%more interesting to divide the area into several subregions, given the
%computation complexity.
-\textcolor{blue}{ Compared to our previous work~\cite{idrees2015distributed},
+\textcolor{black}{ Compared to our previous work~\cite{idrees2015distributed},
in this paper we study the possibility of dividing the sensing phase into
multiple rounds. We make a multiround optimization,
while previously it was a single round optimization. The idea is to
\subsection{Assumptions and primary points}
\label{pp}
-\textcolor{blue}{The assumptions and the coverage model are identical to those presented
+\textcolor{black}{The assumptions and the coverage model are identical to those presented
in~\cite{idrees2015distributed}. We consider a scenario in which sensors are deployed in high
density to initially ensure a high coverage ratio of the interested area. Each
sensor has a predefined sensing range $R_s$, an initial energy supply
As can be seen in Figure~\ref{fig2}, our protocol works in periods fashion,
where each period is divided into 4~phases: Information~Exchange,
-Leader~Election, Decision, and Sensing. \textcolor{blue}{Compared to
+Leader~Election, Decision, and Sensing. \textcolor{black}{Compared to
the DiLCO protocol described in~\cite{idrees2015distributed},} each sensing phase is itself
divided into $T$ rounds 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. \textcolor{blue}{Algorithm~\ref{alg:MuDiLCO} is executed by each sensor
+rounds. \textcolor{black}{Algorithm~\ref{alg:MuDiLCO} is executed by each sensor
node~$s_j$ (with enough remaining energy) at the beginning of a period.}
\begin{figure}[t!]
\centering \includegraphics[width=125mm]{Modelgeneral.pdf} % 70mm
\label{alg:MuDiLCO}
\end{algorithm}
-\textcolor{blue}{As already described in~\cite{idrees2015distributed}}, two
+\textcolor{black}{As already described in~\cite{idrees2015distributed}}, two
types of packets are used by the proposed protocol:
\begin{enumerate}[(a)]
\item INFO packet: such a packet will be sent by each sensor node to all the
sensing phase. We also consider primary points as targets. The set of primary
points is denoted by $P$ and the set of sensors by $J$. Only sensors able to be
alive during at least one round are involved in the integer program.
-\textcolor{blue}{Note that the proposed integer program is an
+\textcolor{black}{Note that the proposed integer program is an
extension of the one formulated in~\cite{idrees2015distributed}, variables are now indexed in
addition with the number of round $t$.}
network disconnection due to node failures. However, too many subdivisions
reduce the advantage of the optimization. In fact, there is a balance between
the benefit from the optimization and the execution time needed to solve it. In
-the following we have set the number of subregions to~16 \textcolor{blue}{as
+the following we have set the number of subregions to~16 \textcolor{black}{as
recommended in~\cite{idrees2015distributed}}.
\subsection{Energy model}
-\textcolor{blue}{The energy consumption model is detailed
+\textcolor{black}{The energy consumption model is detailed
in~\cite{raghunathan2002energy}. It is based on the model proposed
by~\cite{ChinhVu}. We refer to the sensor node Medusa~II which uses an Atmels
AVR ATmega103L microcontroller~\cite{raghunathan2002energy} to use numerical
\subsection{Metrics}
-\textcolor{blue}{To evaluate our approach we consider the performance metrics
+\textcolor{black}{To evaluate our approach we consider the performance metrics
detailed in~\cite{idrees2015distributed}, which are: Coverage Ratio, Network
Lifetime and Energy Consumption. Compared to the previous definitions,
formulations of Coverage Ratio and Energy Consumption are enriched with the
primary points to be used by a MuDiLCO protocol. In this comparison, MuDiLCO-1
protocol is used with five primary point models, each model corresponding to a
number of primary points, which are called Model-5 (it uses 5 primary points),
-Model-9, Model-13, Model-17, and Model-21. \textcolor{blue}{Note
+Model-9, Model-13, Model-17, and Model-21. \textcolor{black}{Note
that the results
presented in~\cite{idrees2015distributed} correspond to Model-13 (13 primary
points)}.
