X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/JournalMultiPeriods.git/blobdiff_plain/8426ef678e715c447cdc24fca4221a005fb01ef9..46e6f7cd7d85049c4f767edfe1091ef287cdc1e0:/article.tex?ds=inline

diff --git a/article.tex b/article.tex
index fa3d153..b37791f 100644
--- a/article.tex
+++ b/article.tex
@@ -805,7 +805,7 @@ Subject to
 \end{equation}
 
 \begin{equation}
-  \sum_{t=1}^{T}  X_{t,j}   \leq  \floor*{RE_{j}/E_{R}} \hspace{6 mm} \forall j \in J, t = 1,\dots,T
+  \sum_{t=1}^{T}  X_{t,j}   \leq  \floor*{RE_{j}/E_{R}} \hspace{10 mm}\forall j \in J\hspace{6 mm} 
   \label{eq144} 
 \end{equation}
 
@@ -853,6 +853,8 @@ to guarantee that the maximum number of points are covered during each round.
 %% MS W_theta is smaller than W_u => problem with the following sentence
 In our simulations priority is given  to the coverage by choosing $W_{U}$ very
 large compared to $W_{\theta}$.
+
+\textcolor{green}{The size of the problem depends on the number of variables and constraints. The number of variables is linked to the number of alive sensors $A \subset J$, the number of rounds $T$, and the number of primary points $P$. Thus the integer program contains $A*T$ variables of type $X_{t,j}$, $P*T$ overcoverage variables and $P*T$ undercoverage variables. The number of constraints is equal to $P*T$ (for constraints (\ref{eq16})) $+$ $A$ (for constraints (\ref{eq144})).}
 %The Active-Sleep packet includes the schedule vector with the number of rounds that should be applied by the receiving sensor node during the sensing phase.
 
 
@@ -1133,7 +1135,7 @@ $W_{U}$ & $|P|^2$ \\
 \label{table3}
 % is used to refer this table in the text
 \end{table}
-  
+
 \textcolor{green}{The MuDilLCO protocol is declined into  four versions: MuDiLCO-1,  MuDiLCO-3, MuDiLCO-5,
 and  MuDiLCO-7, corresponding  respectively to  $T=1,3,5,7$ ($T$  the  number of rounds in one sensing period). Since the time resolution may be prohibitif when the size of the problem increases, a time limit treshold  has been fixed to solve large instances.  In these cases, the solver returns the best solution found, which is not necessary the optimal solution.
  Table \ref{tl} shows time limit values. These time limit treshold have been set empirically. The basic idea consists in considering the average execution time to solve the integer programs  to optimality, then by dividing  this average time by three to set the threshold value. After that, this treshold value is increased if necessary such that the solver is able to deliver a feasible solution within the time limit. In fact, selecting the optimal values for the time limits will be investigated in future. In Table \ref{tl}, "NO" indicates that the problem has been solved to optimality without time limit. }. 
@@ -1151,9 +1153,9 @@ and  MuDiLCO-7, corresponding  respectively to  $T=1,3,5,7$ ($T$  the  number of
 \hline
 150 & NO & NO & NO & 0.03 \\
 \hline
-200 & NO & 0.01 & 0.02 & 0.06 \\
+200 & NO & NO & NO & 0.06 \\
  \hline
- 250 & NO & 0.02 & 0.03 & 0.08 \\
+ 250 & NO & NO & NO & 0.08 \\
  \hline
 \end{tabular}