Recurring contact opportunities within groups of devices

Abstract

The capability to anticipate a contact with another device can greatly improve the performance and user satisfaction not only of mobile social network applications but of any other relying on some form of data harvesting or hoarding. One of the most promising approaches for contact prediction is to extrapolate from past experiences. This paper investigates the recurring contact patterns observed between groups of devices using an 8-year dataset of wireless access logs produced by more than 70000 devices. This effort permitted to model the probabilities of occurrence of a contact at a predefined date between groups of devices using a power law distribution that varies according to neighbourhood size and recurrence period. In the general case, the model can be used by applications that need to disseminate large datasets by groups of devices. As an example, the paper presents and evaluates an algorithm that provides daily contact predictions, based on the history of past pairwise contacts and their duration.

Recurring Contacts between Groups of Devices: Analysis and Application

“…A survey of most mobility models as well as of the modelling process using real traces can be found in [14]. To the best of our knowledge, research on very large data sets, spanning several years and covering thousands of users is limited to the work described in this paper and whose initial results were published in [10]. This paper extends these results by demonstrating their applicability in a practical scenario using a reputation system as the supporting application.…”

GRM: Group Regularity Mobility Model

“…Therefore, such models are not representative of the statistical regularity of human interactions, i.e., groups of people that meet regularly. Recent studies [13,14,15,16] have shown that the regularity of group meetings, present in real-world traces, play an important role for content forwarding in mobile opportunistic networks.…”