Interest-awareness in data dissemination for opportunistic networks

Generally, data dissemination in opportunistic network uses flooding to ensure that content is spread to interested subscribers. However, this might lead to congestion and high overhead, so alternative solutions are required. In this article, we propose leveraging context information such as node interests, social connections and predictions based on contact history, in order to decrease congestion without affecting the network’s hit rate and delivery latency. Thus, we first propose a basic interest-based dissemination algorithm, to show that nodes tend to group together based on interests. Then, we present ONSIDE, an algorithm that also uses other types of context information to select the nodes that act as forwarders. Finally, we propose five heuristics for sorting the messages in a node’s memory, and show how each of them affects hit rate, delivery latency and congestion.