EMO: A statistical encounter-based mobility model for simulating delay tolerant networks

Abstract

We propose EMO, a model to evaluate delay tolerant networks (DTN) and opportunistic systems, which focuses on simulating encounter events between mobile radios, rather than node locations as done in existing models and simulators. Our approach introduces a more accurate simulation of DTNs on the main system timescale (the encounter timescale), while trading off some accuracy at the bit-level, through an abstraction of radio propagation simulation. To design EMO, we extract and characterize the necessary parameters from experimental data and propose a method to generate synthetic node encounter traces based on this characterization. The output of the model is validated using hold-out cross-validation method. Our validation results indicate that EMO is able to maintain the statistical properties of experimental data over a wide range of time (simulation duration) and space (number of nodes) scales, with mean square errors of less than 3% for the main system parameters.