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[JournalMultiPeriods.git] / reponse.tex

diff --git a/reponse.tex b/reponse.tex
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--- a/reponse.tex
+++ b/reponse.tex
@@ -41,15 +41,15 @@ by Ali Kadhum Idrees, Karine Deschinkel, Michel Salomon, and Raph\"ael Couturier
 
 Dear Editor and Reviewers,
 
 
 Dear Editor and Reviewers,
 

-First of all, we would like to thank you very much for your kind help to improve
-our  article named:  ``  Multiround Distributed  Lifetime Coverage  Optimization
-Protocol  in Wireless  Sensor Networks  ''.  We  highly appreciate  the detailed
-valuable comments  of the reviewers  on our  article. The suggestions  are quite
-helpful for us and  we incorporate them in the revised article.  We are happy to
-submit  to  you a  revised  version  that considers  most  of  your remarks  and
-suggestions to improve the quality of our article.
-
-As below, we  would like to clarify  some of the points raised  by the reviewers
+First  of all,  we would  like to  thank you  very much  for your  kind help  in
+improving  our  article  named:  ``  Multiround  Distributed  Lifetime  Coverage
+Optimization Protocol in Wireless Sensor  Networks ''.  We highly appreciate the
+detailed and valuable comments of the  reviewers on our article. The suggestions
+have  been very  helpful to  us and  we have  incorporated them  in the  revised
+article.  We are happy to submit to you a revised version that considers most of
+your remarks and suggestions to improve the quality of our article.
+
+Below, we  would like to clarify  some of the points raised  by the reviewers

 and we hope the reviewers and the  editors will be satisfied by our responses to
 the comments and the revision for the original manuscript.
 
 and we hope the reviewers and the  editors will be satisfied by our responses to
 the comments and the revision for the original manuscript.
 


@@ -76,7 +76,7 @@ discussed and analyzed, but also the clustering strategy.\\
 \textcolor{blue}{\textbf{\textsc{Answer:}  In  our  work,  we  assume  that  the
     sensors are deployed almost uniformly and with high density over the region.
     So we only need  to fix a regular division of the  region into subregions to
 \textcolor{blue}{\textbf{\textsc{Answer:}  In  our  work,  we  assume  that  the
     sensors are deployed almost uniformly and with high density over the region.
     So we only need  to fix a regular division of the  region into subregions to

-    make the problem tractable.  The subdivision is made such that the number of
+    make the problem tractable.  The subdivision has been made so that the number of

     hops between any pairs  of sensors inside a subregion is  less than or equal
     to~3.
 % In particular, we discuss the number of subregions in......
     hops between any pairs  of sensors inside a subregion is  less than or equal
     to~3.
 % In particular, we discuss the number of subregions in......


@@ -94,15 +94,15 @@ and then overcoverage is minimized would probably make more sense.\\
 \textcolor{blue}{\textbf{\textsc{Answer:} As mentioned in  the paper the integer
     program is based  on the model proposed  by F. Pedraza, A.  L. Medaglia, and
     A. Garcia  (``Efficient coverage algorithms for  wireless sensor networks'')
 \textcolor{blue}{\textbf{\textsc{Answer:} As mentioned in  the paper the integer
     program is based  on the model proposed  by F. Pedraza, A.  L. Medaglia, and
     A. Garcia  (``Efficient coverage algorithms for  wireless sensor networks'')

-    with some modifications.  Their initial approach consisted  in first finding
-    the maximum coverage obtainable using the  available sensors and then to use
+    with some modifications.  Their initial approach consisted  first in finding
+    the maximum coverage obtainable using the  available sensors and then in using

     this information as input to the problem of minimizing the overcoverage. But
     this two-steps approach  is time consuming. The originality of  the model is
     to solve  both objectives  in a parallel  fashion. Nevertheless  the weights
     $W_\theta$ and  $W_U$ must be  properly chosen so  as to guarantee  that the
     maximum number of points which are  covered during each round is maximum. By
     this information as input to the problem of minimizing the overcoverage. But
     this two-steps approach  is time consuming. The originality of  the model is
     to solve  both objectives  in a parallel  fashion. Nevertheless  the weights
     $W_\theta$ and  $W_U$ must be  properly chosen so  as to guarantee  that the
     maximum number of points which are  covered during each round is maximum. By

