From: ali@ali.com <ali-Latitude-E6430.fr>
Date: Thu, 29 Aug 2013 09:17:37 +0000 (+0200)
Subject: LastUpdate_Ali
X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/UIC2013.git/commitdiff_plain/04c09785f4cb24f21708a0e95672ce7bcc4290bf?hp=8a8e3cba6646345a937275982087deadde252628

LastUpdate_Ali
---

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