ok pour les modifs de michel

diff --git a/bare_conf.tex b/bare_conf.tex
index b9ed86ee16c2511e7c4666e15a18b2ca69305b1d..90482fc24915c489d7e9bdec96cec891811993f6 100755 (executable)
--- a/bare_conf.tex
+++ b/bare_conf.tex
@@ -110,12 +110,10 @@ coverage  problem,  with  the  objective  of  maximizing  the  network
 lifetime  by using  an adaptive  scheduling. The  area of  interest is
 divided into subregions and an activity scheduling for sensor nodes is
 planned for  each subregion. 
 lifetime  by using  an adaptive  scheduling. The  area of  interest is
 divided into subregions and an activity scheduling for sensor nodes is
 planned for  each subregion. 

-% DEBUT AJOUT
-{\bf In fact, the nodes in a  subregion can be seen as a cluster where
+ In fact, the nodes in a  subregion can be seen as a cluster where

   each node sends  sensing data to the cluster head  or the sink node.
   Furthermore, the activities in a subregion/cluster can continue even
   each node sends  sensing data to the cluster head  or the sink node.
   Furthermore, the activities in a subregion/cluster can continue even

-  if another cluster stops due to too much node failures.}
-% FIN AJOUT
+  if another cluster stops due to too much node failures.

 Our scheduling  scheme considers rounds,  where a round starts  with a
 discovery  phase  to  exchange  information  between  sensors  of  the
 subregion,  in order to  choose in  suitable manner  a sensor  node to
 Our scheduling  scheme considers rounds,  where a round starts  with a
 discovery  phase  to  exchange  information  between  sensors  of  the
 subregion,  in order to  choose in  suitable manner  a sensor  node to


@@ -672,11 +670,9 @@ simulator  OMNeT++  \cite{varga}. We  performed  simulations for  five
 different densities varying from 50 to 250~nodes. Experimental results
 were  obtained from  randomly generated  networks in  which  nodes are
 deployed over a  $(50 \times 25)~m^2 $ sensing  field. 
 different densities varying from 50 to 250~nodes. Experimental results
 were  obtained from  randomly generated  networks in  which  nodes are
 deployed over a  $(50 \times 25)~m^2 $ sensing  field. 

-% DEBUT MODIFICATION
-{\bf More precisely, the deployment is controlled at a coarse scale in
+More precisely, the deployment is controlled at a coarse scale in

   order to ensure that the  deployed nodes can fully cover the sensing
   order to ensure that the  deployed nodes can fully cover the sensing

-  field with the given sensing range.}
-% FIN MODIFICATION
+  field with the given sensing range.

 10~simulation  runs  are performed  with
 different  network  topologies for  each  node  density.  The  results
 presented hereafter  are the  average of these  10 runs.  A simulation
 10~simulation  runs  are performed  with
 different  network  topologies for  each  node  density.  The  results
 presented hereafter  are the  average of these  10 runs.  A simulation


@@ -863,11 +859,8 @@ A  sensor  node has  limited  energy  resources  and computing  power,
 therefore it is important that the proposed algorithm has the shortest
 possible execution  time. The energy of  a sensor node  must be mainly
 used   for  the  sensing   phase,  not   for  the   pre-sensing  ones.
 therefore it is important that the proposed algorithm has the shortest
 possible execution  time. The energy of  a sensor node  must be mainly
 used   for  the  sensing   phase,  not   for  the   pre-sensing  ones.

-Table~\ref{table1} gives the average  execution times {\bf in seconds}
-on a laptop of the decision phase
-% DEBUT AJOUT
-{\bf (resolution of the optimization problem)}
-% FIN AJOUT
+Table~\ref{table1} gives the average  execution times  in seconds
+on a laptop of the decision phase (solving of the optimization problem)

 during one  round.  They  are given for  the different  approaches and
 various numbers of sensors.  The lack of any optimization explains why
 the heuristic has very  low execution times.  Conversely, the Strategy
 during one  round.  They  are given for  the different  approaches and
 various numbers of sensors.  The lack of any optimization explains why
 the heuristic has very  low execution times.  Conversely, the Strategy


@@ -922,11 +915,9 @@ nodes  have  been drained  of  their  energy  or each  sensor  network
 monitoring  an area becomes  disconnected.  In  figure~\ref{fig8}, the
 network  lifetime for different  network sizes  and for  both Strategy
 with Two  Leaders and the Simple  Heuristic is illustrated. 
 monitoring  an area becomes  disconnected.  In  figure~\ref{fig8}, the
 network  lifetime for different  network sizes  and for  both Strategy
 with Two  Leaders and the Simple  Heuristic is illustrated. 

-% DEBUT MODIFICATION
-{\bf  We do  not consider  anymore the  centralized Strategy  with One
+  We do  not consider  anymore the  centralized Strategy  with One

   Leader, because, as shown above, this strategy results  in execution
   Leader, because, as shown above, this strategy results  in execution

-  times that quickly become unsuitable for a sensor network.}
-% FIN MODIFICATION
+  times that quickly become unsuitable for a sensor network.

 
 \begin{figure}[h!]
 %\centering
 
 \begin{figure}[h!]
 %\centering


@@ -982,12 +973,9 @@ problems, one per subregion, that can be solved more easily.
 In  future, we plan  to study  and propose  a coverage  protocol which
 computes  all  active  sensor  schedules  in  a  single  round,  using
 optimization  methods  such  as  swarms optimization  or  evolutionary
 In  future, we plan  to study  and propose  a coverage  protocol which
 computes  all  active  sensor  schedules  in  a  single  round,  using
 optimization  methods  such  as  swarms optimization  or  evolutionary

-algorithms.
-% DEBUT AJOUT
-{\bf  This single  round  will still  consists  of 4  phases, but  the
+algorithms.  This single  round  will still  consists  of 4  phases, but  the

   decision phase will compute the schedules for several sensing phases
   decision phase will compute the schedules for several sensing phases

-  which aggregated together define a kind of meta-sensing phase.}
-% FIN AJOUT
+  which aggregated together define a kind of meta-sensing phase.

 The computation of all cover sets in one round is far more
 difficult, but will reduce the communication overhead.
 
 The computation of all cover sets in one round is far more
 difficult, but will reduce the communication overhead.