low processing power from the sensor nodes, which usually have limited
processing capabilities. The main drawback of this kind of approach is its
higher cost in communications, since the node that will make the decision needs
-information from all the sensor nodes. Moreover, centralized approaches usually
+information from all the sensor nodes. \textcolor{red} {Exact or heuristics approaches are designed to provide cover sets. (Moreover, centralized approaches usually
suffer from the scalability problem, making them less competitive as the network
-size increases.
+size increases.) Contrary to exact methods, heuristic methods can handle very large and centralized problems. They are proposed to reduce computational overhead such as energy consumption, delay and generally increase in
+the network lifetime. }
The first algorithms proposed in the literature consider that the cover sets are
disjoint: a sensor node appears in exactly one of the generated cover
As can be seen in Figure~\ref{fig2}, our protocol works in periods fashion,
where each is divided into 4 phases: Information~Exchange, Leader~Election,
Decision, and Sensing. Each sensing phase may be itself divided into $T$ rounds
-and for each round a set of sensors (a cover set) is responsible for the sensing
+\textcolor{green} {of equal duration} and for each round a set of sensors (a cover set) is responsible for the sensing
task. In this way a multiround optimization process is performed during each
period after Information~Exchange and Leader~Election phases, in order to
produce $T$ cover sets that will take the mission of sensing for $T$ rounds.
decision, the node will not participate to this phase, and, on the other hand,
if the node failure occurs after the decision, the sensing task of the network
will be temporarily affected: only during the period of sensing until a new
-period starts.
+period starts. \textcolor{green}{The duration of the period and the duration of the rounds are predefined parameters. Round duration should be long enough to hide the system control overhead and short enough to minimize the negative effects in case of node failure.}
+
%%RC so if there are at least one failure per period, the coverage is bad...
%%MS if we want to be reliable against many node failures we need to have an
%% overcoverage...
the size of the subregion should be discussed and analyzed, but also the
clustering strategy.\\
-\textcolor{blue}{\textbf{\textsc{Answer:} }}\\
+\textcolor{blue}{\textbf{\textsc{Answer:} In the study, we assume that
+ the deployment of sensors is almost uniform 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 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
minimized would probably make more sense.
\\
-\textcolor{blue}{\textbf{\textsc{Answer:} }}\\
+\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 }}\\
centralized problems (even if exact approaches can't), and these approaches are
very popular in WSN. } \\
-\textcolor{blue}{\textbf{\textsc{Answer:} }}\\
+\textcolor{blue}{\textbf{\textsc{Answer:} Right, fixed }}\\
\noindent {\ding{90} Page 5
"The choice of number and locations of primary points is the subject of another
justified (in column generation based approaches, "rounds" to not have the same
duration). } \\
-\textcolor{blue}{\textbf{\textsc{Answer:} }}\\
+\textcolor{blue}{\textbf{\textsc{Answer:} All rounds have the same duration. It is explicitly explained
+ in paragraph ... in section .... 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 $t$. 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 (not necessarily disjoint) are active. The time intervals are not equal. Concerning the choice of round duration of equal length, 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, 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
+ duration may cause problems in case of nodes failure during the sensing
+ period. }}\\
\noindent {\ding{90} Page 11 in Table 1
$W_\Theta$ should be replaced with $W_\theta$
