The end of Section 2 (Literature review) must be rewritten

diff --git a/Example.tex b/Example.tex
index a5b366a44808e307739e5434e38cbf367b847add..b461e0d281b32c7394a917e194e3870f51900fcd 100644 (file)
--- a/Example.tex
+++ b/Example.tex
@@ -27,7 +27,7 @@
 \title{Distributed Lifetime Coverage Optimization Protocol \\in Wireless Sensor Networks}
 
 \author{\authorname{Ali Kadhum Idrees, Karine Deschinkel, Michel Salomon, and Rapha\"el Couturier}
 \title{Distributed Lifetime Coverage Optimization Protocol \\in Wireless Sensor Networks}
 
 \author{\authorname{Ali Kadhum Idrees, Karine Deschinkel, Michel Salomon, and Rapha\"el Couturier}

-\affiliation{FEMTO-ST Institute, UMR 6174 CNRS, University of Franche-Comte, Belfort, France}
+\affiliation{FEMTO-ST Institute, UMR 6174 CNRS, University of Franche-Comt\'e, Belfort, France}

 %\affiliation{\sup{2}Department of Computing, Main University, MySecondTown, MyCountry}
 \email{ali.idness@edu.univ-fcomte.fr, $\lbrace$karine.deschinkel, michel.salomon, raphael.couturier$\rbrace$@univ-fcomte.fr}
 %\email{\{f\_author, s\_author\}@ips.xyz.edu, t\_author@dc.mu.edu}
 %\affiliation{\sup{2}Department of Computing, Main University, MySecondTown, MyCountry}
 \email{ali.idness@edu.univ-fcomte.fr, $\lbrace$karine.deschinkel, michel.salomon, raphael.couturier$\rbrace$@univ-fcomte.fr}
 %\email{\{f\_author, s\_author\}@ips.xyz.edu, t\_author@dc.mu.edu}


@@ -162,6 +162,11 @@ constraints.   Column  generation techniques,  well-known  and widely  practiced
 techniques for solving  linear programs with too many  variables, have been also
 used~\cite{castano2013column,rossi2012exact,deschinkel2012column}.
 
 techniques for solving  linear programs with too many  variables, have been also
 used~\cite{castano2013column,rossi2012exact,deschinkel2012column}.
 

+% ***** Part which must be rewritten - Start
+
+% Start of Ali's papers catalog => there's no link between them or with our work
+% (use of subregions; optimization based method; etc.)
+

 Diongue  and  Thiare~\cite{diongue2013alarm}  proposed  an  energy  aware  sleep
 scheduling  algorithm  for lifetime  maximization  in  wireless sensor  networks
 (ALARM).  The proposed approach permits to schedule redundant nodes according to
 Diongue  and  Thiare~\cite{diongue2013alarm}  proposed  an  energy  aware  sleep
 scheduling  algorithm  for lifetime  maximization  in  wireless sensor  networks
 (ALARM).  The proposed approach permits to schedule redundant nodes according to


@@ -192,6 +197,8 @@ algorithm in order to produce the set  of active nodes which take the mission of
 sensing during the current epoch.  After  that, the produced schedule is sent to
 the sensor nodes in the network.
 
 sensing during the current epoch.  After  that, the produced schedule is sent to
 the sensor nodes in the network.
 

+% What is the link between the previous work and this paragraph about DiLCO ?
+

 {\it  In DiLCO  protocol,  the area  coverage  is divided  into several  smaller
   subregions, and in  each of which, a  node called the leader is  on charge for
   selecting the active sensors for the current period.}
 {\it  In DiLCO  protocol,  the area  coverage  is divided  into several  smaller
   subregions, and in  each of which, a  node called the leader is  on charge for
   selecting the active sensors for the current period.}


@@ -208,12 +215,15 @@ The work proposed by  \cite{qu2013distributed} considers the coverage problem in
 WSNs where  each sensor has variable  sensing radius. The final  objective is to
 maximize the network coverage lifetime in WSNs.
 
 WSNs where  each sensor has variable  sensing radius. The final  objective is to
 maximize the network coverage lifetime in WSNs.
 

+% Same remark, no link with the two previous citations...

 {\it  In DiLCO  protocol,  each leader,  in  each subregion,  solves an  integer
   program with a double objective  consisting in minimizing the overcoverage and
   limiting  the  undercoverage.  This  program  is inspired  from  the  work  of
   \cite{pedraza2006}  where the  objective is  to maximize  the number  of cover
   sets.}
  
 {\it  In DiLCO  protocol,  each leader,  in  each subregion,  solves an  integer
   program with a double objective  consisting in minimizing the overcoverage and
   limiting  the  undercoverage.  This  program  is inspired  from  the  work  of
   \cite{pedraza2006}  where the  objective is  to maximize  the number  of cover
   sets.}
  

+% ***** Part which must be rewritten - End
+

 \iffalse
 
 Some algorithms have been developed in ~\cite{yang2014energy,ChinhVu,vashistha2007energy,deschinkel2012column,shi2009,qu2013distributed,ling2009energy,xin2009area,cheng2014achieving,ling2009energy} to solve the area coverage problem so as to preserve coverage and prolong the network lifetime.
 \iffalse
 
 Some algorithms have been developed in ~\cite{yang2014energy,ChinhVu,vashistha2007energy,deschinkel2012column,shi2009,qu2013distributed,ling2009energy,xin2009area,cheng2014achieving,ling2009energy} to solve the area coverage problem so as to preserve coverage and prolong the network lifetime.


@@ -1023,12 +1033,12 @@ the performance of our approach, we  compared it with two other approaches using
 many performance metrics  like coverage ratio or network  lifetime. We have also
 study the  impact of the  number of subregions  chosen to subdivide the  area of
 interest,  considering  different  network  sizes.  The  experiments  show  that
 many performance metrics  like coverage ratio or network  lifetime. We have also
 study the  impact of the  number of subregions  chosen to subdivide the  area of
 interest,  considering  different  network  sizes.  The  experiments  show  that

-increasing the  number of subregions allows  to improves the  lifetime. The more
-there  are   subregions,  the  more   the  network  is  robust   against  random
-disconnection resulting from dead nodes.  However, for a given sensing field and
-network size  there is an optimal  number of subregions.  Therefore,  in case of
-our simulation  context a  subdivision in $16$~subregions  seems to be  the most
-relevant. The optimal number of subregions will be investigated in the future.
+increasing the  number of subregions improves  the lifetime. The  more there are
+subregions,  the  more  the  network  is  robust  against  random  disconnection
+resulting from dead nodes.  However, for  a given sensing field and network size
+there is an optimal number of  subregions.  Therefore, in case of our simulation
+context  a subdivision in  $16$~subregions seems  to be  the most  relevant. The
+optimal number of subregions will be investigated in the future.

 
 \iffalse
 \noindent In this paper, we have  addressed the problem of the coverage and the lifetime
 
 \iffalse
 \noindent In this paper, we have  addressed the problem of the coverage and the lifetime