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

Juan Botero-Valencia, Adrian Martinez-Perez, Ruber Hernández-García () and Luis Castano-Londono
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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
Luis Castano-Londono: Faculty of Engineering, Universidad de Antioquia, Calle 70 No. 52-21, Medellin 050010, Colombia

Data, 2023, vol. 8, issue 5, 1-13

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.

Keywords: temperature; relative humidity; RSSI; Internet of Things (IoT); indoor climate (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
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