Repository for publishing the tools and data generated for analyzing trust in sensors from IoT solutions.
The folder is structured in the following way:
.
├── Data Understanding Scripts # R Scripts that implement the proposed data understanding and trust analysis approach (incl. data results)
│ └──Generated Results
├── Data # Some of the input datasets used for the research done
│ ├──Arduino
│ ├──FaultInjectionDatasets
│ └──PhysicsToolboxSuite
├── LICENSE
└── README.md
This work has been done by:
Francisco Javier Nieto (Mail | fjaviernieto | @BrkfstResearch)
The code included in this repository is published under the Apache 2.0 License. On the other hand, the datasets are licensed under the Creative Commons Attribution-ShareAlike 4.0 International License, the Creative Commons Attribution 4.0 International license (CC BY 4.0) and the Creative Commons Attribution NonCommercial-NoDerivs CC BY-NC-ND License - see the LICENSE file in each folder for additional details.
If the provided scripts and data are useful for you and you want to reference this work, please, do so by referencing to the following paper:
Nieto FJ, Aguilera U, López-de-Ipiña D. Analyzing Particularities of Sensor Datasets for Supporting Data Understanding and Preparation. Sensors. 2021; 21(18):6063. https://doi.org/10.3390/s21186063
I want to thank to all those colleagues and entities that provided me with the datasets that I needed to do the analysis and the evaluation of the solutions proposed. This includes Ports of Spain, Kunak, Meteoblue and the people from SZE.