X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/UIC2013.git/blobdiff_plain/a96a3c20a51ff741b0b50ea566e9fab35f311ea1..b55de8fd7068f3bdaa7f15bcb2de807127e19531:/bare_conf.tex?ds=inline

diff --git a/bare_conf.tex b/bare_conf.tex
index 50c500f..832076e 100755
--- a/bare_conf.tex
+++ b/bare_conf.tex
@@ -1,6 +1,3 @@
-
-
- 
 \documentclass[conference]{IEEEtran}
 
 \ifCLASSINFOpdf
@@ -11,7 +8,7 @@
 
 \hyphenation{op-tical net-works semi-conduc-tor}
 
-\usepackage{float}
+\usepackage{float} 
 \usepackage{epsfig}
 \usepackage{calc}
  \usepackage{times,amssymb,amsmath,latexsym}
@@ -33,6 +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}
 
@@ -87,13 +88,13 @@ several  domains ranging  from  health care  applications to  military
 applications.  A sensor network is  composed of a large number of tiny
 sensing  devices deployed  in a  region of  interest. Each  device has
 processing  and wireless communication  capabilities, which  enable it to
-sense its environment, to compute, to store information and to deliver
+sense its environment, to compute, to store information, and to deliver
 report messages to a base station.
 %These sensor nodes run on batteries with limited capacities. To achieve a long life of the network, it is important to conserve battery power. Therefore, lifetime optimisation is one of the most critical issues in wireless sensor networks.
 One of the main design issues in Wireless Sensor Networks (WSNs) is to
 prolong the  network lifetime,  while achieving acceptable  quality of
 service for applications.  Indeed, sensor nodes have limited resources
-in terms of memory, energy and computational power.
+in terms of memory, energy, and computational power.
 
 Since sensor nodes have limited battery life and without being able to
 replace batteries,  especially in remote and  hostile environments, it
@@ -129,8 +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}.
 
@@ -191,7 +191,7 @@ transmit information on an event in the area that it monitors.
 
 {\bf Activity scheduling}
 
-Activitiy scheduling is to  schedule the activation and deactivation of
+Activity scheduling is to  schedule the activation and deactivation of
 sensor nodes.  The  basic objective is to decide  which sensors are in
 what states (active or sleeping mode)  and for how long, so that the
 application  coverage requirement  can be  guaranteed and  the network
@@ -333,7 +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}
@@ -355,7 +355,7 @@ sections.
   decision  is  a  good   compromise  between  these  two  conflicting
   objectives.
 
-\item {\bf  Which node  should make such a decision?}  As  mentioned in
+\item {\bf Which  node should make such a  decision?}  As mentioned in
   \cite{pc10}, both centralized  and distributed algorithms have their
   own  advantages and  disadvantages. Centralized  coverage algorithms
   have the advantage  of requiring very low processing  power from the
@@ -365,10 +365,10 @@ sections.
   that there is a threshold in  terms of network size to switch from a
   localized  to  a  centralized  algorithm.  Indeed  the  exchange  of
   messages  in large  networks may  consume a  considerable  amount of
-  energy in  a localized approach  compared to a centralized  one. Our
+  energy in a centralized approach  compared to a distributed one. Our
   work does not  consider only one leader to  compute and to broadcast
-  the  scheduling decision  to all  the sensors.  When the  network size
-  increases,  the  network  is  divided  into many  subregions  and  the
+  the scheduling decision  to all the sensors.  When  the network size
+  increases,  the network  is  divided into  many  subregions and  the
   decision is made by a leader in each subregion.
 \end{itemize}
 
@@ -418,7 +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
@@ -437,7 +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}
@@ -623,7 +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}
 
@@ -692,7 +692,7 @@ active  node will  consume  12~joules during the sensing  phase, while  a
 sleeping  node will  use  0.002  joules.  Each  sensor  node will  not
 participate in the next round if its remaining energy is less than 12
 joules.  In  all  experiments  the  parameters  are  set  as  follows:
-$R_s=5m$, $w_{\Theta}=1$, and $w_{U}=|P^2|$.
+$R_s=5~m$, $w_{\Theta}=1$, and $w_{U}=|P^2|$.
 
 We  evaluate the  efficiency of  our approach by using  some performance
 metrics such as: coverage ratio,  number of active nodes ratio, energy
@@ -732,7 +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} 
@@ -742,7 +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 
@@ -754,7 +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} 
@@ -772,7 +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}}
@@ -787,7 +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} 
@@ -802,11 +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
@@ -818,8 +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} 
 
@@ -835,7 +835,7 @@ which  is obtained  for 10~simulation  runs,  is then  divided by  the
 average number of rounds to define a metric allowing a fair comparison
 between networks having different densities.
 
-Figure~\ref{fig7} illustrates the Energy Consumption for the different
+Figure~\ref{fig7} illustrates the energy consumption for the different
 network  sizes and  the three  approaches. The  results show  that the
 strategy  with  two  leaders  is  the  most  competitive  from  the energy
 consumption point  of view.  A  centralized method, like  the strategy
@@ -850,7 +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} 
@@ -925,7 +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} 
@@ -944,7 +944,7 @@ subdividing the sensing field  into many subregions, which are managed
 independently and simultaneously, is the most relevant way to maximize
 the lifetime of a network.
 
-\section{Conclusion and future forks}
+\section{Conclusion and future works}
 \label{sec:conclusion}
 
 In this paper, we have  addressed the problem of the coverage and the lifetime
@@ -973,14 +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}
 
