Know Thy Neighbor: Towards Optimal Mapping of Contacts to Social Graphs for DTN Routing

摘要: Delay Tolerant Networks (DTN) are networks of self-organizing wireless nodes, where end-to-end connectivity is intermittent. In these networks, forwarding decisions generally made using locally collected knowledge about node behavior (e.g., past contacts between nodes) to predict future contact opportunities. The use complex network analysis has been recently suggested perform this prediction task and improve the performance DTN routing. Contacts seen in aggregated a social graph, variety metrics centrality similarity) or algorithms community detection) have proposed assess utility deliver content bring it closer destination. paper, we argue that not so much choice sophistication bears most weight on performance, but rather mapping from mobility process generating graph. We first study two well-known routing - SimBet BubbleRap rely such analysis, show their heavily depends how (contact aggregation) performed. What more, for range synthetic models real traces, improved performances (up factor 4 terms delivery ratio) consistently achieved relatively narrow aggregation levels only, graph closely reflects underlying structure. To end, propose an online algorithm uses concepts unsupervised learning spectral theory infer 'correct' structure; allows each identify adjust optimal operating point, achieves good all scenarios considered.