  Power efficiency is a crucial consideration for embedded systems design,
particularly in the field of edge computing and IoT devices. This study aims to
calibrate the power measurements obtained from the built-in sensors of NVIDIA
Jetson devices, facilitating the collection of reliable and precise power
consumption data in real-time. To achieve this goal, accurate power readings
are obtained using external hardware, and a regression model is proposed to map
the sensor measurements to the true power values. Our results provide insights
into the accuracy and reliability of the built-in power sensors for various
Jetson edge boards and highlight the importance of calibrating their internal
power readings. In detail, internal sensors underestimate the actual power by
up to 50% in most cases, but this calibration reduces the error to within 3%.
By making the internal sensor data usable for precise online assessment of
power and energy figures, the regression models presented in this paper have
practical applications, for both practitioners and researchers, in accurately
designing energy-efficient and autonomous edge services.
