X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/JournalMultiPeriods.git/blobdiff_plain/876bc669165cfe7f2667787b7f58b59b77173cb5..acf040f3efe85387361514b1973192ad14dc0e1b:/reponse.tex?ds=inline

diff --git a/reponse.tex b/reponse.tex
index 4c48b6f..f9381c8 100644
--- a/reponse.tex
+++ b/reponse.tex
@@ -97,7 +97,8 @@ two-phase approach where breach is minimized first, 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 () with some modifications. Their initial approach consisted in first finding the maximum coverage obtainable from available sensors to then use 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      }}\\
+\textcolor{blue}{\textbf{\textsc{Answer:} As mentioned in the paper the integer program is based on the model proposed by F. Pedraza and 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 from available sensors to then use 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 are covered during each round. 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  points, solution with a minimal number of active sensors is preferred.  }}\\
 
 
 
@@ -108,7 +109,8 @@ the WSN. The authors are referred to "alpha-coverage to extend network lifetime
 on wireless sensor networks", Optim. Lett. 7, No. 1, 157-172 (2013) by Gentilli
 et al to enforce a constraint on the minimum coverage of each point.  \\
 
-\textcolor{blue}{\textbf{\textsc{Answer:}       }}\\
+\textcolor{blue}{\textbf{\textsc{Answer:} As previously explained, the model with the appropriate weights ensures that a maximum number of points are covered for the set of still alive sensors. The coverage is measured through the performance metrics "coverage ratio". The coverage ratio remains around 100\% as long as possible (as long as there are enough alive sensors to cover all primary points) and then decreases. The problem introduced in "alpha-coverage to extend network lifetime
+on wireless sensor networks" by Gentilli is quite different. In this problem, the coverage ratio is fixed to a predetermined value ($\alpha$) and the amount of time during which the network can satisfy a target coverage greater than $\alpha$ is maximized. }}\\
 
 
 \noindent {\bf 4.}  Page 13
@@ -118,7 +120,7 @@ Computing the average energy consumption per unit of time over the network
 lifetime would be better, as it is independent from the number and duration of
 rounds.  \\
 
-\textcolor{blue}{\textbf{\textsc{Answer :}        }}
+\textcolor{blue}{\textbf{\textsc{Answer :} Yes, you  are  right. It is possible to obtain the average energy consumption per unit of time by dividing the criterion defined in section .... by the round duration.}}
 
 
 
@@ -129,9 +131,10 @@ Alternatively, the authors may remove some versions of MuDiLCO if they do not
 bring any valuable insight. \\
 
 
-\textcolor{blue}{\textbf{\textsc{Answer :}        }}
+\textcolor{blue}{\textbf{\textsc{Answer :} Right. We have completely re-written section 5 to highlight the most significant results.       }}
 
 
+\bigskip
 \noindent {\bf 6.}   Page 19 (most major point)
 The authors state that solving the Mixed Integer Linear Program is
 time-consuming, and use this point for explaining why MuDiLCO-7 is not as
@@ -143,7 +146,8 @@ is very likely to save time and energy, and to improve the results of MuDiLCO-7
 in particular.  \\
 
 
-\textcolor{blue}{\textbf{\textsc{Answer :}        }}
+\textcolor{blue}{\textbf{\textsc{Answer :}  Your  remark is very interesting. We have observed that the optimal solution of the integer program is often obtained after the exploration of very few nodes of the Branch-and-Bound-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 results with multirounds, especially for MuDiLCO-7.}}
 
 
 \bigskip
@@ -160,7 +164,7 @@ follows" should mention the content of Section 5.    }  \\
 The sentence "the centralized approaches usually suffer from the scalability
 problem, making them less competitive as the network size increase" should
 probably be tempered: heuristics and metaheuristics can handle very large and
-centralized problems (even if exact approaches can't), and these approaches are
+centralized problems (even if exact approaches can't), and these approaches areempirically
 very popular in WSN.     } \\
 
 \textcolor{blue}{\textbf{\textsc{Answer:} Right, fixed    }}\\
@@ -170,7 +174,7 @@ very popular in WSN.     } \\
 study not presented here". The authors should provide at least one reference
 about the aforementioned study.      }  \\
 
-\textcolor{blue}{\textbf{\textsc{Answer:}          }}\\
+\textcolor{blue}{\textbf{\textsc{Answer:} Right. We have included a section (section ...) dedicated to the choice and the number of primary points.         }}\\
 
 \noindent {\ding{90}     Page 6, Figure 1:
 All rounds seem to have the same duration. This should be stated explicitly, and