%% \address{Address\fnref{label3}}
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-\title{Distributed Lifetime Coverage Optimization Protocol in Wireless Sensor Networks}
+\title{Distributed Lifetime Coverage Optimization Protocol \\
+in Wireless Sensor Networks}
%% use optional labels to link authors explicitly to addresses:
%% \author[label1,label2]{}
%% \address[label1]{}
%% \address[label2]{}
-\author{Ali Kadhum Idrees, Karine Deschinkel, Michel Salomon, and Rapha\"el Couturier}
+\author{Ali Kadhum Idrees, Karine Deschinkel, \\
+Michel Salomon, and Rapha\"el Couturier}
%\thanks{are members in the AND team - DISC department - FEMTO-ST Institute, University of Franche-Comt\'e, Belfort, France.
% e-mail: ali.idness@edu.univ-fcomte.fr, $\lbrace$karine.deschinkel, michel.salomon, raphael.couturier$\rbrace$@univ-fcomte.fr.}% <-this % stops a space
%\thanks{}% <-this % stops a space
-
-\address{FEMTO-ST Institute, University of Franche-Comt\'e, Belfort, France. \\ e-mail: ali.idness@edu.univ-fcomte.fr, $\lbrace$karine.deschinkel, michel.salomon, raphael.couturier$\rbrace$@univ-fcomte.fr.}
+\address{FEMTO-ST Institute, University of Franche-Comt\'e, Belfort, France. \\
+e-mail: ali.idness@edu.univ-fcomte.fr, \\
+$\lbrace$karine.deschinkel, michel.salomon, raphael.couturier$\rbrace$@univ-fcomte.fr.}
\begin{abstract}
-One of the fundamental challenges in Wireless Sensor Networks (WSNs)
-is the coverage preservation and the extension of the network lifetime
-continuously and effectively when monitoring a certain area (or
-region) of interest. In this paper, a Distributed Lifetime Coverage Optimization Protocol (DiLCO)
-to maintain the coverage and to improve the lifetime in wireless sensor networks is proposed. The area of interest is first divided into subregions using a divide-and-conquer method and then the DiLCO protocol is distributed on the sensor nodes in each subregion. The DiLCO combines two efficient techniques: Leader election for each subregion after that activity scheduling based optimization is planned for each subregion. The proposed
-DiLCO works into rounds during which a small number of nodes,
-remaining active for sensing, is selected to ensure coverage so as to maximize the lifetime of wireless sensor network. Each round consists of four phases: (i)~Information Exchange, (ii)~Leader
-Election, (iii)~Decision, and (iv)~Sensing. The decision process is
-carried out by a leader node, which solves an integer program. Compared with some existing
-protocols, simulation results show that the proposed protocol can prolong the
-network lifetime and improve the coverage performance effectively.
-
+One of the fundamental challenges in Wireless Sensor Networks (WSNs) is the
+coverage preservation and the extension of the network lifetime continuously and
+effectively when monitoring a certain area (or region) of interest. In this
+paper, a Distributed Lifetime Coverage Optimization protocol (DiLCO) to maintain
+the coverage and to improve the lifetime in wireless sensor networks is
+proposed. The area of interest is first divided into subregions using a
+divide-and-conquer method and then the DiLCO protocol is distributed on the
+sensor nodes in each subregion. The DiLCO combines two efficient techniques:
+leader election for each subregion, followed by an optimization-based planning
+of activity scheduling decisions for each subregion. The proposed DiLCO works
+into rounds during which a small number of nodes, remaining active for sensing,
+is selected to ensure coverage so as to maximize the lifetime of wireless sensor
+network. Each round consists of four phases: (i)~Information Exchange,
+(ii)~Leader Election, (iii)~Decision, and (iv)~Sensing. The decision process is
+carried out by a leader node, which solves an integer program. Compared with
+some existing protocols, simulation results show that the proposed protocol can
+prolong the network lifetime and improve the coverage performance effectively.
