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documents/manifest/manifest.tex
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\begin{figure}[H]
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\centering
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  \includegraphics[width=.65\linewidth]{workflow.pdf}
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  \includegraphics[width=.55\linewidth]{workflow.pdf}
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  \caption{Workflow of Experiment to allow autocorrelation analysis}
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  \label{fig:workflow}
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\end{figure}
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To simulate dynamic graphs we will use the Mobility Models implemented \href{https://github.com/panisson/pymobility}{here}.
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Supported Mobility models are:
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\begin{multicols}{2}
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\begin{multicols}{3}
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\footnotesize
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  \begin{itemize}
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     \item Random Walk
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     \item Random Waypoint
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     \item Random Direction
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     \item Truncated Levy Walk \cite{rhee2011levy}
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     \item Gauss-Markov \cite{camp2002survey}
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     \item Reference Point Group Mobility model \cite{hong1999group}
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     \item Reference Point Group \cite{hong1999group}
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     \item Time-variant Community \cite{hsu2007modeling}
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  \end{itemize}
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\end{multicols}
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\section{Preliminary Demo}\label{sec:demo}
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\begin{multicols}{3}
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\footnotesize
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  \begin{itemize}
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  \item duration: 10000,
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  \item  max\_velocity: 1.0,
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  \item  max\_wait\_time: 1.0,
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  \item  max\_x: 100,
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  \item  max\_y: 100,
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  \item  min\_velocity: 0.1,
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  \item  mob\_model: RandomWayPoint,
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  \item  mobility\_timer: 1.0,
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  \item  nodes\_number: 50,
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  \item  radius: 30.0
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  \end{itemize}
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\end{multicols}
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\begin{figure}[H]
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\centering
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  \includegraphics[width=.8\linewidth]{BCACF.pdf}
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  \caption{ACF averaged over all Nodes}
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  \label{fig:BCACF}
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\end{figure}
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\subsection{How is computed the above Mean ACF}
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For every time-instant we have a graph. For each node in the graph we have its \ac{BC} index.
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Therefore, for each node we can compute the ACF over the time-series of \ac{BC} for that node. Given that we have 50 nodes, for any time-lag we have
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50 ACF values. The average ACF is computed over the 50 nodes for time-lags spanning from 0 to 750. 
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This average ACF is plotted in \cref{fig:BCACF}
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\bibliographystyle{IEEEtran}

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