Scalable Performance Analysis of Epidemic Routing Considering Skewed Location Visiting Preferences

This paper investigates the performance of epidemic routing, in mobile social networks (MSNs), which makes use of the store-carry-forward paradigm for communication. Real-life mobility traces show that people have skewed location visiting preferences, with some places visited frequently and some others infrequently. In order to model epidemic routing in MSNs, we first analyze the time taken for a node to meet the first node belonging to a set of nodes restricted to move in a specific subarea. Afterwards, 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 monolithic 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 SRN and an approximate folded SRN is proposed to evaluate the performance of epidemic routing. Discrete-event simulation is applied to cross-validate the proposed models. Results indicate that the monolithic model has higher accuracy in predicting the performance of epidemic routing. The approximate folded model also achieves a good accuracy and can be solved for a network with a large number of nodes/frequently visited locations. This model is more accurate than the ordinary differential equation approach.