A methodology to select topology generators for ad hoc mesh network simulations

Abstract

Many academic and industrial research working on Wireless Communications and Networking 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 Ad Hoc Mesh Network 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, which 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 this problem by investigating the presence of bias in the initial placement of nodes in artificial Ad Hoc Mesh Network topologies produced by different TGs. Indeed, any bias introduced by some TGs would affect the representativeness of generated topologies. We propose a methodology to quantify this bias and select what TGs to employ to minimise it, given the number of TGs to use. Our methodology relies on the extraction of a number of features from generated topologies. We carry out experiments on three well-known TGs, namely BRITE, NPART and GT-ITM. We find that selecting NPART (if one TG needs to be chosen) or BRITE and NPART (if two TGs need to be chosen) allows to minimise the bias index. The impact of our research lies in the provisioning of an empirical methodology to select the TGs to use for Ad Hoc Mesh Network simulations, which is expected to be beneficial for researchers involved in experimental evaluations on this type of networks.

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Identifiers

ORCID for Michael O'Sullivan: orcid.org/0000-0002-4216-4287

ORCID for Leonardo Aniello: orcid.org/0000-0003-2886-8445

ORCID for Vladimiro Sassone: orcid.org/0000-0002-6432-1482

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