Impact of infection rate on scaling law of epidemic routing

Performance modeling of epidemic routing is challenging because of the unguaranteed end-to-end connectivity and lack of global information in delay-tolerant scenarios. Existing works analyze the scaling law of epidemic protocol based on the assumption that each node has the same infectivity. Whereas, the most recent work indicates that the distribution of infected nodes has spatial-temporal correlation rather than homogeneity, i.e., nodes in different locations have different infectivities, which leads to defectiveness of the existing solutions. In this paper, by exploring the reason behind this difference, we try to relax the assumption and rebuild the model for epidemic routing. We first introduce the concept of infection rate to reflect the infectivity of infected nodes. Second, we propose an effective method to compute the average infection rate and use it to derive a generic scaling law. Third, we give an explicit expression for the generic scaling law, which provides us with upper bound. We finally compare our model with the existing works through theoretical analysis and simulations. The results show that our model has a closer match than those of the existing works and gets some insights into the spatial distribution of infection process.