X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/LiCO.git/blobdiff_plain/27b71d0f349b075890c36c261dabbf78ef2f26d3..f728a52ec5b5d3a0b90f03b90777f454b88b21be:/PeCO-EO/reponse.tex?ds=sidebyside

diff --git a/PeCO-EO/reponse.tex b/PeCO-EO/reponse.tex
index ce66393..1fb3f07 100644
--- a/PeCO-EO/reponse.tex
+++ b/PeCO-EO/reponse.tex
@@ -73,7 +73,7 @@ methodology uses existing methods and the original contribution lies only in the
 application of these methods for the coverage scheduling problem.\\
 
 \textcolor{blue}{\textbf{\textsc{Answer:}  To  the  best of  our  knowledge,  no
-    integer  linear programming  based on  perimeter coverage  has been  already
+    integer  linear programming  based on  perimeter coverage  has ever been  
     proposed in the literature.  As specified in the paper, in  Section 4, it is
     inspired from a model developed for brachytherapy  treatment planning for
     optimizing dose distribution. In this  model the deviation between an actual
@@ -86,9 +86,9 @@ application of these methods for the coverage scheduling problem.\\
 assumption made on the selection criteria for the leader seems too vague.  \\
 
 \textcolor{blue}{\textbf{\textsc{Answer:} The selection  criteria for the leader
-    inside each subregion  is explained in page~9, at  the end of Section~3.3
-    After information  exchange among  the sensor nodes  in the  subregion, each
-    node will have all the information needed to decide if it will the leader or
+    inside each subregion  is explained page~9, at  the end of Section~3.3
+    After the information  exchange among  the sensor nodes  in the  subregion, each
+    node will have all the information needed to decide if it will be the leader or
     not. The decision is based on selecting  the sensor node that has the larger
     number of one-hop neighbors. If this value is the same for many sensors, the
     node that has the largest remaining energy will be selected as a leader.  If
@@ -122,7 +122,7 @@ optimization problem.\\
     coverage interval  $i$. If the sensor  $j$ is undercovered, there  exists at
     least  one of  its CI  (say  $i$) for  which  the number  of active  sensors
     (denoted by  $l^{i}$) covering this part  of the perimeter is  less than $l$
-    and in this  case : $M_{i}^{j}=l-l^{i}$, $V_{i}^{j}=0$. In  the contrary, if
+    and in this  case : $M_{i}^{j}=l-l^{i}$, $V_{i}^{j}=0$. On  the contrary, if
     the sensor $j$ is overcovered, there exists at least one of its CI (say $i$)
     for which  the number of active  sensors (denoted by $l^{i}$)  covering this
     part of the perimeter is greater than  $l$ and in this case : $M_{i}^{j}=0$,
@@ -139,9 +139,9 @@ results showing how the algorithm performs with different alphas and betas.\\
     for alpha  and beta.   Table 4 presents  the results obtained  for a  WSN of
     200~sensor nodes.  It explains the  value chosen for the simulation settings
     in Table~2. \\ \indent The choice of alpha and beta should be made according
-    to the  needs of the  application. Alpha should  be enough large  to prevent
-    undercoverage and  so to  reach the highest  possible coverage  ratio.  Beta
-    should be enough large to prevent  overcoverage and so to activate a minimum
+    to the  needs of the  application. Alpha should  be large enough  to prevent
+    undercoverage and  thus to  reach the highest  possible coverage  ratio.  Beta
+    should be enough large to prevent  overcoverage and thus to activate a minimum
     number of sensors.  The values of  $\alpha_{i}^{j}$ can be identical for all
     coverage  intervals $i$  of one  sensor  $j$ in  order to  express that  the
     perimeter of each  sensor should be uniformly  covered, but $\alpha_{i}^{j}$
@@ -151,9 +151,9 @@ results showing how the algorithm performs with different alphas and betas.\\
     as $\alpha$  is low,  some areas  may be poorly  covered. This  explains the
     results obtained for $Lifetime_{50}$ with $\beta \gg \alpha$: a large number
     of periods with  low coverage ratio.  With $\alpha \gg  \beta$, we favor the
-    coverage even  if some areas may  be overcovered, so high  coverage ratio is
+    coverage even  if some areas may  be overcovered, so a high  coverage ratio is
     reached, but a  large number of sensors are activated  to achieve this goal.
-    Therefore  network  lifetime  is   reduced.   The  choice  $\alpha=0.6$  and
+    Therefore  the network  lifetime  is   reduced.   The  choice  $\alpha=0.6$  and
     $\beta=0.4$  seems  to achieve  the  best  compromise between  lifetime  and
     coverage ratio.}}\\
 
