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Developing IoT Sustainable Real-Time Monitoring Devices for Food Supply Chain Systems Based on Climate Change Using Circular Intuitionistic Fuzzy Set

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dc.citedby11
dc.contributor.authorAlsattar H.A.en_US
dc.contributor.authorMourad N.en_US
dc.contributor.authorZaidan A.A.en_US
dc.contributor.authorDeveci M.en_US
dc.contributor.authorQahtan S.en_US
dc.contributor.authorJayaraman V.en_US
dc.contributor.authorKhalid Z.en_US
dc.contributor.authorid57196317038en_US
dc.contributor.authorid57212672587en_US
dc.contributor.authorid58789685700en_US
dc.contributor.authorid55734383000en_US
dc.contributor.authorid57223984929en_US
dc.contributor.authorid35606770400en_US
dc.contributor.authorid58314479500en_US
dc.date.accessioned2025-03-03T07:48:49Z
dc.date.available2025-03-03T07:48:49Z
dc.date.issued2024
dc.description.abstractInternet of Things (IoT) real-time monitoring devices, which compromise sustainable sensing parameter-based climate change, are developed to minimize food loss and waste to support supply chain systems during natural disasters. Numerous studies have shown that current IoT real-time monitoring devices offer remarkable prospects for future developments involving food supply chain systems with sustainable sensing parameters. Hence, modeling effective IoT real-time monitoring devices to minimize food loss and waste to support supply chain systems is crucial during natural disasters. This modeling process can be classified as multiple-attribute decision-making (MADM) given three issues: 1) the existence of multiple sensing parameter attributes; 2) the uncertainty related to the relative importance of these attributes; and 3) the variability of data. The present study endeavors to combine the fuzzy weighted with zero inconsistency method and circular intuitionistic fuzzy sets (C-IFS-FWZIC) with a new additive ratio assessment (ARAS) to determine ideal IoT real-time monitoring devices to minimize loss and waste and support food supply chain systems during natural disasters. The decision matrix for the study is built by intersecting 54 IoT real-time monitoring devices with ten sustainable sensing parameter attributes. The proposed method is further developed to ascertain the importance level of the sustainable sensing parameter attributes. These data are used in ARAS. Sensitivity analysis and correlation coefficient test are performed to assess the robustness of the proposed method. ? 2014 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/JIOT.2023.3305910
dc.identifier.epage26689
dc.identifier.issue16
dc.identifier.scopus2-s2.0-85168283341
dc.identifier.spage26680
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85168283341&doi=10.1109%2fJIOT.2023.3305910&partnerID=40&md5=8c57e6ef64ef30579c00da2281eb3666
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/37220
dc.identifier.volume11
dc.pagecount9
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleIEEE Internet of Things Journal
dc.subjectClimate change
dc.subjectDecision making
dc.subjectDisasters
dc.subjectFood safety
dc.subjectFood supply
dc.subjectFuzzy sets
dc.subjectInternet of things
dc.subjectSensitivity analysis
dc.subjectSupply chains
dc.subjectUncertainty analysis
dc.subjectWaste management
dc.subjectAdditive ratio assessment
dc.subjectFood security
dc.subjectFood supply chain
dc.subjectIOT
dc.subjectMonitoring device
dc.subjectMultiple attribute decision making
dc.subjectParameters estimation
dc.subjectReal - Time system
dc.subjectReal time monitoring
dc.subjectSupply chain systems
dc.subjectReal time systems
dc.titleDeveloping IoT Sustainable Real-Time Monitoring Devices for Food Supply Chain Systems Based on Climate Change Using Circular Intuitionistic Fuzzy Seten_US
dc.typeArticleen_US
dspace.entity.typePublication
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