  Many academic and industrial research works on WANETs rely on simulations, at
least in the first stages, to obtain preliminary results to be subsequently
validated in real settings. Topology generators (TG) are commonly used to
generate the initial placement of nodes in artificial WANET topologies, where
those simulations take place. The significance of these experiments heavily
depends on the representativeness of artificial topologies. Indeed, if they
were not drawn fairly, obtained results would apply only to a subset of
possible configurations, hence they would lack of the appropriate generality
required to port them to the real world. Although using many TGs could mitigate
this issue by generating topologies in several different ways, that would
entail a significant additional effort. Hence, the problem arises of what TGs
to choose, among a number of available generators, to maximise the
representativeness of generated topologies and reduce the number of TGs to use.
  In this paper, we address that problem by investigating the presence of bias
in the initial placement of nodes in artificial WANET topologies produced by
different TGs. We propose a methodology to assess such bias and introduce two
metrics to quantify the diversity of the topologies generated by a TG with
respect to all the available TGs, which can be used to select what TGs to use.
We carry out experiments on three well-known TGs, namely BRITE, NPART and
GT-ITM. Obtained results show that using the artificial networks produced by a
single TG can introduce bias.
