczel Alsina Aggregation Operators and Their Application in Wireless Sensor NetworksWajid Ali0Tanzeela Shaheen1Iftikhar Ul Haq2Tmader Alballa3Alhanouf Alburaikan4Hamiden Abd El-Wahed Khalifa5Department of Mathematics, Air University, PAF Complex E-9, Islamabad 44230, PakistanDepartment of Mathematics, Air University, PAF Complex E-9, Islamabad 44230, PakistanDepartment of Mathematics, Air University, PAF Complex E-9, Islamabad 44230, PakistanDepartment of Mathematics, College of Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Mathematics, College of Science and Arts, Qassim University, Al-Badaya 51951, Saudi ArabiaDepartment of Mathematics, College of Science and Arts, Qassim University, Al-Badaya 51951, Saudi ArabiaQ-rung orthopair fuzzy sets have been proven to be highly effective at handling uncertain data and have gained importance in decision-making processes. Torra’s hesitant fuzzy model, on the other hand, offers a more generalized approach to fuzzy sets. Both of these frameworks have demonstrated their efficiency in decision algorithms, with numerous scholars contributing established theories to this research domain. In this paper, recognizing the significance of these frameworks, we amalgamated their principles to create a novel model known as Q-rung orthopair hesitant fuzzy sets. Additionally, we undertook an exploration of Aczel–Alsina aggregation operators within this innovative context. This exploration resulted in the development of a series of aggregation operators, including Q-rung orthopair hesitant fuzzy Aczel–Alsina weighted average, Q-rung orthopair hesitant fuzzy Aczel–Alsina ordered weighted average, and Q-rung orthopair hesitant fuzzy Aczel–Alsina hybrid weighted average operators. Our research also involved a detailed analysis of the effects of two crucial parameters: <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>λ</mi></mrow></semantics></math></inline-formula>, associated with Aczel–Alsina aggregation operators, and N, related to Q-rung orthopair hesitant fuzzy sets. These parameter variations were shown to have a profound impact on the ranking of alternatives, as visually depicted in the paper. Furthermore, we delved into the realm of Wireless Sensor Networks (WSN), a prominent and emerging network technology. Our paper comprehensively explored how our proposed model could be applied in the context of WSNs, particularly in the context of selecting the optimal gateway node, which holds significant importance for companies operating in this domain. In conclusion, we wrapped up the paper with the authors’ suggestions and a comprehensive summary of our findings.https://www.mdpi.com/1424-8220/23/19/8105Q-rung orthopair hesitant fuzzy setsdecision makingwireless sensor networksoptimizationAczel–Alsina aggregation operatorsefficiency |
| spellingShingle | Wajid Ali Tanzeela Shaheen Iftikhar Ul Haq Tmader Alballa Alhanouf Alburaikan Hamiden Abd El-Wahed Khalifa A Novel Generalization of Q-Rung Orthopair Fuzzy Aczel Alsina Aggregation Operators and Their Application in Wireless Sensor Networks Sensors Q-rung orthopair hesitant fuzzy sets decision making wireless sensor networks optimization Aczel–Alsina aggregation operators efficiency |
| title | A Novel Generalization of Q-Rung Orthopair Fuzzy Aczel Alsina Aggregation Operators and Their Application in Wireless Sensor Networks |
| title_full | A Novel Generalization of Q-Rung Orthopair Fuzzy Aczel Alsina Aggregation Operators and Their Application in Wireless Sensor Networks |
| title_fullStr | A Novel Generalization of Q-Rung Orthopair Fuzzy Aczel Alsina Aggregation Operators and Their Application in Wireless Sensor Networks |
| title_full_unstemmed | A Novel Generalization of Q-Rung Orthopair Fuzzy Aczel Alsina Aggregation Operators and Their Application in Wireless Sensor Networks |
| title_short | A Novel Generalization of Q-Rung Orthopair Fuzzy Aczel Alsina Aggregation Operators and Their Application in Wireless Sensor Networks |
| title_sort | novel generalization of q rung orthopair fuzzy aczel alsina aggregation operators and their application in wireless sensor networks |
| topic | Q-rung orthopair hesitant fuzzy sets decision making wireless sensor networks optimization Aczel–Alsina aggregation operators efficiency |
| url | https://www.mdpi.com/1424-8220/23/19/8105 |
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