SemanticCast: Content-Based Data Distribution over Self-Organizing Semantic Overlay Networks

Many applications demand distributing data with different contents efficiently in the network environment with unreliable links and a high node churn. Existing approaches mostly focus on optimizing either efficiency or robustness of data distribution, and fail to ensure both of them simultaneously. In this paper, we propose Semantic Cast - a content-based data distribution approach over self-organizing semantic overlay networks. Semantic Cast maintains a self-organizing semantic overlay based on view exchange (called Crowd). In Crowd, each node seeks neighbors with more similar interests by periodically exchanging its neighbor list (called view) with a chosen neighbor. Through these nodes' self-organizing behavior, various interest communities emerge in the overlay. For data distribution over Crowd, Semantic Cast adopts random walk to route data between interest communities, and adopts flooding to disseminate data inside the interested communities. The experimental results show that compared to existing approaches, Semantic Cast can support efficient content-based data distribution in the unreliable and highly dynamic network environment.