A Comparative Analysis of Flooding Methods on Random and Real Network Topologies

Abstract

The Internet of Things (IoT) is revolutionizing industries by connecting everyday objects, known as smart devices, via the Internet. These devices, embedded with sensors and communication technologies, gather and share data. For the guaranteed gathering of information, the devices share global knowledge with each other, by using dissemination mechanisms in order to broadcast information. This study evaluates four flooding methods for broadcasting information across network nodes, namely: (i) blind flooding; (ii) probabilistic flooding; (iii) m-probabilistic flooding; and (iv) scoped probabilistic flooding, the latter to be introduced here. The evaluation considers random networks that are based on the Burr Type XII distribution and seven real networks. The evaluated flooding methods are studied on three different metrics: (i) coverage achieved; (ii) number of messages exchanged; and (iii) a metric that is based on binomial approximation. The latter is introduced to provide deeper insights into the particulars of the under-evaluation flooding methods. The results show that, under certain conditions, m-probabilistic flooding outperforms probabilistic flooding in terms of coverage, while requiring significantly fewer messages. Additionally, the study revealed that the scoped probabilistic flooding achieves coverage comparable to that of the probabilistic flooding while reducing the number of exchanged messages.