Indoor Temperature and Relative Humidity Dataset of Controlled and Uncontrolled Environments
The large volume of data generated with the increasing development of Internet of Things applications has encouraged the development of a large number of works related to data management, wireless communication technologies, the deployment of sensor networks with limited resources, and energy consum...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Data |
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| Online Access: | https://www.mdpi.com/2306-5729/7/6/81 |
| _version_ | 1797488379579858944 |
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| author | Juan Botero-Valencia Luis Castano-Londono David Marquez-Viloria |
| author_facet | Juan Botero-Valencia Luis Castano-Londono David Marquez-Viloria |
| author_sort | Juan Botero-Valencia |
| collection | DOAJ |
| description | The large volume of data generated with the increasing development of Internet of Things applications has encouraged the development of a large number of works related to data management, wireless communication technologies, the deployment of sensor networks with limited resources, and energy consumption. Different types of new or well-known algorithms have been used for the processing and analysis of data acquired through sensor networks, algorithms for compression, filtering, calibration, analysis, or variables being common. In some cases, databases available on the network, public government databases, data generated from sensor networks deployed by the authors themselves, or values generated by simulation are used. In the case that the work approach is more related to the algorithm than to the characteristics of the sensor networks, these data source options may have some limitations such as the availability of databases, the time required for data acquisition, the need for the deployment of a real sensors network, and the reliability or characteristics of acquired data. The dataset in this article contains 4,164,267 values of timestamp, indoor temperature, and relative humidity acquired in the months of October and November 2019, with twelve temperature and humidity sensors Xiaomi Mijia at the laboratory of Control Systems and Robotics, and the De La Salle Museum of Natural Sciences, both of the Instituto Tecnológico Metropolitano, Medellín—Colombia. The devices were calibrated in a Metrology Laboratory accredited by the National Accreditation Body of Colombia (Organismo Nacional de Acreditación de Colombia—ONAC). The dataset is available in Mendeley Data repository. |
| first_indexed | 2024-03-10T00:02:17Z |
| format | Article |
| id | doaj.art-635a655a2432402989145aea219353e1 |
| institution | Directory Open Access Journal |
| issn | 2306-5729 |
| language | English |
| last_indexed | 2024-03-10T00:02:17Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj.art-635a655a2432402989145aea219353e12023-11-23T16:14:52ZengMDPI AGData2306-57292022-06-01768110.3390/data7060081Indoor Temperature and Relative Humidity Dataset of Controlled and Uncontrolled EnvironmentsJuan Botero-Valencia0Luis Castano-Londono1David Marquez-Viloria2Faculty of Engineering, Instituto Tecnológico Metropolitano—ITM, Medellín 050034, ColombiaFaculty of Engineering, Instituto Tecnológico Metropolitano—ITM, Medellín 050034, ColombiaFaculty of Engineering, Instituto Tecnológico Metropolitano—ITM, Medellín 050034, ColombiaThe large volume of data generated with the increasing development of Internet of Things applications has encouraged the development of a large number of works related to data management, wireless communication technologies, the deployment of sensor networks with limited resources, and energy consumption. Different types of new or well-known algorithms have been used for the processing and analysis of data acquired through sensor networks, algorithms for compression, filtering, calibration, analysis, or variables being common. In some cases, databases available on the network, public government databases, data generated from sensor networks deployed by the authors themselves, or values generated by simulation are used. In the case that the work approach is more related to the algorithm than to the characteristics of the sensor networks, these data source options may have some limitations such as the availability of databases, the time required for data acquisition, the need for the deployment of a real sensors network, and the reliability or characteristics of acquired data. The dataset in this article contains 4,164,267 values of timestamp, indoor temperature, and relative humidity acquired in the months of October and November 2019, with twelve temperature and humidity sensors Xiaomi Mijia at the laboratory of Control Systems and Robotics, and the De La Salle Museum of Natural Sciences, both of the Instituto Tecnológico Metropolitano, Medellín—Colombia. The devices were calibrated in a Metrology Laboratory accredited by the National Accreditation Body of Colombia (Organismo Nacional de Acreditación de Colombia—ONAC). The dataset is available in Mendeley Data repository.https://www.mdpi.com/2306-5729/7/6/81temperaturerelative humidityInternet of Things (IoT)indoor climate |
| spellingShingle | Juan Botero-Valencia Luis Castano-Londono David Marquez-Viloria Indoor Temperature and Relative Humidity Dataset of Controlled and Uncontrolled Environments Data temperature relative humidity Internet of Things (IoT) indoor climate |
| title | Indoor Temperature and Relative Humidity Dataset of Controlled and Uncontrolled Environments |
| title_full | Indoor Temperature and Relative Humidity Dataset of Controlled and Uncontrolled Environments |
| title_fullStr | Indoor Temperature and Relative Humidity Dataset of Controlled and Uncontrolled Environments |
| title_full_unstemmed | Indoor Temperature and Relative Humidity Dataset of Controlled and Uncontrolled Environments |
| title_short | Indoor Temperature and Relative Humidity Dataset of Controlled and Uncontrolled Environments |
| title_sort | indoor temperature and relative humidity dataset of controlled and uncontrolled environments |
| topic | temperature relative humidity Internet of Things (IoT) indoor climate |
| url | https://www.mdpi.com/2306-5729/7/6/81 |
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