13,250 research outputs found
Equation-free analysis of a dynamically evolving multigraph
In order to illustrate the adaptation of traditional continuum numerical
techniques to the study of complex network systems, we use the equation-free
framework to analyze a dynamically evolving multigraph. This approach is based
on coupling short intervals of direct dynamic network simulation with
appropriately-defined lifting and restriction operators, mapping the detailed
network description to suitable macroscopic (coarse-grained) variables and
back. This enables the acceleration of direct simulations through Coarse
Projective Integration (CPI), as well as the identification of coarse
stationary states via a Newton-GMRES method. We also demonstrate the use of
data-mining, both linear (principal component analysis, PCA) and nonlinear
(diffusion maps, DMAPS) to determine good macroscopic variables (observables)
through which one can coarse-grain the model. These results suggest methods for
decreasing simulation times of dynamic real-world systems such as
epidemiological network models. Additionally, the data-mining techniques could
be applied to a diverse class of problems to search for a succint,
low-dimensional description of the system in a small number of variables
Dissimilarity metric based on local neighboring information and genetic programming for data dissemination in vehicular ad hoc networks (VANETs)
This paper presents a novel dissimilarity metric based on local neighboring information
and a genetic programming approach for efficient data dissemination in Vehicular Ad Hoc Networks
(VANETs). The primary aim of the dissimilarity metric is to replace the Euclidean distance in
probabilistic data dissemination schemes, which use the relative Euclidean distance among vehicles
to determine the retransmission probability. The novel dissimilarity metric is obtained by applying a
metaheuristic genetic programming approach, which provides a formula that maximizes the Pearson
Correlation Coefficient between the novel dissimilarity metric and the Euclidean metric in several
representative VANET scenarios. Findings show that the obtained dissimilarity metric correlates with
the Euclidean distance up to 8.9% better than classical dissimilarity metrics. Moreover, the obtained
dissimilarity metric is evaluated when used in well-known data dissemination schemes, such as
p-persistence, polynomial and irresponsible algorithm. The obtained dissimilarity metric achieves
significant improvements in terms of reachability in comparison with the classical dissimilarity
metrics and the Euclidean metric-based schemes in the studied VANET urban scenarios
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