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

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:The fuzzy pairwise comparison methodology and expert dataset provide a replicable approach for studying constraints in renewable energy IoT systems.

🏢実務担当者:Supply chain and renewable energy managers can use the identified constraints to prioritize cybersecurity measures in IoT deployments.

🏛政策担当者:Policymakers should consider cybersecurity standards and infrastructure resilience as key enablers for renewable energy supply chains.

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.

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