reading until Section III

diff --git a/LiCO_Journal.tex b/LiCO_Journal.tex
index 45719613ee474b949927fb8e7263c32af8f76e80..02be4ac25396654877c281cb78b2a579f4a8521c 100644 (file)
--- a/LiCO_Journal.tex
+++ b/LiCO_Journal.tex
@@ -45,7 +45,7 @@
         Karine Deschinkel,~\IEEEmembership{}
         Michel Salomon,~\IEEEmembership{}
         and~Rapha\"el Couturier ~\IEEEmembership{} 
         Karine Deschinkel,~\IEEEmembership{}
         Michel Salomon,~\IEEEmembership{}
         and~Rapha\"el Couturier ~\IEEEmembership{} 

-        \thanks{The authors are with FEMTO-ST Institute, UMR 6174 CNRS, University of Franche-Comt\'e,
+        \thanks{The authors are with FEMTO-ST Institute, UMR 6174 CNRS, University of Franche-Comte,

           Belfort, France. Email: ali.idness@edu.univ-fcomte.fr, $\lbrace$karine.deschinkel,
           michel.salomon, raphael.couturier$\rbrace$@univ-fcomte.fr}}
 
           Belfort, France. Email: ali.idness@edu.univ-fcomte.fr, $\lbrace$karine.deschinkel,
           michel.salomon, raphael.couturier$\rbrace$@univ-fcomte.fr}}
 


@@ -63,12 +63,14 @@ scheduling which ensures  sensing coverage while minimizing the  energy cost. In
 this paper,  we propose such an approach  called Lifetime  Coverage Optimization
 protocol (LiCO).   It is a  hybrid of  centralized and distributed  methods: the
 region of interest is first subdivided  into subregions and our protocol is then
 this paper,  we propose such an approach  called Lifetime  Coverage Optimization
 protocol (LiCO).   It is a  hybrid of  centralized and distributed  methods: the
 region of interest is first subdivided  into subregions and our protocol is then

-distributed among sensor nodes in each  subregion. A sensor node which runs LiCO
-protocol  repeats   periodically  four  stages:  information   exchange,  leader
-election, optimization decision, and sensing.  More precisely, the scheduling of
-nodes' activities (sleep/wake up duty cycles) is achieved in each subregion by a
-leader selected after  cooperation between nodes within the  same subregion. The
-novelty  of  our  approach  lies  essentially   in  the  formulation  of  a  new
+distributed among sensor nodes in each  subregion.
+%%RAPH abstract too long
+% A sensor node which runs LiCO
+%protocol  repeats   periodically  four  stages:  information   exchange,  leader
+%election, optimization decision, and sensing.  More precisely, the scheduling of
+%nodes' activities (sleep/wake up duty cycles) is achieved in each subregion by a
+%leader selected after  cooperation between nodes within the  same subregion.
+The novelty  of  our  approach  lies  essentially   in  the  formulation  of  a  new

 mathematical optimization  model based on  perimeter coverage level  to schedule
 sensors' activities.  Extensive simulation experiments have been performed using
 OMNeT++, the  discrete event simulator, to  demonstrate that LiCO is  capable to
 mathematical optimization  model based on  perimeter coverage level  to schedule
 sensors' activities.  Extensive simulation experiments have been performed using
 OMNeT++, the  discrete event simulator, to  demonstrate that LiCO is  capable to


@@ -121,23 +123,23 @@ lifetime of the WSNs~\cite{rault2014energy}.
 
 This paper makes the following contributions.
 \begin{enumerate}
 
 This paper makes the following contributions.
 \begin{enumerate}

-\item We devise a framework to schedule nodes to be activated alternatively such
+\item We have devised a framework to schedule nodes to be activated alternatively such

   that the network lifetime is prolonged  while ensuring that a certain level of
   coverage is preserved.  A key idea in  our framework is to exploit spatial and
   that the network lifetime is prolonged  while ensuring that a certain level of
   coverage is preserved.  A key idea in  our framework is to exploit spatial and

-  temporal subdivision.   On the one hand  the area of interest  if divided into
-  several smaller subregions and on the other hand the time line is divided into
+  temporal subdivision.   On the one hand,  the area of interest  if divided into
+  several smaller subregions and, on the other hand, the time line is divided into

   periods of equal length. In each subregion the sensor nodes will cooperatively
   choose a  leader which will schedule  nodes' activities, and this  grouping of
   sensors is similar to typical cluster architecture.
   periods of equal length. In each subregion the sensor nodes will cooperatively
   choose a  leader which will schedule  nodes' activities, and this  grouping of
   sensors is similar to typical cluster architecture.

