再生可能エネルギー供給チェーンにおけるIoT–クラウドセンシングシステムのセキュリティ確保

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

🔬研究者:FLBWMによる専門家判断の整合性評価手法と、再エネ供給網のデジタルセキュリティ制約の重み付けデータを再利用できる。

🏢実務担当者:再エネ調達やIoT導入時のセキュリティ制約を多基準で整理する際の評価枠組みとして参考になる。

🏛政策担当者:再エネインフラのデジタル化に伴うサイバーセキュリティ政策の優先順位づけに示唆を与える。

Dataset Title Supporting Data for “Securing IoT–Cloud Sensing Systems for Viable Renewable Energy Supply Chains” General Description This repository contains expert comparison data and associated consistency results supporting the study “Securing IoT–Cloud Sensing Systems for Viable Renewable Energy Supply Chains,” accepted for publication in Energy for Sustainable Development . The study examines implementation constraints affecting secure IoT–cloud sensing systems in renewable energy supply chains in Mexico. The repository documents the fuzzy comparisons used for constraint weighting and the corresponding expert-level consistency assessments under the fuzzy linear best–worst method (FLBWM). Repository Contents 1. Pairwise comparison data.xlsx This workbook contains fuzzy comparison evaluations provided by 15 domain experts. It includes two sheets: Best-to-Others Comparisons: preferences of the selected best (most important) criterion over the other criteria in each comparison set. Others-to-Worst Comparisons: preferences of the other criteria over the selected worst (least important) criterion in each comparison set. Each expert’s evaluations are presented in a separate column. 2. FLBWM_consistency_results.xlsx This supplementary workbook contains 90 records covering 15 anonymized experts, identified as S01–S15. Each expert has six records: one for the main category comparison and one for each of the five within-category comparison sets. The variables are defined below: Variable Description Expert Anonymized expert identifier, from S01 to S15. Set Comparison set: Main denotes the comparison of categories C1–C5; C1–C5 denote comparisons of the criteria within each respective category. Best Code of the criterion selected as most important within the comparison set. Worst Code of the criterion selected as least important within the comparison set. Best–Worst TFN Triangular fuzzy number expressing the preference of the selected best criterion over the selected worst criterion. ξ* Optimal deviation value obtained from the FLBWM optimization model for the corresponding expert and comparison set. CI Consistency index corresponding to the best–worst fuzzy preference. CR Consistency ratio, calculated as ξ*/CI. Time (s) Recorded computational solution time, in seconds, for the corresponding optimization run. Criterion codes follow the notation used in the accompanying article. The within-category code prefixes are SV for C1, EV for C2, OV for C3, OP for C4, and IV for C5. Numerical results are reported at the precision displayed in the workbook. Consequently, recalculating CR from the displayed, rounded ξ* and CI values may produce small differences from the reported CR. Values displayed as 0.0000 should be interpreted at the reported numerical precision. Expert Panel The comparison data were provided by 15 experts with academic and professional backgrounds relevant to renewable energy systems, supply chain management, logistics, cloud computing, IoT systems, cybersecurity, and digital infrastructure. Expert identifiers are anonymized, and personally identifiable information is not included in the shared files. Linguistic Scale The following linguistic terms and triangular fuzzy numbers were used in the fuzzy comparison process: Linguistic term Abbreviation Triangular fuzzy number 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) Version Update This version replaces the previously deposited dataset with an updated Excel file containing expert-level FLBWM best/worst selections and consistency results. The record title and description have been revised to reflect the current contents. Data Usage Notes The files support examination of the expert judgments and consistency assessments used in the FLBWM weighting analysis. The consistency-results workbook should be interpreted alongside the original comparison workbook and the methodological details in the accompanying article. Computational solution times depend on the hardware, software, solver, and execution conditions and should not be treated as general performance benchmarks. Reuse is governed by the license selected for this Zenodo record. Please cite the dataset and the accompanying article when using these materials.

• Zenodo https://zenodo.org/records/22869744first seen 2026-10-05 04:34:17