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ZENODO
Dataset . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
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Securing IoT–Cloud Sensing Systems for Viable Renewable Energy Supply Chains: Evidence from Mexico

Authors: Sadeghi Darvazeh, Saeed; Farzaneh Mansoori Mooseloo; Andrés Esteban Acero López; Mostafa Hajiaghaei-Keshteli; Leopoldo Eduardo Cárdenas-Barrón; Yasel Costa; Muhammet Deveci;

Securing IoT–Cloud Sensing Systems for Viable Renewable Energy Supply Chains: Evidence from Mexico

Abstract

README.md Dataset Title Dataset for “Securing IoT–Cloud Sensing Systems for Viable Renewable Energy Supply Chains: Evidence from Mexico” General Description This repository contains the expert evaluation dataset used in the study entitled: “Securing IoT–Cloud Sensing Systems for Viable Renewable Energy Supply Chains: Evidence from Mexico” The dataset supports the analysis of implementation constraints affecting secure IoT–cloud sensing systems in renewable energy supply chains in Mexico. The data were collected from experts with backgrounds in renewable energy systems, logistics, supply chain management, cloud computing, IoT systems, cybersecurity, and digital infrastructure. Repository Contents Pairwise comparison data.xlsx The repository contains an Excel workbook including fuzzy pairwise comparison evaluations collected from 15 domain experts. The workbook consists of two sheets: Sheet 1 — Best-to-Others Comparisons This sheet contains the degree of preference of the best (most important) constraint over the remaining constraints using fuzzy pairwise comparisons. Sheet 2 — Others-to-Worst Comparisons This sheet contains the degree of preference of the remaining constraints over the worst (least important) constraint using fuzzy pairwise comparisons. Each column represents the evaluations provided by one expert. Expert Panel Description The dataset was developed using evaluations collected from a multidisciplinary Delphi panel consisting of 15 experts with academic and professional experience in renewable energy systems, supply chain analytics, logistics operations, cloud computing, IoT systems, cybersecurity, digital infrastructure, and industrial engineering. The panel included researchers, logistics managers, cloud systems engineers, cybersecurity specialists, renewable energy project managers, digital transformation professionals, and operations supervisors with experience ranging from 9 to 18 years. The educational backgrounds of the experts ranged from B.Sc. to Ph.D. levels. Linguistic Scale The following linguistic expressions and corresponding triangular fuzzy numbers (TFNs) were used in the fuzzy pairwise comparison process. Linguistic Term Abbreviation TFN Equally Important EI (1, 1, 1) Weakly Important WI (2/3, 1, 3/2) Fairly Important FI (3/2, 2, 5/2) Very Important VI (5/2, 3, 7/2) Absolutely Important AI (7/2, 4, 9/2) Data Usage Notes The dataset is intended exclusively for academic and research purposes. Expert identities and personally identifiable information have been removed. The dataset supports the reproducibility and transparency of the study findings.

Keywords

Internet of Things (IoT); Cloud computing; IoT–cloud sensing systems; Renewable energy supply chains; Supply chain viability; Importance–Performance Map Analysis (IPMA).

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average