Accuracy of NTC thermistor measurements using the sensor-to-microcontroller direct interface

In recent years, the adoption of wireless sensor systems has significantly grown in many different kind of applications, like environmental monitoring, chemical analysis, food safety, health monitoring and quality analysis in industrial environments, and contributed to the development of the Internet of Things (IoT) paradigm.
Typically, sensors are interfaced with computing devices (e.g., microcontrollers, FPGAs, etc.) that acquire sensor data using an analog-to-digital converter (ADC). Since individual sensor nodes are usually powered by batteries, power consumption is a critical aspect that significantly impact the sensor node lifetime. In order to reduce power consumption, sensors can be interfaced with computing devices by using the sensor-to-microcontroller direct interface (SMDI), without the need to use an ADC which requires higher power. The SMDI exploits Schmitt trigger circuits that are typically integrated in the general purpose input output (GPIO) interface of a microcontroller. SMDI can be applied to many different kind of sensors, such as resistive and capacitive sensors, as well as any other sensors producing analog output voltage, and can allow sensor measurements with lower cost and power consumption than the traditional ADC based data acquisition.
In this study, we investigate the application of SMDI technique when it is employed to acquire data from a non-linear negative temperature coefficient (NTC) thermistor. We evaluated the accuracy of temperature measurements by means of electrical level simulations, considering real operating conditions and two well know models from literature (Steinhart-Hart model and polynomial model) to estimate the accuracy of temperature measurements. The results have shown that the temperature estimation using data obtained by SMDI measurements provides good accuracy. In particular, the Steinhart-Hart model provides more accurate results (average error 0.078 °C) than the polynomial model, that features an average error of 0.28 °C.