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Wireless networks are rapidly becoming highly complex systems with large numbers of heterogeneous devices interacting with each other, often in a harsh environment. In the absence of central control, network entities need to self-organize to reach an efficient operating state, while operating in a distributed fashion. Depending on whether the operating criteria are individual or global, nodes interact in an autonomous or coordinated way. Despite recent progress in autonomous networks, the fundamental understanding of the operational behaviour of large-scale networks is still lacking.

CROWN addresses these emergent network properties, by introducing new tools and concepts from other disciplines. We analyze how imperfect network state information can be harvested and distributed efficiently through the network using machine learning techniques. We design flexible methodologies to shape the competition between autonomous nodes for resources, with aim to maintain robust social optimality. Both cooperating and non-cooperating game-theoretic models are used. We also consider networks with nodes coordinating to achieve a joint task, e.g. global optimization. Using algorithms inspired from statistical physics, we address two representative paradigms in the context of wireless ad hoc networks, namely connectivity optimization and the localization of a network of primary sources from a sensor network.

Finally, we explore delay tolerant networks as a case study of an emerging class of networks that, while sharing most of characteristics of traditional autonomous or coordinated networks, they present unique challenges due to the intermittency and constant fluctuations of the connectivity. We study tradeoffs involving delay, the impact of mobility on information transfer, and the optimal usage of resources by using tools from information theory and stochastic evolution theory.