-\item We  propose a new mathematical  optimization model.  Instead of  trying to
+\item We have proposed a new mathematical  optimization model.  Instead of  trying to

   cover a set of specified points/targets as  in most of the methods proposed in
   the literature, we formulate an integer program based on perimeter coverage of
   each sensor.  The  model involves integer variables to  capture the deviations
   cover a set of specified points/targets as  in most of the methods proposed in
   the literature, we formulate an integer program based on perimeter coverage of
   each sensor.  The  model involves integer variables to  capture the deviations

-  between  the actual  level of  coverage and  the required  level.  So  that an
+  between  the actual  level of  coverage and  the required  level.  Hence, an

   optimal scheduling  will be  obtained by  minimizing a  weighted sum  of these
   deviations.
   optimal scheduling  will be  obtained by  minimizing a  weighted sum  of these
   deviations.

-\item We  conducted extensive simulation  experiments, using the  discrete event
-  simulator OMNeT++, to demonstrate the  efficiency of our protocol. We compared
+\item We have conducted extensive simulation  experiments, using the  discrete event
+  simulator OMNeT++, to demonstrate the  efficiency of our protocol. We have compared

   our   LiCO   protocol   to   two   approaches   found   in   the   literature:
   DESK~\cite{ChinhVu} and  GAF~\cite{xu2001geography}, and also to  our previous
   work published in~\cite{Idrees2} which is  based on another optimization model
   our   LiCO   protocol   to   two   approaches   found   in   the   literature:
   DESK~\cite{ChinhVu} and  GAF~\cite{xu2001geography}, and also to  our previous
   work published in~\cite{Idrees2} which is  based on another optimization model


@@ -153,7 +155,7 @@ is devoted to the LiCO protocol  description and Section~\ref{cp} focuses on the
 coverage model  formulation which is used  to schedule the activation  of sensor
 nodes.  Section~\ref{sec:Simulation  Results and Analysis}  presents simulations
 results and discusses the comparison  with other approaches. Finally, concluding
 coverage model  formulation which is used  to schedule the activation  of sensor
 nodes.  Section~\ref{sec:Simulation  Results and Analysis}  presents simulations
 results and discusses the comparison  with other approaches. Finally, concluding

-remarks   are  drawn   and  some   suggestions   given  for   future  works   in
+remarks   are  drawn   and  some   suggestions are  given  for   future  works   in

 Section~\ref{sec:Conclusion and Future Works}.
 
 % that show that our protocol outperforms others protocols.
 Section~\ref{sec:Conclusion and Future Works}.
 
 % that show that our protocol outperforms others protocols.


@@ -167,7 +169,7 @@ the literature.
 The most  discussed coverage problems in  literature can be classified  in three
 categories~\cite{li2013survey}   according   to  their   respective   monitoring
 objective.  Hence,  area coverage \cite{Misra}  means that every point  inside a
 The most  discussed coverage problems in  literature can be classified  in three
 categories~\cite{li2013survey}   according   to  their   respective   monitoring
 objective.  Hence,  area coverage \cite{Misra}  means that every point  inside a

-fixed area  must be monitored, while  target coverage~\cite{yang2014novel} refer
+fixed area  must be monitored, while  target coverage~\cite{yang2014novel} refers

 to  the objective  of coverage  for a  finite number  of discrete  points called
 targets,  and  barrier coverage~\cite{HeShibo}\cite{kim2013maximum}  focuses  on
 preventing  intruders   from  entering   into  the   region  of   interest.   In
 to  the objective  of coverage  for a  finite number  of discrete  points called
 targets,  and  barrier coverage~\cite{HeShibo}\cite{kim2013maximum}  focuses  on
 preventing  intruders   from  entering   into  the   region  of   interest.   In


@@ -178,7 +180,7 @@ nodes    or   between    disk   of    sensor   nodes    and   boundaries.     In
 sensors are sufficiently  covered it will be  the case for the  whole area. They
 provide an algorithm in $O(nd~log~d)$  time to compute the perimeter-coverage of
 each  sensor,  where  $d$  denotes  the  maximum  number  of  sensors  that  are
 sensors are sufficiently  covered it will be  the case for the  whole area. They
 provide an algorithm in $O(nd~log~d)$  time to compute the perimeter-coverage of
 each  sensor,  where  $d$  denotes  the  maximum  number  of  sensors  that  are

-neighboring  to  a  sensor and  $n$  is  the  total  number of  sensors  in  the
+neighbors  to  a  sensor and  $n$  is  the  total  number of  sensors  in  the

 network. {\it In LiCO protocol, instead  of determining the level of coverage of
   a set  of discrete  points, our  optimization model is  based on  checking the
   perimeter-coverage of each sensor to activate a minimal number of sensors.}
 network. {\it In LiCO protocol, instead  of determining the level of coverage of
   a set  of discrete  points, our  optimization model is  based on  checking the
   perimeter-coverage of each sensor to activate a minimal number of sensors.}


