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-\citation{pedraza2006}
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+\citation{pedraza2006}
+\citation{idrees2014coverage}
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-\@writefile{toc}{\contentsline {subsection}{\numberline {5.2}Performance analysis}{7}}
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\citation{ChinhVu}
\citation{xu2001geography}
\@writefile{lof}{\contentsline {figure}{\numberline {2}{\ignorespaces Coverage ratio\relax }}{8}}
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+\@writefile{toc}{\contentsline {subsection}{\numberline {5.2}Performance analysis}{8}}
+\newlabel{sub1}{{5.2}{8}}
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\newlabel{sec:Conclusion and Future Works}{{6}{10}}
\bibstyle{plain}
\bibdata{Example}
-\bibcite{ref17}{1}
-\bibcite{ref19}{2}
-\bibcite{berman04}{3}
-\bibcite{cardei2005improving}{4}
-\bibcite{cardei2005energy}{5}
-\bibcite{castano2013column}{6}
-\bibcite{conti2014mobile}{7}
-\bibcite{Deng2012}{8}
-\bibcite{deschinkel2012column}{9}
-\bibcite{idrees2014coverage}{10}
-\bibcite{jaggi2006}{11}
-\bibcite{kim2013maximum}{12}
-\bibcite{Kumar:2005}{13}
-\bibcite{li2013survey}{14}
-\bibcite{ling2009energy}{15}
-\bibcite{pujari2011high}{16}
-\bibcite{Misra}{17}
-\bibcite{Nayak04}{18}
-\bibcite{pc10}{19}
-\bibcite{pedraza2006}{20}
-\bibcite{qu2013distributed}{21}
-\bibcite{raghunathan2002energy}{22}
-\bibcite{ref22}{23}
-\bibcite{rossi2012exact}{24}
-\bibcite{varga}{25}
-\bibcite{chin2007}{26}
-\bibcite{ChinhVu}{27}
-\bibcite{5714480}{28}
-\bibcite{xu2001geography}{29}
-\bibcite{yang2014novel}{30}
-\bibcite{yangnovel}{31}
-\bibcite{Yang2014}{32}
-\bibcite{Zhang05}{33}
-\bibcite{zorbas2010solving}{34}
+\bibcite{berman04}{Berman and Calinescu, 2004}
+\bibcite{cardei2005improving}{Cardei and Du, 2005}
+\bibcite{cardei2005energy}{Cardei et\nobreakspace {}al., 2005}
+\bibcite{castano2013column}{Casta{\~n}o et\nobreakspace {}al., 2013}
+\bibcite{conti2014mobile}{Conti and Giordano, 2014}
+\bibcite{Deng2012}{Deng et\nobreakspace {}al., 2012}
+\bibcite{deschinkel2012column}{Deschinkel, 2012}
+\bibcite{idrees2014coverage}{Idrees et\nobreakspace {}al., 2014}
+\bibcite{jaggi2006}{Jaggi and Abouzeid, 2006}
+\bibcite{kim2013maximum}{Kim and Cobb, 2013}
+\bibcite{Kumar:2005}{Kumar et\nobreakspace {}al., 2005}
+\bibcite{li2013survey}{Li and Vasilakos, 2013}
+\bibcite{ling2009energy}{Ling and Znati, 2009}
+\bibcite{pujari2011high}{Manju and Pujari, 2011}
+\bibcite{Misra}{Misra et\nobreakspace {}al., 2011}
+\bibcite{Nayak04}{Nayak and Stojmenovic, 2010}
+\bibcite{pc10}{Padmavathy and Chitra, 2010}
+\bibcite{pedraza2006}{Pedraza et\nobreakspace {}al., 2006}
+\bibcite{qu2013distributed}{Qu and Georgakopoulos, 2013}
+\bibcite{raghunathan2002energy}{Raghunathan et\nobreakspace {}al., 2002}
+\bibcite{rossi2012exact}{Rossi et\nobreakspace {}al., 2012}
+\bibcite{varga}{Varga, 2003}
+\bibcite{ChinhVu}{Vu et\nobreakspace {}al., 2006}
+\bibcite{chin2007}{Vu, 2009}
+\bibcite{5714480}{Xing et\nobreakspace {}al., 2010}
+\bibcite{xu2001geography}{Xu et\nobreakspace {}al., 2001}
+\bibcite{yang2014novel}{Yang and Chin, 2014a}
+\bibcite{yangnovel}{Yang and Chin, 2014b}
+\bibcite{Yang2014}{Yang and Liu, 2014}
+\bibcite{Zhang05}{Zhang and Hou, 2005}
+\bibcite{zorbas2010solving}{Zorbas et\nobreakspace {}al., 2010}
unpractical environments) or cost reasons. Therefore, it is desired that the
WSNs are deployed with high densities so as to exploit the overlapping sensing
regions of some sensor nodes to save energy by turning off some of them during
