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Disasters result in disruptions to the communication infrastructure, typically forming a multitude of fragmented networks. This makes it difficult to exchange even simple while critical messages with large portions of the affected population, including first-responders and government authorities. In this paper, we focus on a content driven data retrieval model and propose an enhanced information-centric network (ICN) approach to provide communication resilience to such disruption-prone, delay-tolerant networks. The message exchange between communities and mules takes into account the limited available resources and the fact that the interest and the data might traverse completely different paths, unlike the assumption in many of the existing ICN solutions. Moreover, we argue for the separation of the the logical faces from the actual physical interfaces taking into account the fact that the data mules behave as mobile routers and are connected to different nodes at different time periods, unlike the assumptions made in existing ICN solutions. Our preliminary evaluations show that our solution is able to outperform other approaches such as “Epidemic” and “SprayAndWait” with respect to the latency, response probability and overall performance while improving the performance (latency and response probability) for high priority messages.

Layered neural networks are feedforward structures that yield robust parallel and distributed pattern recognition. Even though much attention has been paid to pattern retrieval properties in such systems, many aspects of their dynamics are not yet well characterized or understood. In this work we study, at different temperatures, the memory activity and information flows through layered networks in which the elements are the simplest binary odd non-monotonic function. Our results show that, considering a standard Hebbian learning approach, the network information content has its maximum always at the monotonic limit, even though the maximum memory capacity can be found at non-monotonic values for small enough temperatures. Furthermore, we show that such systems exhibit rich macroscopic dynamics, including not only fixed point solutions of its iterative map, but also cyclic and chaotic attractors that also carry information.