In post-disaster scenarios, e.g., after earthquakes or floods, the traditional communication infrastructure may be unavailable or seriously disrupted and overloaded; therefore, rapidly deployable network solutions are needed to restore connectivity and provide assistance to users and first responders in the incident area. This work surveys the solutions proposed to address the deployment of a network without any a priori knowledge about the communication environment for critical communications. The design of such a network should also allow for quick, flexible, scalable, and resilient deployment with minimal human intervention.

We present how the mobility of routers impacts the performance of a wireless substitution network. To that end, we simulate a scenario where a wireless router moves between three static nodes, a source and two destinations of UDP traffic. Specifically, our goal is to deploy or redeploy the mobile relays so that application-level requirements, such as data delivery or latency, are met. Our proposal for a mobile relay achieves these goals by using an adaptive approach to self-adjust their position based on local information. We obtain results on the performance of end-to-end delay, jitter, loss percentage, and throughput under such mobility pattern for the mobile relay. We show how the proposed solution is able to adapt to topology changes and to the evolution of the network characteristics through the usage of limited neighborhood knowledge.

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Dynamic Factor Models, which assume the existence of a small number of unobserved latent factors that capture the comovements in a system of variables, are the main big data tool used by empirical macroeconomists during the last 30 years. One important tool to extract the factors is based on Kalman lter and smoothing procedures that can cope with missing data, mixed frequency data, time-varying parameters, non-linearities, non-stationarity and many other characteristics often observed in real systems of economic variables. This paper surveys the literature on latent common factors extracted using Kalman lter and smoothing procedures in the context of Dynamic Factor Models. Signal extraction and parameter estimation issues are separately analyzed. Identication issues are also tackled in both stationary and non-stationary models. Finally, empirical applications are surveyed in both cases.