Modeling Epidemic Routing: Capturing Frequently Visited Locations While Preserving Scalability

This paper investigates the performance of epidemic routing in mobile social networks considering several communities which are frequently visited by nodes. To this end, a monolithic Stochastic Reward Net (SRN) is proposed to evaluate the delivery delay and the average number of transmissions under epidemic routing by considering skewed location visiting preferences. This model is not scalable enough, in terms of the number of nodes and frequently visited locations. In order to achieve higher scalability, the folding technique is applied to the monolithic model, and an approximate folded SRN is proposed to evaluate performance of epidemic routing. Discrete-event simulation is used to validate the proposed models. Both SRN models for predicting the performance of epidemic routing exhibit high accuracy. We also propose an Ordinary Differential Equation (ODE) model for epidemic routing and compare it with the folded model. The obtained results show that the folded model is more accurate than the ODE model. Moreover, it is proved that the number of transmissions by the time of delivery follows a uniform distribution, for a general class of networks, where positions of nodes are always independent and identically distributed.