Information dissemination is an integral part of modern networking environments, such as Wireless Sensor Networks (WSNs). Probabilistic flooding, a common epidemic-based approach, is used as an efficient alternative to traditional blind flooding as it minimizes redundant transmissions and energy consumption. It shares some similarities with the Susceptible-Infected-Recovered (SIR) epidemic model, in the sense that the dissemination process and the epidemic thresholds, which achieve maximum coverage with the minimum required transmissions, have been found to be common in certain cases. In this paper, some of these similarities between probabilistic flooding and the SIR epidemic model are identified, particularly with respect to the epidemic thresholds. Both of these epidemic algorithms are experimentally evaluated on a university campus testbed, where a low-cost WSN, consisting of 25 nodes, is deployed. Both algorithm implementations are shown to be efficient at covering a large portion of the network’s nodes, with probabilistic flooding behaving largely in accordance with the considered epidemic thresholds. On the other hand, the implementation of the SIR epidemic model behaves quite unexpectedly, as the epidemic thresholds underestimate sufficient network coverage, a fact that can be attributed to implementation limitations.
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