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We use a percolation network to study the effect of random topology on the displacement processes, and the effect of the geometry of the pore space is investigated by using various probability density functions for the effective radii of the tubes.
Figure 7 An example of random topology.
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In case of random topologies, this study considers ten different random topologies and their simulation results are averaged.
An interesting extension is the study of random topologies, in particular random regular graphs [62], but this problem will be tackled in another article.
An example of random topologies generated in the simulator is depicted in Figure 7 where 100 ad hoc nodes are placed.
We investigate a scenario, as shown in Figure 4, where TICA, which is based on one-way interference-range edge coloring, has been used for CA. This scenario consists of a random topology comprised of 36 MRs where Node 15 is the gateway.
At d = 18, short-term fair capacity of the random topology is 53% of the tree topology.
Thus, the traffic sources in each realization of the random topology for each CAA are the end nodes of the SPT and the end nodes of the MST.
Examining the (binary) topological organization of the resulting network, revealed high levels of binary clustering (mean/standard: 0.66/0.033) and normalized clustering (1.46/0.25, when compared with 1000 random networks), suggesting a high level of local organization, higher than one can expect on basis of a random topology (Fig. 1 a).
The average degree of the random topology generated by this algorithm is related to the distance d and the distribution of the nodes, and is not controllable.
It is observed that the short-term fairness of the random topology does not depend significantly on n for large networks.
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