What is the best way to normalize a set of datasets
If you know strict bounds on the sensor output, that would be better than normalizing by the min/max of the dataset. Even if the bounds are not necessarily strict, but simply reasonable, that would suffice. For example, if there are no theoretical bounds on a temperature sensor, you might reasonably impose strict bounds given prior knowledge about its environment (e.g. if a temp sensor was placed in NY, you might assume strict bounds as -30C to 50C)
If you were to normalize by the min/max of the training data, what do you expect to happen if the deployed model encounters a value outside this range? If, for example, your training set had min 5 and max 30, how would you normalize an input of -10? It would be much more intuitive and reliable to shift up to a nonnegative domain, (i.e. subtract your strict minima), then scale to a value in [0,1] via the strict range.
Also, depending on your neuronal activation functions, consider centering your data in addition to scaling it.