X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/UIC2013.git/blobdiff_plain/97c825d7b15b2a2dc1350641ca38613d63d30efa..ac8d10f19baa2364ff3a23b7401ca0439258561f:/bare_conf.tex

diff --git a/bare_conf.tex b/bare_conf.tex
index 7518ab7..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}
 
@@ -126,8 +130,7 @@ reviews the related work in the field.  Section~\ref{pd} is devoted to
 the    scheduling     strategy    for    energy-efficient    coverage.
 Section~\ref{cp} gives the coverage model formulation which is used to
 schedule  the  activation  of  sensors.  Section~\ref{exp}  shows  the
-simulation  results obtained  using  the discrete  event simulator  on
-OMNeT++  \cite{varga}. They  fully demonstrate  the usefulness  of the
+simulation  results obtained  using  the discrete  event simulator OMNeT++  \cite{varga}. They  fully demonstrate  the usefulness  of the
 proposed  approach.   Finally, we  give  concluding  remarks and  some
 suggestions for future works in Section~\ref{sec:conclusion}.
 
@@ -330,7 +333,7 @@ lifetime         increases        compared         with        related
 work~\cite{Cardei:2005:IWS:1160086.1160098}.   In~\cite{berman04}, the
 authors  have formulated  the lifetime  problem and  suggested another
 (LP)  technique to  solve this  problem. A  centralized  solution  based      on      the     Garg-K\"{o}nemann
-algorithm~\cite{garg98}, probably near
+algorithm~\cite{garg98}, provably near
 the optimal solution,    is also proposed.
 
 {\bf Our contribution}
@@ -415,7 +418,7 @@ each phase in more details.
 \subsection{Information exchange phase}
 
 Each sensor node $j$ sends  its position, remaining energy $RE_j$, and
-the number of local neighbors  $NBR_j$ to all wireless sensor nodes in
+the number of local neighbours  $NBR_j$ to all wireless sensor nodes in
 its subregion by using an INFO  packet and then listens to the packets
 sent from  other nodes.  After that, each  node will  have information
 about  all the  sensor  nodes in  the  subregion.  In  our model,  the
@@ -434,7 +437,7 @@ independently  for each  round.  All the  sensor  nodes cooperate  to
 select WSNL.  The nodes in the  same subregion will  select the leader
 based on  the received  information from all  other nodes in  the same
 subregion.  The selection criteria  in order  of priority  are: larger
-number  of neighbors,  larger remaining  energy, and  then in  case of
+number  of neighbours,  larger remaining  energy, and  then in  case of
 equality, larger index.
 
 \subsection{Decision phase}
@@ -620,7 +623,7 @@ X_{j} \in \{0,1\}, &\forall j \in J
   sensing in the round (1 if yes and 0 if not);
 \item $\Theta_{p}$  : {\it overcoverage}, the number  of sensors minus
   one that are covering the primary point $p$;
-\item $U_{p}$  : {\it undercoverage},  indicates whether or  not the principal point
+\item $U_{p}$  : {\it undercoverage},  indicates whether or  not the primary point
   $p$ is being covered (1 if not covered and 0 if covered).
 \end{itemize}
 
@@ -729,7 +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} 
@@ -739,7 +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 
@@ -751,7 +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} 
@@ -769,7 +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}}
@@ -784,7 +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} 
@@ -799,11 +802,11 @@ number of  rounds increases  the two leaders'  strategy becomes  the most
 performing one, since it takes longer  to have the two subregion networks
 simultaneously disconnected.
 
-\subsection{The number of stopped simulation runs}
+\subsection{The percentage of stopped simulation runs}
 
-We  will now  study  the number  of  simulations which  stopped due  to
+We  will now  study  the percentage  of  simulations which  stopped due  to
 network  disconnections per round  for each  of the  three approaches.
-Figure~\ref{fig6} illustrates the average number of stopped simulation
+Figure~\ref{fig6} illustrates the percentage of stopped simulation
 runs per  round for 150 deployed  nodes.  It can be  observed that the
 simple heuristic is  the approach which  stops first because  the nodes
 are   randomly chosen.   Among  the  two   proposed  strategies,  the
@@ -815,8 +818,8 @@ optimization participates in extending the network lifetime.
 
 \begin{figure}[h!]
 \centering
-\includegraphics[scale=0.55]{TheNumberofStoppedSimulationRuns150.eps} 
-\caption{The number of stopped simulation runs compared to the number of rounds for 150 deployed nodes }
+\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} 
 
@@ -847,7 +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} 
@@ -922,7 +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} 
@@ -970,14 +973,13 @@ single global optimization problem  by partitioning it in many smaller
 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  a  single  round,  using
+computes  all  active  sensor  schedules  in  one time,  using
 optimization  methods  such  as  swarms optimization  or  evolutionary
-algorithms.  This single  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 round is far more
+The computation of all cover sets in one time is far more
 difficult, but will reduce the communication overhead.
-
 % use section* for acknowledgement
 %\section*{Acknowledgment}
 
