On Stochastic Confidence of Information Spread in Opportunistic Networks
Yoora Kim, Kyunghan Lee, Ness B. Shroff · IEEE Transactions on Mobile Computing · 2015
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.