-the sensing phase to prolong the network lifetime. \textcolor{blue}{A WSN can use various types of sensors such as \cite{ref17,ref19}: thermal, seismic, magnetic, visual, infrared, acoustic, and radar. These sensors are capable of observing different physical conditions such as: temperature, humidity, pressure, speed, direction, movement, light, soil makeup, noise levels, presence or absence of certain kinds of objects, and mechanical stress levels on attached objects. Consequently, a wide range of WSN applications such as~\cite{ref22}: health-care, environment, agriculture, public safety, military, transportation systems, and industry applications.}
+the sensing phase to prolong the network lifetime. \textcolor{blue}{A WSN can use various types of sensors such as \cite{ref17,ref19}: thermal, seismic, magnetic, visual, infrared, acoustic, and radar. These sensors are capable of observing different physical conditions such as: temperature, humidity, pressure, speed, direction, movement, light, soil makeup, noise levels, presence or absence of certain kinds of objects, and mechanical stress levels on attached objects. Consequently, there is a wide range of WSN applications such as~\cite{ref22}: health-care, environment, agriculture, public safety, military, transportation systems, and industry applications.}
In this paper we design a protocol that focuses on the area coverage problem
with the objective of maximizing the network lifetime. Our proposition, the
paper we made more realistic simulations by taking into account the
characteristics of a Medusa II sensor ~\cite{raghunathan2002energy} to measure
the energy consumption and the computation time. We have implemented two other
-existing approaches (a distributed one, DESK ~\cite{ChinhVu}, and a centralized
-one called GAF ~\cite{xu2001geography}) in order to compare their performances
+existing \textcolor{blue}{and distributed approaches}(DESK ~\cite{ChinhVu}, and GAF ~\cite{xu2001geography}) in order to compare their performances
with our approach. We also focus on performance analysis based on the number of
subregions.
% MODIF - END
\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.
+executed simultaneously in each subregion. \textcolor{blue}{Sensor nodes are assumed to
+be deployed almost uniformly over the region and the subdivision of the area of interest is regular.}
\begin{figure}[ht!]
\centering
protocol where each period is decomposed into 4~phases: Information Exchange,
Leader Election, Decision, and Sensing. For each period there will be exactly
one cover set in charge of the sensing task. A periodic scheduling is
-interesting because it enhances the robustness of the network against node failures. \textcolor{blue}{Many WSN applications have communication requirements that are periodic and known previously such as collecting temperature statistics at regular intervals. This periodic nature can be used to provide a regular schedule to sensor nodes and thus avoid a sensor failure. If the period time increases, the reliability and energy consumption are decreased and vice versa}. First, a node that has not enough energy to complete a period, or
+interesting because it enhances the robustness of the network against node failures.
+% \textcolor{blue}{Many WSN applications have communication requirements that are periodic and known previously such as collecting temperature statistics at regular intervals. This periodic nature can be used to provide a regular schedule to sensor nodes and thus avoid a sensor failure. If the period time increases, the reliability and energy consumption are decreased and vice versa}.
+First, a node that has not enough energy to complete a period, or
which fails before the decision is taken, will be excluded from the scheduling
process. Second, if a node fails later, whereas it was supposed to sense the
region of interest, it will only affect the quality of the coverage until the
