A Methodology to Select Topology Generators for WANET Simulations (Extended Version)

TL;DR

This paper proposes a methodology to evaluate and select topology generators for WANET simulations to ensure representativeness and reduce bias in artificial network topologies.

Contribution

It introduces a bias assessment methodology and two diversity metrics to help choose the most suitable topology generators for WANET simulations.

Findings

Using a single TG can introduce bias in network topologies.

The proposed metrics effectively quantify topology diversity.

Different TGs produce significantly different network structures.

Abstract

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.