X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/UIC2013.git/blobdiff_plain/8a8e3cba6646345a937275982087deadde252628..04c09785f4cb24f21708a0e95672ce7bcc4290bf:/bare_conf.tex?ds=inline diff --git a/bare_conf.tex b/bare_conf.tex index 030f484..1a9602b 100755 --- a/bare_conf.tex +++ b/bare_conf.tex @@ -30,6 +30,10 @@ \usepackage{caption} \usepackage{multicol} +\usepackage{graphicx,epstopdf} +\epstopdfsetup{suffix=} +\DeclareGraphicsExtensions{.ps} +\DeclareGraphicsRule{.ps}{pdf}{.pdf}{`ps2pdf -dEPSCrop -dNOSAFER #1 \noexpand\OutputFile} \begin{document} @@ -728,7 +732,7 @@ subregion. \parskip 0pt \begin{figure}[h!] \centering -\includegraphics[scale=0.55]{TheCoverageRatio150.eps} %\\~ ~ ~(a) +\includegraphics[scale=0.5]{TheCoverageRatio150g.eps} %\\~ ~ ~(a) \caption{The impact of the number of rounds on the coverage ratio for 150 deployed nodes} \label{fig3} \end{figure} @@ -738,7 +742,7 @@ subregion. It is important to have as few active nodes as possible in each round, in order to minimize the communication overhead and maximize the network lifetime. This point is assessed through the Active Sensors -Ratio, which is defined as follows: +Ratio (ASR), which is defined as follows: \begin{equation*} \scriptsize \mbox{ASR}(\%) = \frac{\mbox{Number of active sensors @@ -750,7 +754,7 @@ for 150 deployed nodes. \begin{figure}[h!] \centering -\includegraphics[scale=0.55]{TheActiveSensorRatio150.eps} %\\~ ~ ~(a) +\includegraphics[scale=0.5]{TheActiveSensorRatio150g.eps} %\\~ ~ ~(a) \caption{The impact of the number of rounds on the active sensors ratio for 150 deployed nodes } \label{fig4} \end{figure} @@ -768,7 +772,7 @@ lifetime of the network. \subsection{The impact of the number of rounds on the energy saving ratio} In this experiment, we consider a performance metric linked to energy. -This metric, called Energy Saving Ratio, is defined by: +This metric, called Energy Saving Ratio (ESR), is defined by: \begin{equation*} \scriptsize \mbox{ESR}(\%) = \frac{\mbox{Number of alive sensors during this round}} @@ -783,7 +787,7 @@ for all three approaches and for 150 deployed nodes. %\centering % \begin{multicols}{6} \centering -\includegraphics[scale=0.55]{TheEnergySavingRatio150.eps} %\\~ ~ ~(a) +\includegraphics[scale=0.5]{TheEnergySavingRatio150g.eps} %\\~ ~ ~(a) \caption{The impact of the number of rounds on the energy saving ratio for 150 deployed nodes} \label{fig5} \end{figure} @@ -814,7 +818,7 @@ optimization participates in extending the network lifetime. \begin{figure}[h!] \centering -\includegraphics[scale=0.55]{TheNumberofStoppedSimulationRuns150.eps} +\includegraphics[scale=0.5]{TheNumberofStoppedSimulationRuns150g.eps} \caption{The percentage of stopped simulation runs compared to the number of rounds for 150 deployed nodes } \label{fig6} \end{figure} @@ -846,7 +850,7 @@ communications have a small impact on the network lifetime. \begin{figure}[h!] \centering -\includegraphics[scale=0.55]{TheEnergyConsumption.eps} +\includegraphics[scale=0.5]{TheEnergyConsumptiong.eps} \caption{The energy consumption} \label{fig7} \end{figure} @@ -921,7 +925,7 @@ with two leaders and the simple heuristic is illustrated. %\centering % \begin{multicols}{6} \centering -\includegraphics[scale=0.5]{TheNetworkLifetime.eps} %\\~ ~ ~(a) +\includegraphics[scale=0.5]{TheNetworkLifetimeg.eps} %\\~ ~ ~(a) \caption{The network lifetime } \label{fig8} \end{figure} @@ -971,7 +975,7 @@ problems, one per subregion, that can be solved more easily. In future work, we plan to study and propose a coverage protocol which computes all active sensor schedules in one time, using optimization methods such as swarms optimization or evolutionary -algorithms. The round will still consists of 4 phases, but the +algorithms. The round will still consist of 4 phases, but the decision phase will compute the schedules for several sensing phases which, aggregated together, define a kind of meta-sensing phase. The computation of all cover sets in one time is far more