ONSIDE: Socially-aware and Interest-based dissemination in opportunistic networks

TLDR

Data dissemination in opportunistic networks lacks a central entity, so nodes only know their own interests and those of immediate contacts, leading epidemic flooding to cause high overhead and congestion. The authors propose ONSIDE, an algorithm that uses a node’s online social connections, interests, and contact history to reduce congestion and bandwidth while preserving hit rate and delivery latency. ONSIDE directs data dissemination based on friends on social networks (e.g., Facebook, Google+), node interests, and past contact patterns. Evaluation with an opportunistic network emulator and three mobility traces demonstrates that ONSIDE lowers congestion and bandwidth usage without degrading hit rate or latency.

Abstract

Data dissemination in opportunistic networks poses a series of challenges, since there is no central entity aware of all the nodes' subscriptions. Each individual node is only aware of its own interests and those of a node that it is contact with, if any. Thus, dissemination is generally performed using epidemic algorithms that flood the network, but they have the disadvantage that the network overhead and congestion are very high. In this paper, we propose ONSIDE, an algorithm that leverages a node's online social connections (i.e. friends on social networks such as Facebook or Google+), its interests and the history of contacts, in order to decrease congestion and required bandwidth, while not affecting the overall network's hit rate and the delivery latency. We present the results of testing our algorithm using an opportunistic network emulator and three mobility traces taken in different environments.