Various approaches, including centralized, or distributed
algorithms, have been proposed to extend the network lifetime.
-%For instance, in order to hide the occurrence of faults, or the sudden unavailability of
-%sensor nodes, some distributed algorithms have been developed in~\cite{Gallais06,Tian02,Ye03,Zhang05,HeinzelmanCB02}.
In distributed algorithms~\cite{yangnovel,ChinhVu,qu2013distributed},
information is disseminated throughout the network and sensors decide
cooperatively by communicating with their neighbors which of them will remain in
\cite{pedraza2006} where the objective is to maximize the number of cover
sets.}
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\section{\uppercase{Description of the DiLCO protocol}}
\label{sec:The DiLCO Protocol Description}
techniques: network leader election and sensor activity scheduling for coverage
preservation and energy conservation, applied periodically to efficiently
maximize the lifetime in the network.
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\subsection{Assumptions and models}
\subsection{Main idea}
\label{main_idea}
-
\noindent We start by applying a divide-and-conquer algorithm to partition the
area of interest into smaller areas called subregions and then our protocol is
executed simultaneously in each subregion.
not. Alternately, if the sensor is not the leader, it will wait for the
Active-Sleep packet to know its state for the coming sensing phase.
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\begin{algorithm}[h!]
subregions during the current sensing phase and $N$ is the total number of grid
points in the sensing field. In our simulations, we have a layout of $N = 51
\times 26 = 1326$ grid points.
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%The accuracy of this method depends on the distance between grids. In our
%simulations, the sensing field has been divided into 50 by 25 grid points, which means
%there are $51 \times 26~ = ~ 1326$ points in total.
% Therefore, for our simulations, the error in the coverage calculation is less than ~ 1 $\% $.
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\item {{\bf Energy Consumption}:} energy consumption (EC) can be seen as the
total amount of energy consumed by the sensors during $Lifetime_{95}$ or
during a period. Finally, $E^a_{m}$ and $E^s_{m}$ indicate the energy consumed
by the whole network in the sensing phase (active and sleeping nodes).
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\end{itemize}
%\end{enumerate}
\label{fig3}
\end{figure}
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\subsubsection{Energy consumption}
Based on the results shown in Figure~\ref{fig3}, we focus on the DiLCO-16 and
\begin{figure}[h!]
\centering
\includegraphics[scale=0.45]{R/EC.pdf}
-\caption{Energy consumption}
+\caption{Energy consumption per period}
\label{fig95}
\end{figure}
similar level of area coverage. This observation reflects the larger number of
nodes set active by DESK and GAF.
-
-%In fact, a distributed method on the subregions greatly reduces the number of communications and the time of listening so thanks to the partitioning of the initial network into several independent subnetworks.
-%As shown in Figures~\ref{fig95} and ~\ref{fig50} , DiLCO-2 consumes more energy than the other versions of DiLCO, especially for large sizes of network. This is easy to understand since the bigger the number of sensors involved in the integer program, the larger the time computation to solve the optimization problem as well as the higher energy consumed during the communication.
-
\subsubsection{Execution time}
Another interesting point to investigate is the evolution of the execution time
subregions. Thus, the optimal number of subregions can be seen as a trade-off
between execution time and coverage performance.
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\subsubsection{Network lifetime}
In the next figure, the network lifetime is illustrated. Obviously, the lifetime
the larger level of coverage ($95\%$) in the case of our protocol, the subdivision
in $16$~subregions seems to be the most appropriate.
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\section{\uppercase{Conclusion and future work}}
\label{sec:Conclusion and Future Works}
context a subdivision in $16$~subregions seems to be the most relevant. The
optimal number of subregions will be investigated in the future.
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\section*{\uppercase{Acknowledgements}}
\noindent As a Ph.D. student, Ali Kadhum IDREES would like to gratefully
