On Stochastic Confidence of Information Spread in Opportunistic Networks

Predicting spreading patterns of information or virus has been a popular research topic for which various mathematical tools have been developed. These tools have mainly focused on estimating the average time of spread to a fraction (e.g., alpha) of the agents, i.e., so-called average alpha-completion time E(T-alpha). We claim that understanding stochastic confidence on the time T-alpha rather than only its average gives more comprehensive knowledge on the spread behavior and wider engineering choices. Obviously, the knowledge also enables us to effectively accelerate or decelerate a spread. To demonstrate the benefits of understanding the distribution of spread time, we introduce a new metric G(alpha,beta) that denotes the time required to guarantee alpha completion (i.e., penetration) with probability beta. Also, we develop a new framework characterizing G(alpha,beta) for various spread parameters such as number of seeders, contact rates between agents, and heterogeneity in contact rates. We apply our technique to a large-scale experimental vehicular trace and show that it is possible to allocate resources for acceleration of spread in a far more elaborated way compared to conventional average-based mathematical tools.