Development and Implementation of an IoT Weather Station for Localized Meteorological Parameter Monitoring and Data Localization

Abstract

Continuous, high-precision air measurement in off-grid and resource constrained environments is a major challenge in the fields of environmental science, precision agriculture, and climate studies. The development of Internet-of-Things (IoT) and renewable energy technologies has introduced significant advances to the creation of autonomous and affordable monitoring of meteorological parameters, in particular to detect weather phenomena in Nigeria. As a counter measure, this paper designed, built, and deployed a solar-powered weather station, which uses ESP32 microcontroller to read and record local weather conditions in the atmosphere. The sensor package included a DHT11 thermistor-humidity sensor, an MQ135 particulate-matter sensor, an IR fog detector, a rainfall-intensity gauge, and an LDR to measure the solar irradiance and darkness. The transmission of real-time data is done through the Blynk dashboard and Google Sheets simultaneously with the presentation of numeric values on a specific LCD display. All sensor modules yielded the same results according to the requirements of the manufacturers in the laboratory and in the field. The I2C LCD was able to cycle through all the parameters screens. The ESP32 had also been very stable in terms of Wi-Fi connectivity and was also very efficient when it came to transmitting structured data to the Blynk server as well as the Google sheets endpoint where timed records of the environmental variables were recorded in real time. The LM7805 voltage regulator provided a constant 5.01V -0.03V at the entire input voltage of the solar charging subsystem. The noise of analog sensors was reduced to reasonable values of measurements by applying a 20-sample moving-average hardware filter. Performance measures were measured in the field under natural conditions and found that the performance metrics showed high correlation in temperature and relative-humidity measurements, effective fog detection, consistent rainfall status measurements and accurate irradiance measurements. The system was fully initialized and was successful in data transfer (98 percent) in both Google Sheets and the Blynk dashboard. This platform has thus been easily implemented in agricultural, meteorological and environmental management applications without requiring infrastructure other than a local access point of Wi-Fi.