Hypercube-Based Multipath Social Feature Routing in Human Contact Networks

Most routing protocols for delay tolerant networks resort to the sufficient state information, including trajectory and contact information, to ensure routing efficiency. However, state information tends to be dynamic and hard to obtain without a global and/or long-term collection process. In this paper, we use the internal social features of each node in the network to perform the routing process. In this way, feature-based routing converts a routing problem in a highly mobile and unstructured contact space to a static and structured feature space. This approach is motivated from several human contact networks, such as the Infocom 2006 trace and MIT reality mining data, where people contact each other more frequently if they have more social features in common. Our approach includes two unique processes: social feature extraction and multipath routing. In social feature extraction, we use entropy to extract the m most informative social features to create a feature space (F-space): (F₁, F₂,..., F_m), where Fi corresponds to a feature. The routing method then becomes a hypercube-based feature matching process, where the routing process is a step-by-step feature difference resolving process. We offer two special multipath routing schemes: node-disjoint-based routing and delegation-based routing. Extensive simulations on both real and synthetic traces are conducted in comparison with several existing approaches, including spray-and-wait routing, spray-and-focus routing, and social-aware routing based on betweenness centrality and similarity. In addition, the effectiveness of multipath routing is evaluated and compared to that of single-path routing.