X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/JournalMultiPeriods.git/blobdiff_plain/b784ec6e8ac570882887b9c433290c93638129d6..refs/heads/master:/reponse2.tex?ds=inline

diff --git a/reponse2.tex b/reponse2.tex
index 56b6904..2294df2 100644
--- a/reponse2.tex
+++ b/reponse2.tex
@@ -30,8 +30,8 @@
 \begin{center}
 On the decision for the article
 
-``Multiround Distributed Lifetime Coverage Optimization \\
- Protocol in Wireless Sensor Networks''\\
+``Multiround Distributed Lifetime Coverage  Optimization \\ Protocol in Wireless
+Sensor Networks''\\
   
 by Ali Kadhum Idrees, Karine Deschinkel, Michel Salomon, and Raph\"ael Couturier
 
@@ -41,60 +41,142 @@ by Ali Kadhum Idrees, Karine Deschinkel, Michel Salomon, and Raph\"ael Couturier
 
 \noindent Dear Editor,
 
-We were disappointed by the rejection of our  article  named:  ``  Multiround  Distributed  Lifetime  Coverage Optimization Protocol in Wireless Sensor  Networks '' for publication in the AD HOC NETWORKS journal. Indeed, we've started the submission process since September 2014. We obtained the comments of only ONE reviewer during July 2015. Its suggestion were very  helpful and  we incorporated them  in the  revised article submitted in September 2015. In particular, 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, as it was suggested by the reviewer. We made our best to carefully address the issues raised by the referee and revise your paper accordingly.
-
-So, we  would like to clarify  some of the points raised  by the reviewer, since some of its remarks seem to be not relevant.\\
-
-\noindent {\bf 1.}  The authors have partially taken into account the comments of my previous review. Additional content has been added, but these additions are sometime confusing.\\
-
-\textcolor{blue}{\textbf{\textsc{Answer:} The reviewer does not clearly indicate which addition is confusing.}}\\
-
-\noindent {\bf 2.} The answer that the authors have provided to my comments should have been inserted into the paper, this is not always the case (i.e., my comment about the duration of the rounds).\\
-
-\textcolor{blue}{\textbf{\textsc{Answer:} We clearly indicated in section 3.2 that the rounds are of equal duration and we explained that this parameter should be set according to the types of application (see our answer in part "minor comments" in our previous answer). }}\\
-
-\noindent {\bf  3.} In Section 3.1, all nodes are assumed to be "homogeneous from the point of view of energy provision". Why is such an hypothesis necessary? This assumption is likely to be satisfied only when the WSN is deployed for the first time, with new sensors. But after using the network for the first time, the aforementioned hypothesis is not likely to be satisfied again.\\
-
-\textcolor{blue}{\textbf{\textsc{Answer:} Maybe the honourable reviewer misread the sentence in section 3.1. : "We  assume that all  nodes are
-{\bf homogeneous}  in   terms  of  communication  and   processing  capabilities,  and
-{\bf heterogeneous}  from the  point  of view  of  energy provision. }}\\
-
-\noindent {\bf 4.}  The last paragraph of Section 3.1 is very confusing. It seems that the author attempt to propose area coverage instead of target coverage, but instead of defining "primary points" inside the area to be covered, they define points inside the sensing range of the sensors, that are obviously covered when the corresponding sensor is active. There exists works in the literature for area coverage, the authors should read them.\\
-
-\textcolor{blue}{\textbf{\textsc{Answer :} This remark is rather offensive. Of course we read literature for area coverage. We use the metric "Coverage Ratio" (defined in section 4.3.) to measure how much the area is covered. But the optimization process to decide which sensor has to be active or not in each round is based on the coverage of only a specified set of points called primary points. So the area coverage problem is transformed to the target coverage problem.}}\\
-
-\noindent {\bf  5.} In Section 3.2, the reason why a subregion is defined in such a way that the distance between any two sensors in the same subregion is less than 3 hops is not justified. The reader is not told if the proposed protocol works better when the number of subregions is low or high. No algorithm for defining the subregions is given. It pertains to a clustering problem, for which a large number of algorithms exists, but without a sound definition of what is a 'good' partitioning for the proposed protocol, the reader cannot select a clustering algorithm. \\
-
-%Pas de reponse
-\textcolor{blue}{\textbf{\textsc{Answer :} ???????????? }}
-
-\bigskip
-\noindent {\bf  6.} The ILP of Section 3.5 aims at addressing a bi-objective problem. Since full coverage is required, the ILP should first ensure total coverage, and then minimize overcoverage. The current ILP is not a proper formulation for reaching this objective, as undercoverage can be compensated by overcoverage. The primary and secondary objective are then mixed up into a single objective function with no interpretable meaning. Of course, an objective function value of 100 is better than an objective value of 101, but one cannot tell if a gap of 1 makes a major difference or not. The objectives should be distinguished, and the problem should be addressed as a multiobjective optimization problem.\\
-
-\textcolor{blue}{\textbf{\textsc{Answer :} As mentioned in the paper,
-    the ILP of section 3.5 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. The
-    originality of the model is to solve both objectives in a parallel
-    fashion: maximizing the coverage and minimizing the
-    overcoverage. Nevertheless the weights $w_\theta$ and $w_U$ must
-    be properly chosen so as to guarantee that the  number of points
-    which are covered during each round is maximum. By 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. It has been formally proven in the paper mentioned
-    above that this guarantee is satisfied for a  constant weighting $w_{U}$ greater than $\left|P\right|$ (when $w_{\theta}$ is fixed to 1).}}
+After a careful reading of your  last decision letter, we are quite disappointed
+by  the  rejection  of  our article  named:  ``Multiround  Distributed  Lifetime