-    choosing  $W_{U}$ very  large compared  to $W_{\theta}$,  the coverage  of a
-    maximum of  primary points  is ensured.  Then for a  same number  of covered
+    choosing  $W_{U}$ much  larger than $W_{\theta}$,  the coverage  of a
+    maximum of  primary points  is ensured.  Then for the  same number  of covered

     primary points,  the solution  with a  minimal number  of active  sensors is
     preferred.  }}\\
 
     primary points,  the solution  with a  minimal number  of active  sensors is
     preferred.  }}\\
 


@@ -159,7 +159,7 @@ to improve the results of MuDiLCO-7 in particular.\\
     tree. Therefore we conducted new simulations  by applying your idea. That is
     to say we have stopped the resolution of the Branch-and-Bound method after a
     time threshold empirically defined and  we retain the best feasible solution
     tree. Therefore we conducted new simulations  by applying your idea. That is
     to say we have stopped the resolution of the Branch-and-Bound method after a
     time threshold empirically defined and  we retain the best feasible solution

-    found  by the  solver. This  approach  allows to  improve significantly  the
+    found  by the  solver. This  approach  allows to   significantly improve the

     results with multirounds, especially for MuDiLCO-7, as shown in the paper.}}
 
 \bigskip
     results with multirounds, especially for MuDiLCO-7, as shown in the paper.}}
 
 \bigskip


@@ -196,7 +196,7 @@ to improve the results of MuDiLCO-7 in particular.\\
     is  explicitly  explained in  the  second  paragraph of  section~3.2.   This
     assumption leads to an integer formulation of the optimization problem.  The
     decision  variables  are  binary  variables, $X_{t,j}$  for  the  activation
     is  explicitly  explained in  the  second  paragraph of  section~3.2.   This
     assumption leads to an integer formulation of the optimization problem.  The
     decision  variables  are  binary  variables, $X_{t,j}$  for  the  activation

-    ($X_{t,j}=1$) or not  ($X_{t,j}=0$) of the sensor $j$ during  the round $t$.
+    ($X_{t,j}=1$) or not  ($X_{t,j}=0$) of the sensor $j$ during  round $t$.

     Column  generation  based  approaches  can  be  applied  when  the  decision
     variables  of the  optimization problem  are  continuous. In  this case  the
     variables are  the time intervals  during which the  sensors of a  cover set
     Column  generation  based  approaches  can  be  applied  when  the  decision
     variables  of the  optimization problem  are  continuous. In  this case  the
     variables are  the time intervals  during which the  sensors of a  cover set


@@ -205,11 +205,11 @@ to improve the results of MuDiLCO-7 in particular.\\
     with the types of applications, with the amount of initial energy in sensors
     batteries,  and  also  with  the   duration  of  the  exchange  phase.   All
     applications do not  have the same Quality of Service  requirements.  In our
     with the types of applications, with the amount of initial energy in sensors
     batteries,  and  also  with  the   duration  of  the  exchange  phase.   All
     applications do not  have the same Quality of Service  requirements.  In our

-    case, information  exchange is executed  every hour,  but the length  of the
+    case, the information  exchange is executed  every hour,  but the length  of the

     sensing period could be reduced and  adapted dynamically. On the one hand, a
     small sensing period  would allow the network to be  more reliable but would
     have higher  communication costs.  On the  other hand, the choice  of a long
     sensing period could be reduced and  adapted dynamically. On the one hand, a
     small sensing period  would allow the network to be  more reliable but would
     have higher  communication costs.  On the  other hand, the choice  of a long

-    duration may  cause problems  in case  of nodes  failure during  the sensing
+    duration may  cause problems  in case  of node  failures during  the sensing

     period.}}\\
 
 \noindent  {\ding{90} Page  11 in  Table 1  $W_\Theta$ should  be replaced  with
     period.}}\\
 
 \noindent  {\ding{90} Page  11 in  Table 1  $W_\Theta$ should  be replaced  with