<![CDATA[The DHT22 digital temperature sensor is widely used in environmental monitoring, building automation, and Internet of Things (IoT) applications due to its low cost and ease of integration. However, its measurement accuracy and precision are limited by manufacturing variability, environmental conditions, and component ageing, which can lead to systematic errors. Therefore, calibration accompanied by measurement uncertainty analysis is required to ensure reliable temperature data. In this study, a DHT22 temperature sensor was calibrated using a calibrated digital thermometer as a reference instrument through a direct comparison method at several temperature points within the sensor’s operating range. Linear regression was applied to derive a correction equation, while measurement error and Type A and Type B uncertainties were evaluated to determine the combined measurement uncertainty. The results show that, before calibration, the DHT22 sensor exhibited a temperature-dependent bias, with errors exceeding 1 °C at medium to high temperatures. The application of the regression-based correction significantly reduced measurement errors and improved agreement with the reference values, as indicated by a high coefficient of determination (R² = 0.998). The combined measurement uncertainty was found to lie within a moderate accuracy range and to increase with temperature, with dominant contributions from measurement repeatability and reference instrument uncertainty. Consequently, the calibrated DHT22 sensor can be more reliably employed in environmental temperature measurement applications requiring moderate accuracy.]]>
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