Promoting tolerance for delay tolerant network research
ACM SIGCOMM Computer Communication Review
CoNEXT '08 Proceedings of the 2008 ACM CoNEXT Conference
Privacy-enhanced opportunistic networks
MobiOpp '10 Proceedings of the Second International Workshop on Mobile Opportunistic Networking
Planet-scale human mobility measurement
Proceedings of the 2nd ACM International Workshop on Hot Topics in Planet-scale Measurement
Towards a framework for weaving social networks principles into web services discovery
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
Privacy-enhanced social-network routing
Computer Communications
Social aspects to support opportunistic networks in an academic environment
ADHOC-NOW'12 Proceedings of the 11th international conference on Ad-hoc, Mobile, and Wireless Networks
Predicting encounters in opportunistic networks
Proceedings of the 1st ACM workshop on High performance mobile opportunistic systems
Social-awareness in opportunistic networking
International Journal of Intelligent Systems Technologies and Applications
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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.