+Coverage  Optimization Protocol  in  Wireless Sensor  Networks'', submitted  for
+publication  in  the AD  HOC  NETWORKS  journal.  Indeed,  we have  started  the
+submission  process in  September 2014  and obtained  the comments  of only  ONE
+reviewer in July 2015. Its suggestion  were very helpful and we incorporated
+them in the revised article submitted in September 2015.
+
+In particular, we have stopped the resolution of the Branch-and-Bound
+method after a time threshold empirically defined and we have retained
+the best feasible solution found by the solver, as it was suggested by
+the reviewer.  We made our best to carefully address the issues raised
+by the referee and revised our paper accordingly.  So we would like to
+clarify some of the points raised by the reviewer, since some them
+seem to be irrelevant or unfair.
+
+\noindent {\bf 1.}   The authors have partially taken into  account the comments
+of my  previous review. Additional content  has been added, but  these additions
+are sometime confusing.\\
+
+\textcolor{blue}{\textbf{\textsc{Answer:} The reviewer does not clearly indicate
+    which addition is confusing.}}\\
+
+\noindent {\bf  2.} The  answer that  the authors have  provided to  my comments
+should have been inserted into the paper,  this is not always the case (i.e., my
+comment about the duration of the rounds).\\
+
+\textcolor{blue}{\textbf{\textsc{Answer:}  We clearly  indicated in  Section~3.2
+    that the rounds  are of equal duration and we  explained that this parameter
+    should be set according to the types  of application (see our
+    answer in the part
+    ``minor comments'' in our previous answer).}}\\
+
+\noindent {\bf  3.} In Section~3.1,  all nodes  are assumed to  be ``homogeneous
+from  the  point of  view  of  energy provision''.  Why  is  such an  hypothesis
+necessary?   This assumption  is likely  to be  satisfied only  when the  WSN is
+deployed for the first  time, with new sensors. But after  using the network for
+the first  time, the  aforementioned hypothesis  is not  likely to  be satisfied
+again.\\
+
+\textcolor{blue}{\textbf{\textsc{Answer:} The reviewer might have misread
+    the  sentence  in   Section~3.1:  ``We  assume  that  all   nodes  are  {\bf
+      homogeneous} in  terms of  communication and processing  capabilities, and
+    {\bf heterogeneous} from the point of view of energy provision.'' }}\\
+
+\noindent {\bf  4.}  The  last paragraph  of Section 3.1  is very  confusing. It
+seems  that the  author  attempt  to propose  area  coverage  instead of  target
+coverage,  but instead  of defining  ``primary points''  inside the  area to  be
+covered, they  define points inside the  sensing range of the  sensors, that are
+obviously covered when the corresponding sensor is active. There exists works in
+the literature for area coverage, the authors should read them.\\
+
+\textcolor{blue}{\textbf{\textsc{Answer:}  This remark is rather  offensive. Of
+    course we have read  the literature for area coverage.  We have used  the metric ``Coverage
+    Ratio'' (defined  in Section~4.3) to measure  how much of the area  is covered.
+    But the optimization process to decide which  sensor has to be active or not
+    in each  round is based on  the coverage of  only a specified set  of points
+    called primary points. So the area  coverage problem is transformed into the
+    target coverage problem. So from our point of view, this remark is
+    not justified.}}\\
+
+\noindent {\bf 5.} In Section 3.2, the reason why a subregion is defined in such
+a way that  the distance between any  two sensors in the same  subregion is less
+than 3 hops  is not justified. The  reader is not told if  the proposed protocol
+works better  when the  number of subregions  is low or  high. No  algorithm for
+defining the subregions is given. It pertains to a clustering problem, for which
+a large number of algorithms exists, but without a sound definition of what is a
+'good'  partitioning for  the  proposed  protocol, the  reader  cannot select  a
+clustering algorithm. \\
+
+\textcolor{blue}{\textbf{\textsc{Answer:}   The  choice   of  the   number  of
+    subregions  is  discussed  in  the   last  paragraph  of  Section~4.2.  More
+    particularly, we explain that this parameter should be chosen by taking into
+    account  the  trade-off between  the  benefit  of the  optimization  problem
+    induced by the number of sensors in a subregion and the time needed to solve
+    it.  As said  at  the beginning  of  Section~3.2, the  area  of interest  is
+    ``divided   into    regular   homogeneous   subregions   using    a   simple
+    divide-and-conquer algorithm.''}}\\
+
+\noindent {\bf  6.} The  ILP of  Section 3.5 aims  at addressing  a bi-objective
+problem. Since  full coverage  is required,  the ILP  should first  ensure total
+coverage,  and then  minimize  overcoverage. The  current ILP  is  not a  proper
+formulation for reaching this objective,  as undercoverage can be compensated by
+overcoverage.  The primary  and secondary  objective are  then mixed  up into  a
+single objective function with no interpretable meaning. Of course, an objective
+function value of 100  is better than an objective value of  101, but one cannot
+tell if  a gap of 1  makes a major difference  or not. The objectives  should be
+distinguished,  and  the  problem  should   be  addressed  as  a  multiobjective
+optimization problem.\\
+
+\textcolor{blue}{\textbf{\textsc{Answer:} As mentioned in the paper, the ILP of
+    Section~3.5 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. The  originality of  the model  is to
+    solve both  objectives in  a parallel fashion:  maximizing the  coverage and
+    minimizing the  overcoverage. Nevertheless the weights  $w_\theta$ and $w_U$
+    must be properly chosen  so as to guarantee that the  number of points which
+    are covered  during each round is  maximum. By 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.  It has been formally
+    proven in the  paper mentioned above that this guarantee  is satisfied for a
+    constant weighting $w_{U}$ greater  than $\left|P\right|$ (when $w_{\theta}$
+    is fixed to 1).}}
 
