  We present new families of goodness-of-fit tests of uniformity on a
full-dimensional set $W\subset\R^d$ based on statistics related to edge lengths
of random geometric graphs. Asymptotic normality of these statistics is proven
under the null hypothesis as well as under fixed alternatives. The derived
tests are consistent and their behaviour for some contiguous alternatives can
be controlled. A simulation study suggests that the procedures can compete with
or are better than established goodness-of-fit tests. We show with a real data
example that the new tests can detect non-uniformity of a small sample data
set, where most of the competitors fail.
