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Exploring Spatial Patterns in Sensor Data for Humidity, Temperature, and RSSI Measurements

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  • Juan Botero-Valencia

    (Grupo Sistemas de Control y Robótica, Faculty of Engineering, Instituto Tecnológico Metropolitano—ITM, Calle 73 No. 76A-354, Medellin 050034, Colombia)

  • Adrian Martinez-Perez

    (Grupo Materiales Avanzados y Energía, Faculty of Engineering, Instituto Tecnológico Metropolitano—ITM, Calle 73 No. 76A-354, Medellin 050034, Colombia)

  • Ruber Hernández-García

    (Research Center for Advanced Studies of Maule (CIEAM), Universidad Católica del Maule, Avenida San Miguel 3605, Talca 3480094, Chile
    Laboratory of Technological Research in Pattern Recognition (LITRP), Universidad Católica del Maule, Avenida San Miguel 3605, Talca 3480094, Chile)

  • Luis Castano-Londono

    (Faculty of Engineering, Universidad de Antioquia, Calle 70 No. 52-21, Medellin 050010, Colombia)

Abstract

The Internet of Things (IoT) is one of the fastest-growing research areas in recent years and is strongly linked to the development of smart cities, smart homes, and factories. IoT can be defined as connecting devices, sensors, and physical objects that can collect and transmit data across a network, enabling increased automation and better decision-making. In several IoT applications, humidity and temperature are some of the most used variables for adjusting system configurations and understanding their performance because they are related to various physical processes, human comfort, manufacturing processes, and 3D printing, among other things. In addition, one of the biggest problems associated with IoT is the excessive production of data, so it is necessary to develop methodologies to optimize the process of collecting information. This work presents a new dataset comprising almost 55 million values of temperature, relative humidity, and RSSI (Received Signal Strength Indicator) collected in two indoor spaces for longer than 3915 h at 10 s intervals. For each experiment, we captured the information from 13 previously calibrated sensors suspended from the ceiling at the same height and with a known relative position. The proposed dataset aims to contribute a benchmark for evaluating indoor temperature and humidity-controlled systems. The collected data allow the validation and improvement of the acquisition process for IoT applications.

Suggested Citation

  • Juan Botero-Valencia & Adrian Martinez-Perez & Ruber Hernández-García & Luis Castano-Londono, 2023. "Exploring Spatial Patterns in Sensor Data for Humidity, Temperature, and RSSI Measurements," Data, MDPI, vol. 8(5), pages 1-13, April.
    • Handle: RePEc:gam:jdataj:v:8:y:2023:i:5:p:82-:d:1136642
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    References listed on IDEAS

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    1. Ma, Nan & Aviv, Dorit & Guo, Hongshan & Braham, William W., 2021. "Measuring the right factors: A review of variables and models for thermal comfort and indoor air quality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    2. Candelaria de la Merced Díaz-González & Noa Mateos-López & Milagros De la Rosa-Hormiga & Gloria Carballo-Hernández, 2023. "Influence of Hospital Environmental Variables on Thermometric Measurements and Level of Concordance: A Cross-Sectional Descriptive Study," IJERPH, MDPI, vol. 20(5), pages 1-15, March.
    3. Juan Botero-Valencia & Luis Castano-Londono & David Marquez-Viloria, 2022. "Indoor Temperature and Relative Humidity Dataset of Controlled and Uncontrolled Environments," Data, MDPI, vol. 7(6), pages 1-15, June.
    4. Abhishek Gaur & Michael Lacasse, 2022. "Climate Data to Support the Adaptation of Buildings to Climate Change in Canada," Data, MDPI, vol. 7(4), pages 1-22, April.
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