Simulating the statistics of the first meetings using dynamic "open environments"

Algorithms and protocols for opportunistic, delay tolerant and wireless ad-hoc networks are designed and validated by simulating the people interactions induced by the nodes mobility. There are cases in which we are interested in simulating just the first interaction between a pair of nodes, for instance to assess the performance of a discovery or epidemic routing protocol. In this cases nodes rapidly extinguish their utility hence it is not convenient to simulate these scenarios using a fixed amount of nodes. Thus we present a novel simulation methodology that introduces the "open environment" where nodes enter, can interact through meeting with other nodes and then exit, keeping the focus on the environment rather than on what happen before and after the nodes stay in the interesting area. The proposed approach uses the statistical distribution extracted from the real traces to reproduce directly the human interaction pattern without going through the traditional random way point approach. Meetings are simulated by a time-varying graph that holds the state of the interactions in the environment, while adapting to the statistics of single node to its history. We show that even in a simple scenario, the epidemic infection, Markov memory-less models have been fairly far from the interaction scenarios that the method reproduces.