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

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Offloading traffic through opportunistic communications has been recently proposed as a way to relieve the current overload of cellular networks. Opportunistic communication can occur when mobile device users are (temporarily) in each other's proximity, such that the devices can establish a local peer-to-peer connection (e.g., via WLAN or Bluetooth). Since opportunistic communication is based on the spontaneous mobility of the participants, it is inherently unreliable. This poses a serious challenge to the design of any cellular offloading solutions, that must meet the applications' requirements. In this paper, we address this challenge from an optimization analysis perspective, in contrast to the existing heuristic solutions. We first model the dissemination of content (injected through the cellular interface) in an opportunistic network with heterogeneous node mobility. Then, based on this model, we derive the optimal content injection strategy, which minimizes the load of the cellular network while meeting the applications' constraints. Finally, we propose an adaptive algorithm based on control theory that implements this optimal strategy without requiring any data on the mobility patterns or the mobile nodes' contact rates. The proposed approach is extensively evaluated with both a heterogeneous mobility model as well as real-world contact traces, showing that it substantially outperforms previous approaches proposed in the literature.

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Epidemic spreading is one of the most popular bio-inspired principles, which has made its way into computer networking. This principle naturally applies to Opportunistic or Delay Tolerant Networks (DTNs), where nodes probabilistically meet their neighbors thanks to mobility. Epidemic-based algorithms are often the only choice for DTN problems such as broadcast and unicast routing, distributed estimation etc. Existing analyses of epidemic spreading in various contexts only consider specific graph geometries (complete, random, regular etc) and/or homogeneous exponential node meeting rates. In addition, in wired networks, synchronous communication is usually assumed.In this paper, we relax these assumptions and provide a detailed analysis of epidemic spreading in DTNs with heterogeneous node meeting rates. We observe the special properties of a Markov model, describing the epidemic process and use them to derive bounds for the delay (expectation and distribution). We apply our analysis to epidemic-based DTN algorithms for routing and distributed estimation and validate the bounds against simulation results, using various real and synthetic mobility scenarios. Finally, we empirically show that the delay distribution is relatively concentrated, and that, depending on graph properties (communities, scale-freeness), the delay scales very well with network size.