Using a model for a different dataset
I think you answered yourself by "values of B are not comparable". Learning for predictions is based on a fundamental assumption which is the data for prediction has the same joint distribution as the data for learning. This is the link between those processes.

Now, if you want to handle that in a meaningful way you have to know somehow the source type. In your example the device type. One way would be to introduce the device type as a different column in your data set, so that the model can have the chance to differentiate between source type. Obviously you have to have training data for all the device types. Supposing you have 2 device types, A and B. Your training data should have some columns for the signal and also a factor column with type A and B. Also you have to have enough instances of data with type A and type B.