\end{abstract}
\begin{keyword}
\end{frontmatter}
\section{Introduction}
-\indent In the last years, there has been increasing development in wireless networking,
-Micro-Electro-Mechanical Systems (MEMS), and embedded computing technologies, which are led to construct low-cost, small-sized and low-power sensor nodes that can perform detection, computation and data communication of surrounding environment. WSN
-includes a large number of small, limited-power sensors that can
-sense, process and transmit data over a wireless communication. They
-communicate with each other by using multi-hop wireless communications, cooperate together to monitor the area of interest,
-and the measured data can be reported to a monitoring center called sink
-for analysis it~\cite{Sudip03}. There are several applications used the
-WSN including health, home, environmental, military, and industrial
-applications~\cite{Akyildiz02}. One of the major scientific research challenges in WSNs, which are addressed by a large number of literature during the last few years is to design energy efficient approches for coverage and connectivity in WSNs~\cite{conti2014mobile}.The coverage problem is one of the
-fundamental challenges in WSNs~\cite{Nayak04} that consists in monitoring efficiently and continuously
-the area of interest. The limited energy of sensors represents the main challenge in WSNs
-design~\cite{Sudip03}, where it is impossible or inconvenient to replace and/or recharge their batteries because the the area of interest nature (such
-as remote, hostile or unpractical environments) and the cost. So, it is necessary that a WSN
-deployed with high density because spatial redundancy can then be
-exploited to increase the lifetime of the network. However, turn on
-all the sensor nodes, which monitor the same region at the same time
-leads to decrease the lifetime of the network. To extend the lifetime
-of the network, the main idea is to take advantage of the overlapping
-sensing regions of some sensor nodes to save energy by turning off
-some of them during the sensing phase~\cite{Misra05}. WSNs require
-energy-efficient solutions to improve the network lifetime that is
-constrained by the limited power of each sensor node ~\cite{Akyildiz02}. In this paper, we concentrate on the area
-coverage problem, with the objective of maximizing the network
-lifetime by using an adaptive scheduling. The area of interest is
-divided into subregions and an activity scheduling for sensor nodes is
-planned for each subregion. In fact, the nodes in a subregion can be
-seen as a cluster where each node sends sensing data to the cluster head or the sink node. Furthermore, the activities in a
-subregion/cluster can continue even if another cluster stops due to
-too many node failures. Our scheduling scheme considers rounds, where
-a round starts with a discovery phase to exchange information between
-sensors of the subregion, in order to choose in a suitable manner a
-sensor node to carry out a coverage strategy. This coverage strategy
-involves the solving of an integer program, which provides the
-activation of the sensors for the sensing phase of the current round.
+\indent In the last years, there has been an increasing development in wireless
+networking, Micro-Electro-Mechanical Systems (MEMS), and embedded computing
+technologies, which have led to construct low-cost, small-sized, and low-power
+sensor nodes that can perform detection, computation, and data communication of
+surrounding environment. A WSN includes a large number of small, limited-power
+sensors that can sense, process, and transmit data over a wireless
+communication. They communicate with each other by using multi-hop wireless
+communications and cooperate together to monitor the area of interest, so that
+each measured data can be reported to a monitoring center called sink for
+further analysis~\cite{Sudip03}.
+
+There are several fields of application covering a wide spectrum for a WSN,
+including health, home, environmental, military, and industrial
+applications~\cite{Akyildiz02}. One of the major scientific research challenges
+in WSNs, which has been addressed by a large amount of literature during the
+last few years, is the design of energy efficient approaches for coverage and
+connectivity~\cite{conti2014mobile}. On the one hand an optimal
+coverage~\cite{Nayak04} is required to monitor efficiently and continuously the
+area of interest and on the other hand the energy consumption must be as low as
+possible, due to the limited energy of sensors~\cite{Sudip03} and the
+impossibility or difficulty to replace and/or recharge their batteries because
+of the area of interest nature (such as remote, hostile, or unpractical
+environments) and the cost. So, it is of great relevance for a WSN to be
+deployed with high density, because spatial redundancy can then be exploited to
+increase the lifetime of the network. However, turning on all the sensor nodes
+which monitor the same region at the same time reduces the the lifetime of the
+network. Therefore, to extend the lifetime of the network, the main idea is to
+take advantage of the overlapping sensing regions of some sensor nodes to save
+energy by turning off some of them during the sensing phase~\cite{Misra05}.
+
+In this paper we concentrate on the area coverage problem with the objective of
+maximizing the network lifetime by using an adaptive scheduling. The area of
+interest is divided into subregions and an activity scheduling for sensor nodes
+is planned for each subregion. In fact, the nodes in a subregion can be seen as
+a cluster where each node sends sensing data to the cluster head or the sink
+node. Furthermore, the activities in a subregion/cluster can continue even if
+another cluster stops due to too many node failures. Our scheduling scheme
+considers rounds, where a round starts with a discovery phase to exchange
+information between sensors of the subregion, in order to choose in a suitable
+manner a sensor node to carry out a coverage strategy. This coverage strategy
+involves the solving of an integer program, which provides the activation of the
+sensors for the sensing phase of the current round.
The remainder of the paper is organized as follows. The next section
% Section~\ref{rw}
-reviews the related work in the field. In section~\ref{prel}, the problem definition and some background are described. Section~\ref{pd} is devoted to
-the DiLCO Protocol Description. Section~\ref{cp} gives the coverage model formulation, which is used
-to schedule the activation of sensors. Section~\ref{exp} shows the
-simulation results obtained using the discrete event simulator OMNeT++
-\cite{varga}. They fully demonstrate the usefulness of the proposed
-approach. Finally, we give concluding remarks and some suggestions
-for future works in Section~\ref{sec:conclusion}.
-
+reviews the related work in the field. In section~\ref{prel}, the problem
+definition and some background are described. Section~\ref{pd} is devoted to the
+DiLCO protocol Description. Section~\ref{cp} gives the coverage model
+formulation which is used to schedule the activation of sensors.
+Section~\ref{exp} shows the simulation results obtained using the discrete event
+simulator OMNeT++ \cite{varga}. They fully demonstrate the usefulness of the
+proposed approach. Finally, we give concluding remarks and some suggestions for
+future works in Section~\ref{sec:conclusion}.
+
+% MICHEL - OK up to here
\section{Related works}
\label{rw}
