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Closeness centralization of a network describes the organization of nodes in strongly connected groups.
Network community analysis has become an invaluable tool to explore and reveal the internal organization of nodes.
Degree centralization determines the extent to which the organization of nodes in the network is around some central points.
Clustered organization of nodes with aggregation of data at the cluster head becomes one of the significant means to extend life expectancy of the network.
A hierarchical organization of nodes according to some authority or centrality measure allows to visualize the graph at different levels of details, eliminating the need to display all nodes and edges at once [60].
Inspired on a previous study on a dynamic addressing paradigm, the "Dynamic address routing for scalable ad-hoc and mesh networks" DART[4], is proposed as a proactive hierarchical approach that efficiently manages the organization of nodes into zones for large scale networks.
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On the other hand, in infrastructureless or fully distributed solutions, like the one we propose here, security does not rely on TTPs or special kind of servers but on self-organization of nodes.
A further feature of tactical scenarios is the presence of a hierarchical organization of the nodes.
Graph theory is a mathematical tool that has been recently applied to rs-fMRI data with the goal of studying the organization of network nodes (i.e., connections) at both the local and whole-brain levels [Bullmore and Sporns, 2007; Buckner et al., 2009].
Therefore, the variability in the geometry and position of the nodes and notochords implies the loss of a critical regulatory mechanism in FN-null and integrin α5-null mutants, leading to stochastic organization of the node and "off-midline" placement of the notochord.
However, the selection and organization of suitable sensor nodes are ignored in the task.
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