Exploiting Self-Reported Social Networks for Routing in Ubiquitous Computing Environments

Mobile, delay-tolerant, ad hoc and pocket-switched networks may form an important part of future ubiquitous computing environments. Understanding how to efficiently and effectively route information through such networks is an important research challenge, and much recent work has looked at detecting communities and cliques to determine forwarding paths. Such detected communities, however, may miss important aspects. For instance, a user may have strong social ties to another user that they seldom encounter; a detected social network may omit this tie and so produce sub-optimal forwarding paths. Moreover, the delay in detecting communities may slow the bootstrapping of a new delay-tolerant network. This paper explores the use of self-reported social networks for routing in mobile networks in comparison with detected social networks discovered through encounters. Using encounter records from a group of participants carrying sensor motes, we generate detected social networks from these records. We use these networks for routing, and compare these to the social networks which the users have self-reported on a popular social networking website. Using techniques from social network analysis, we find that the two social networks are different. These differences, however, do not lead to a significant impact on delivery ratio, while the self-reported social network leads to a significantly lower cost.