@@ -203,28 +205,28 @@ each period.   {\it Motivated by  these works,  LiCO protocol works  in periods,
   decisions, followed by a sensing phase where one cover set is in charge of the
   sensing task.}
 
   decisions, followed by a sensing phase where one cover set is in charge of the
   sensing task.}
 

-Various centralized  and distributed approaches, or  even a mixing of  these two
+Various centralized  and distributed approaches, or  even a mixing  of these two

 concepts, have  been proposed  to extend the  network lifetime.   In distributed
 algorithms~\cite{yangnovel,ChinhVu,qu2013distributed} each sensor decides of its
 own activity scheduling  after an information exchange with  its neighbors.  The
 concepts, have  been proposed  to extend the  network lifetime.   In distributed
 algorithms~\cite{yangnovel,ChinhVu,qu2013distributed} each sensor decides of its
 own activity scheduling  after an information exchange with  its neighbors.  The

-main interest of a such approach is  to avoid long range communications and thus
+main interest of such an approach is to avoid long range communications and thus

 to reduce the energy dedicated to the communications.  Unfortunately, since each
 to reduce the energy dedicated to the communications.  Unfortunately, since each

-node has only information on its  immediate neighbors (usually the one-hop ones)
+node has only information on  its immediate neighbors (usually the one-hop ones)

 it may take a bad decision leading to a global suboptimal solution.  Conversely,
 centralized
 it may take a bad decision leading to a global suboptimal solution.  Conversely,
 centralized

-algorithms~\cite{cardei2005improving,zorbas2010solving,pujari2011high}    always
+algorithms~\cite{cardei2005improving,zorbas2010solving,pujari2011high}     always

 provide nearly  or close to  optimal solution since  the algorithm has  a global
 view of the whole network. The disadvantage of a centralized method is obviously
 provide nearly  or close to  optimal solution since  the algorithm has  a global
 view of the whole network. The disadvantage of a centralized method is obviously

-its high cost  in communications needed to  transmit to a single  node, the base
-station which will globally schedule nodes'  activities, data from all the other
-sensor nodes in  the area.  The price  in communications can be  very huge since
-long range communications will be needed. In fact the larger the WNS, the higher
-the  communication and  thus energy  cost.   {\it In  order to  be suitable  for
-  large-scale networks,  in the LiCO  protocol the  area of interest  is divided
-  into several smaller subregions, and in each  one, a node called the leader is
-  in charge  for selecting the active  sensors for the current  period. Thus our
-  protocol  is  scalable  and  a  globally distributed  method,  whereas  it  is
-  centralized in each subregion.}
+its high  cost in communications needed to  transmit to a single  node, the base
+station which will globally schedule  nodes' activities, data from all the other
+sensor nodes  in the area.  The price  in communications can be  very huge since
+long range  communications will be  needed. In fact  the larger the WNS  is, the
+higher the  communication and  thus the energy  cost are.   {\it In order  to be
+  suitable for large-scale  networks, in the LiCO protocol,  the area of interest
+  is divided into several smaller subregions, and in each one, a node called the
+  leader  is  in  charge  of  selecting  the active  sensors  for  the  current
+  period.  Thus our  protocol is  scalable  and is a  globally distributed  method,
+  whereas it is centralized in each subregion.}

 
 Various  coverage scheduling  algorithms have  been developed  this last  years.
 Many of  them, dealing with  the maximization of the  number of cover  sets, are
 
 Various  coverage scheduling  algorithms have  been developed  this last  years.
 Many of  them, dealing with  the maximization of the  number of cover  sets, are


@@ -239,7 +241,7 @@ optimization  solver).  The  problem is  formulated as  an optimization  problem
 energy  constraints.   Column  generation   techniques,  well-known  and  widely
 practiced techniques for  solving linear programs with too  many variables, have
 also                                                                        been
 energy  constraints.   Column  generation   techniques,  well-known  and  widely
 practiced techniques for  solving linear programs with too  many variables, have
 also                                                                        been

-used~\cite{castano2013column,rossi2012exact,deschinkel2012column}. {\it  In LiCO
+used~\cite{castano2013column,rossi2012exact,deschinkel2012column}. {\it  In the LiCO

   protocol, each  leader, in charge  of a  subregion, solves an  integer program
   which has a twofold objective: minimize the overcoverage and the undercoverage
   of the perimeter of each sensor.}
   protocol, each  leader, in charge  of a  subregion, solves an  integer program
   which has a twofold objective: minimize the overcoverage and the undercoverage
   of the perimeter of each sensor.}