 \bigskip
-\noindent {\bf  6.} The content of Section 4.3, where different metrics are proposed to assess the solution quality, is a sign of a ill formulated problem: how to comment on the performance of an algorithm on a criterion (say network lifetime) if the ILP does not take this objective into account? The performances with these additional objectives are likely to be related to the ILP solver used: many optimal solutions to the ILP of Section 3 may have a very different impact in terms of these additional metrics. Hence, measuring them is pointless.\\
-
-\textcolor{blue}{\textbf{\textsc{Answer :} We disagree this remark. It is quite possible to optimize a criterion to have an impact on another one. In the problem formulation proposed here, the number of active sensors is minimized in each round for a maximal level of coverage. Limiting the activation time of each sensor has a direct impact on its lifetime and consequently on the network lifetime as shown in our experimental results. For example, such an idea is used in the models developed for   brachytherapy  treatment   planning  to improve the quality of a dose   distribution ("Comparison of inverse planning simulated annealing and geometrical optimization for prostate high-dose-rate brachytherapy", I-Chow J. Hsu1, E. Lessard, V. Weinberg, J. Pouliot, {\it Brachytherapy} Volume 3, Issue 3, 2004, Pages 147-152): the objective in the problem formulation is to minimize a weighted sum of the differences between prescribed doses and obtained doses in reference points, whereas many criterion (like dose-volume histograms, conformal index COIN) are used for quantitative evaluation of dose plans. }}\\
+\noindent  {\bf 6.}  The content  of Section  4.3, where  different metrics  are
+proposed to assess the solution quality, is a sign of an ill formulated problem:
+how to comment  on the performance of  an algorithm on a  criterion (say network
+lifetime) if the ILP does not take this objective into account? The performances
+with these  additional objectives  are likely  to be related  to the  ILP solver
+used: many optimal solutions  to the ILP of Section 3 may  have a very different
+impact  in  terms  of  these   additional  metrics.  Hence,  measuring  them  is
+pointless.\\
+
+\textcolor{blue}{\textbf{\textsc{Answer:} We disagree with this remark. It is quite
+    possible to optimize  a criterion to have  an impact on another  one. In the
+    problem formulation proposed here, the number of active sensors is minimized
+    in each round for a maximal  level of coverage. Limiting the activation time
+    of each sensor has  a direct impact on its lifetime  and consequently on the
+    network lifetime, as shown in our experimental results. For example, such an
+    idea is used in the models developed for brachytherapy treatment planning to
+    improve the quality of a  dose distribution ("Comparison of inverse planning
+    simulated annealing and geometrical optimization for prostate high-dose-rate
+    brachytherapy", I-Chow J. Hsu1, E.   Lessard, V. Weinberg, J.  Pouliot, {\it
+      Brachytherapy} Volume 3,  Issue 3, 2004, Pages 147-152):  the objective in
+    the problem  formulation is to  minimize a  weighted sum of  the differences
+    between prescribed  doses and  obtained doses  in reference  points, whereas
+    many criteria (like dose-volume histograms,  conformal index COIN) are used
+    for quantitative evaluation of dose plans.}}\\
 %si vous avez d'autres exemples plus parlants?
 
-
-We hope that these observations allow you to revise your decision concerning our manuscript. If possible, we would like to have comments of an additional reviewer.
-
+We  hope  that  these  observations  will allow  you  to  revise  your  decision
+concerning our  manuscript. If possible,  we would like  to benefit
+from the comments  of an
+additional reviewer.
 
 \begin{flushright}
 
