Title: Modelling epidemic routing with heterogeneous infection rate

Authors: Peiyan Yuan; Shangwang Liu; En Zhang

Addresses: School of Computer and Information Engineering, Henan Normal University, Xinxiang, China; Engineering Laboratory of Intellectual Business and Internet of Things Technologies, Henan Province, China ' School of Computer and Information Engineering, Henan Normal University, Xinxiang, China; Engineering Laboratory of Intellectual Business and Internet of Things Technologies, Henan Province, China ' School of Computer and Information Engineering, Henan Normal University, Xinxiang, China; Engineering Laboratory of Intellectual Business and Internet of Things Technologies, Henan Province, China

Abstract: The epidemic routing has been integrated into many applications, ranging from the worm propagation of online social networks to the message diffusion in offline physical systems. Modelling epidemic routing provides a baseline to evaluate system performance; it also becomes very desirable for engineers to have theoretical guidance before they deploy the real system. Early works analyse the dynamics of epidemic routing with the average contact rate, i.e., each node will encounter the same number of other nodes in a time slot. They neglect the status of encountered nodes (i.e., infected or susceptible), resulting in the defectiveness of existing models. In this paper, we observe that the infectivity of nodes has heterogeneity rather than homogeneity, two nodes with the same contact rate may behave different infectivities. Motivated by this observation, we first use infection rate to reflect the infectivity of infected nodes. We then model the epidemic routing with the average infection rate, instead of the contact rate. 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 state-of-the-art works, which provides an upper bound on the number of infected nodes.

Keywords: epidemic routing; scaling law; contact rate; infection rate.

DOI: 10.1504/IJHPCN.2017.084250

International Journal of High Performance Computing and Networking, 2017 Vol.10 No.3, pp.218 - 225

Available online: 18 May 2017 *

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