Abstract
Cognitive Networks working on large scale are experimenting an increasing popularity. The interest, by both a scientific and commercial perspective, in the context of different environments, applications and domains is a fact. The natural convergence point for these heterogeneous disciplines is the need of a strong advanced technologic support that enables the generation of distributed observations on large scale as well as the intelligent process of obtained information. Focusing mostly on cognitive networks that generate information directly through sensor networks, existent solutions at level of metropolitan area are mainly limited by the use of obsolete/static coverage models as well as by a fundamental lack of flexibility respect to the dynamic features of the virtual organizations. Furthermore, the centralized view at the systems is a strong limitation for dynamic data processing and knowledge building.
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Pileggi, S.F., Fernandez-Llatas, C., Traver, V. (2013). Metropolitan Ecosystems among Heterogeneous Cognitive Networks: Issues, Solutions and Challenges. In: Fred, A., Dietz, J.L.G., Liu, K., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2011. Communications in Computer and Information Science, vol 348. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37186-8_21
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DOI: https://doi.org/10.1007/978-3-642-37186-8_21
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