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To characterize the connectivity between sensor nodes, we used the well-known unit disk graph model.
In order to make a connection between two tree nodes, we used doubly linked list data structure as a part of the object.
For the virtualization of substrate nodes, we used the Xen Cloud Platform (XCP) [18] that includes the Xen Hypervisor as well as Xen API (Xen Management API or XAPI).
To extract the most relevant nodes, we used CentiScaPe to select all nodes having all centrality values over the average.
To categorize bridge nodes, we used a method similar to the degree distribution and assessed betweenness values by their rank (Additional file 7).
To calculate the posterior probability of ancestral distribution at internal nodes, we used a Bayesian approach implemented in the updated version of Statistical Dispersal Vicariance Analysis (S-DIVA), RASP v.2.0b [ 67, 68], and our generic-level phylogeny.
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For the communication between nodes we use MPI.
To simulate multi-level nodes, we use a Boolean variable to denote each level greater than 1.
In order to distinguish dead nodes from disconnected nodes, we use battery-low signal; whenever a node's battery goes into weak stage, it broadcasts a 'low battery' message to all neighbors.
In the following, we first describe a simple distributed solution that does not exploit neither mobility nor cooperation among nodes; we use this solution as a reference solution to compare with our proposal.
For choosing neighboring nodes of nodes, as well as for assigning parts of the domain to the nodes, we use the Voronoi diagram (Fortune, 1987).
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