+\noindent\textcolor{black}{\textbf{MAJOR COMMENTS:}}\\
+
+\noindent {\bf 1.} Page 6, Section 3.2 The author didn't explain how subregions
+are created. This is an important point, as clustering may have a significant
+impact on solution quality. Not only the size of the subregion should be
+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
+ 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......
+}}\\
+
+\noindent {\bf 2.} Page 8 The objective function (5) of the Mixed Integer Linear
+Program appears to be very questionable. Indeed overcoverage and undercoverage
+may compensate each other, so the same objective value may represent two
+incomparable situations. It seems that the semantic of the objective function is
+not well defined, as one may wonder what exactly is (quantitatively speaking)
+the problem objective. Coverage breach is obviously an issue in WSN, but why
+penalizing overcoverage? A 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 F. Pedraza, A. L. Medaglia, and
+ A. Garcia (``Efficient coverage algorithms for wireless sensor networks'')
+ 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
+ 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. }}\\
+
+\noindent {\bf 3.} Page 9 In the MILP formulation, it is possible that some
+point p is never covered at all, which means that some part of the area to
+monitor may never be monitored by 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:} As previously explained, the model
+ with the appropriate weights ensures that a maximum number of points are
+ covered by the set of still alive sensors. The coverage is measured through
+ the performance metrics ``coverage ratio''. This one 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 The criterion ``Energy Consumption'' is the average
+consumption per round. But the duration of a round is a feature that can be
+arbitrarily set in the algorithm. 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 :} Yes, you are right. It is possible to
+ obtain the average energy consumption per unit of time by dividing the
+ criterion defined in section~4.3 by the round duration.}}
+
+\noindent {\bf 5.} Page 15-18 Figures 2-6 mention four different versions of
+MuDiLCO. The performance of these different versions should be analyzed with
+more details for each figure. Alternatively, the authors may remove some
+versions of MuDiLCO if they do not bring any valuable insight. \\
+
+\textcolor{blue}{\textbf{\textsc{Answer :} Right. Therefore we have completely
+ re-written section 5 to highlight the most significant results.}}
