Physical sensors could be expensive, prone to fail or to drift. With the Ekkono SDK, you can create a virtual sensor based on related physical sensors. The virtual sensor can fully replace a physical sensor.

The virtual sensor can also provide support on sensor failure or act as a reference when monitoring drift and anomalies. A virtual sensor “measures” signals without the need of a physical sensor. Instead, the sensor readings are generated from a model of the physical sensor. Virtual sensors can replace expensive sensors in the field and add robustness to the system. You can use it as the physical sensor’s digital twin.

Virtual sensor

Edge Opportunities

A fully trained virtual sensor will output almost the exact same values as the physical sensor. Because of this, an expensive sensor that has been virtualised can be removed completely. Another area of usage is to keep the physical sensor and compare its readings to the output of the virtual one. If the responses of the physical and the virtual sensor start to deviate, this implies an error. When this happens, you can send a technician to the site to investigate the machine. Until the technician arrives, the virtual sensor can override the faulty sensor’s output.

The Ekkono SDK has incremental learning capabilities. This means that virtual sensors can be trained in real-time out on the field. In other words, we can train the models with no need for additional historical data. The incremental learning process collects data over time, and learns to replicate the physical sensor.

Virtual Sensors


Physical sensors may be expensive, challenging to install and require frequent calibrations. In addition, physical sensors can be subject to sensor drift and failure.


Create a virtual sensor based on related physical sensors. Use a reference sensor to train the virtual sensor. You can train the virtual sensor in a lab environment or incrementally on the field.


Replace the physical sensor with a virtual sensor permanently. Use the readings from the virtual sensor when the physical sensor fails. Compare the physical sensor with the virtual sensor to monitor drift and anomalies.

See more of our Use Case Archetypes.