English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

A variety of mathematical tools have been developed for predicting thespreading patterns in a number of varied environments including infectiousdiseases, computer viruses, and urgent messages broadcast to mobile agent(e.g., humans, vehicles, and mobile devices). These tools have mainly focusedon estimating the average time for the spread to reach a fraction (e.g.,$\alpha$) of the agents, i.e., the so-called average completion time$E(T_{\alpha})$. We claim that providing probabilistic guarantee on the timefor the spread $T_{\alpha}$ rather than only its average gives a much betterunderstanding of the spread, and hence could be used to design improved methodsto prevent epidemics or devise accelerated methods for distributing data. Todemonstrate the benefits, we introduce a new metric $G_{\alpha, \beta}$ thatdenotes the time required to guarantee $\alpha$ completion with probability$\beta$, and develop a new framework to characterize the distribution of$T_\alpha$ for various spread parameters such as number of seeds, level ofcontact rates, and heterogeneity in contact rates. We apply our technique to anexperimental mobility trace of taxies in Shanghai and show that our frameworkenables us to allocate resources (i.e., to control spread parameters) foracceleration of spread in a far more efficient way than the state-of-the-art.

Providing Probabilistic Guarantees on the Time of Information Spread in Opportunistic Networks