Which techniques can I use to select the important variables that I should keep? In a project that I'm working in, there are 5 equal soil humidity sensors displayed in the exact same point but at different depths (20,40,60,80 and 100cm from the surface, respectively). Therefore, each sensor measures the humidity level at these different depths providing info that is later used into a prediction model (including some other variables though). As the differences of conditions between each sensor vary so little, there's little difference and a high linear correlation between each sensor.
How can I select a subset of this sensors (without having a response variable) or demonstrate that among those variables there is redundant information (if it is redundant) and so I can dispense with using some of them, considering that the fewer sensors I use, the cheaper the system gets?
 A: Without examining the relation of the candidate predictors to the response, you can perform a redundancy analysis to determine how well each can be predicted from the others, or from subsets of the others.
The idea is to regress $x_1$ on $x_2, \ldots, x_5$, then $x_2$ on $x_1, x_3, \ldots, x_5$, & so on. A high coefficient of determination for a regression with $x_i$ as response suggests $x_i$ might be considered redundant. You can follow a stepwise procedure of removing the most redundant variable  & repeating the analysis—but be sure to continue checking how well excluded variables are predicted by reduced subsets. It would usually be sensible to allow for curvilinear relations, say by using a spline basis function for predictors. Have a look at the redun function in Frank Harrell's Hmisc package for R.
There aren't any guarantees, of course, that slight variations in the profile of soil humidity by depth aren't highly predictive of whatever it is you want to predict—that's a possibility you need to be confident in rejecting before carrying on with any form of data reduction.
A: Principal Component Analysis:
If you use PCA to solve this you could use each sensor as a dimension in a PCA and then use the Principle Component (sensor/sensors)  to infer the depth/depths at which to place the acceptable number of sensors.

Principal component analysis (PCA) is a statistical procedure that
  uses an orthogonal transformation to convert a set of observations of
  possibly correlated variables into a set of values of linearly
  uncorrelated variables called principal components. The number of
  principal components is less than or equal to the number of original
  variables.
This transformation is defined in such a way that 
the first principal component has the largest possible variance  (that
  is, accounts for as much of the variability in the data as possible),
  and 
each succeeding component  in turn has the highest variance possible
  under the constraint that it is orthogonal to the preceding
  components.

