Reasoning and prediction on opportunistic networks to improve data dissemination

Authors:
Carlos Oberdan Rolim;Claudio F. R. Geyer
Affiliations:
Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil;Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil
Venue:
Proceedings of the ACM International Conference on Computing Frontiers
Year:
2013

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Abstract

Opportunistic networks exploits social behavior to build connectivity opportunities. This paradigm uses pair-wise contact to share and forward content without any prior knowledge about pre-existing infrastructure. In this context, optimize data dissemination among nodes is a paramount. This paper presents early stages of our research with focus on reasoning and predictions issues to improve data dissemination on opportunistic networks. We intend to explore contextual and social aspects with machine learning techniques in the design of a reasoning and prediction engine for this purpose.