set of points called primary points~\cite{idrees2014coverage}. We assume that
the sensing disk defined by a sensor is covered if all the primary points of
this sensor are covered. By knowing the position of wireless sensor node
-(centered at the the position $\left(p_x,p_y\right)$) and it's sensing range
+(centered at the the position $\left(p_x,p_y\right)$) and its sensing range
$R_s$, we define up to 25 primary points $X_1$ to $X_{25}$ as decribed on
Figure~\ref{fig1}. The optimal number of primary points is investigated in
section~\ref{ch4:sec:04:06}.
the area of interest for a larger number of rounds. It also means that MuDiLCO
saves more energy, with less dead nodes, at most for several rounds, and thus
should extend the network lifetime. \textcolor{blue}{MuDiLCO-7 seems to have
- most of the time the best coverage ratio up to round~80, after MuDiLCO-5 is
+ most of the time the best coverage ratio up to round~80, after that MuDiLCO-5 is
slightly better.}
\begin{figure}[ht!]
\textcolor{blue}{Energy consumption increases with the size of the networks and
the number of rounds. The curve Unlimited-MuDiLCO-7 shows that energy
- consumption due to the time spent to solve the integer program to optimality
+ consumption due to the time spent to optimally solve the integer program
increases drastically with the size of the network. When the resolution time
is limited for large network sizes, the energy consumption remains of the same
order whatever the MuDiLCO version. As can be seen with MuDiLCO-7.}
rather than limiting the execution time, similar results might be obtained by
replacing the computation of the exact solution with the finding of a
suboptimal one using a heuristic approach. For our simulation setup and
- considering the different metrics, MuDiLCO-5 seems to be the most suited
- method in comparison with MuDiLCO-7.}
+ considering the different metrics, MuDiLCO-5 seems to be the best suited
+ method compared to MuDiLCO-7.}
\begin{figure}[t!]
\centering