@@ -169,7 +169,7 @@ suggest using the journals template to adjust them for overall consistency.\\
     WSN. Each  paragraph of this Section  discusses the literature related  to a
     particular aspect of the problem : 1. types of coverage, 2. types of scheme,
     3. centralized versus distributed protocols,  4. optimization method. At the
-    end of each paragraph we position our approach.}}\\
+    end of each paragraph we position our approach. We have also added a last paragraph about our previous work on DilCO protocol to explain the difference with PeCO. }}\\
 
 \noindent {\bf 7.} The methodology is implemented in OMNeT++ (network simulator)
 and tested  against 2 existing algorithms  and a previously developed  method by
@@ -191,11 +191,11 @@ network to increase  its lifetime but does not improve  the coverage ratio. This
 may be an  issue if this approach  is used in an application  that requires high
 coverage ratio. \\
 
-\textcolor{blue}{\textbf{\textsc{Answer:} Your  remark is  interesting.  Indeed,
-    Figures 8(a) and (b) highlight this  result. PeCO protocol allows to achieve
+\textcolor{blue}{\textbf{\textsc{Answer:} Your  remark is very interesting.  Indeed,
+    Figures 8(a) and (b) highlight this  result. The PeCO protocol allows to achieve
     a coverage  ratio greater than $50\%$  for far more periods  than the others
     three  methods, but  for applications  requiring  a high  level of  coverage
-    (greater than  $95\%$), DiLCO method is  more efficient. It is  explained at
+    (greater than  $95\%$), the DiLCO method is  more efficient. It is  explained at
     the end of Section 5.2.4.}}\\
 
 %%%%%%%%%%%%%%%%%%%%%%  ENGLISH and GRAMMAR %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@@ -280,11 +280,11 @@ how should this common duration should be chosen?\\
     to~3. Concerning the choice of the sensing period duration, it is correlated
     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
+    do  not  have  the  same  Quality of  Service  requirements.   In  our  case,
     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  to  be more  reliable  but  would have  higher
-    communication costs.   On the other hand  the choice of a  long duration may
+    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 period.
     Several  explanations on  these points  are given  throughout the  paper. In
     particular,  we discuss  the number  of subregions  in Section  5.2 and  the
@@ -320,8 +320,7 @@ and explain how the protocol is built to optimize these objectives.  \\
 
 \textcolor{blue}{\textbf{\textsc{Answer:}  Right.   The   mixed  Integer  Linear
     Program adresses  a multiobjective  problem, where the  goal is  to minimize
-    overcoverage and  undercoverage for each  coverage interval of a  sensor. As
-    far as  we know, representing  the objective function  as a weighted  sum of
+    overcoverage and  undercoverage for each  coverage interval of a  sensor. To the best of our knowledge, representing  the objective function  as a weighted  sum of
     criteria  to  be  minimized  in  case of  multicriteria  optimization  is  a
     classical method.   In Section 5, the  comparison of protocols with  a large
     variety of performance metrics allows  to select the most appropriate method
@@ -387,7 +386,7 @@ of nodes &&&&relaxation &B\&B tree &\\
 \medskip  \\
 It  is noteworthy  that the  difference  of memory  used with  GLPK between  the
 resolution of  the IP  and its  LP-relaxation is  very weak  (not more  than 0.1
-MB). The size of  the branch and bound tree dos not exceed  3 nodes. This result
+MB). The size of  the branch and bound tree does not exceed  3 nodes. This result
 leads one to  believe that the memory use with  CPLEX\textregistered for solving
 the IP would be very close to that  for the LP-relaxation, that is to say around
 100 Kb for a subregion containing $S=10$ sensors.  Moreover the IP seems to have
@@ -399,7 +398,7 @@ Optimization, issn 1572-5286).
 \item  the subdivision  of the  region of  interest. To  make the  resolution of
   integer programming tractable by a leader  sensor, we need to limit the number
   of nodes  in each subregion  (the number of  variables and constraints  of the
-  integer  programming  is  directly  depending  on  the  number  of  nodes  and
+  integer  programming  directly  depends  on  the  number  of  nodes  and
   neigbors). It is therefore necessary to  adapt the subdvision according to the
   number of sensors deployed in the area  and their sensing range (impact on the
   number of coverage intervals).
@@ -487,8 +486,8 @@ A discussion about memory consumption has been added in Section 5.2}}
 
 \textcolor{blue}{\textbf{\textsc{Answer:} For minimizing the objective function,
     $M_{i}^{j}$ and  $V_{i}^{j}$ should  be set to  the smallest  possible value
-    such that  the inequalities are satisfied.  It is explained in  the answer 4
-    for  the reviewer  1.  But,  at optimality,  constraints  are not  necessary
+    such that  the inequalities are satisfied.  It is explained in  answer 4
+    for reviewer  1.  But,  at optimality,  constraints  are not  necessary
     satisfied with equality. For instance, if a sensor $j$ is overcovered, there
     exists at least one of its coverage  interval (say $i$) for which the number
     of active sensors  (denoted by $l^{i}$) covering this part  of the perimeter
