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+%\usepackage{apalike}
+%\usepackage{SCITEPRESS}
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-\begin{document}
%\title{Authors' Instructions \subtitle{Preparation of Camera-Ready Contributions to SCITEPRESS Proceedings} }
-\title{Distributed Lifetime Coverage Optimization Protocol \\in Wireless Sensor Networks}
+\title{Distributed Lifetime Coverage Optimization Protocol \\
+ in Wireless Sensor Networks}
-\author{\authorname{Ali Kadhum Idrees, Karine Deschinkel, Michel Salomon, and Rapha\"el Couturier}
-\affiliation{FEMTO-ST Institute, UMR 6174 CNRS, University of Franche-Comt\'e, Belfort, France}
-%\affiliation{\sup{2}Department of Computing, Main University, MySecondTown, MyCountry}
-\email{ali.idness@edu.univ-fcomte.fr, $\lbrace$karine.deschinkel, michel.salomon, raphael.couturier$\rbrace$@univ-fcomte.fr}
-%\email{\{f\_author, s\_author\}@ips.xyz.edu, t\_author@dc.mu.edu}
-}
+\author{Ali Kadhum Idrees$^{a,b}$, Karine Deschinkel$^{a}$,\\ Michel Salomon$^{a}$, and Rapha\"el Couturier$^{a}$\\
+$^{a}$FEMTO-ST Institute, UMR 6174 CNRS, \\ University Bourgogne Franche-Comt\'e, Belfort, France\\
+$^{b}${\em{Department of Computer Science, University of Babylon, Babylon, Iraq}}\\
+email: ali.idness@edu.univ-fcomte.fr,\\ $\lbrace$karine.deschinkel, michel.salomon, raphael.couturier$\rbrace$@univ-fcomte.fr}
-\keywords{Wireless Sensor Networks, Area Coverage, Network lifetime,
-Optimization, Scheduling.}
+%\author{Ali Kadhum Idrees$^{a,b}$, Karine Deschinkel$^{a}$,\\ Michel Salomon$^{a}$, and Rapha\"el Couturier $^{a}$ \\
+%$^{a}${\em{FEMTO-ST Institute, UMR 6174 CNRS, University Bourgogne Franche-Comt\'e,\\ Belfort, France}} \\
+%$^{b}${\em{Department of Computer Science, University of Babylon, Babylon, Iraq}} }
+
+\begin{document}
+ \maketitle
+%\keywords{Wireless Sensor Networks, Area Coverage, Network lifetime,Optimization, Scheduling.}
\abstract{ One of the main research challenges faced in Wireless Sensor Networks
(WSNs) is to preserve continuously and effectively the coverage of an area (or
region) of interest to be monitored, while simultaneously preventing as much
as possible a network failure due to battery-depleted nodes. In this paper we
propose a protocol, called Distributed Lifetime Coverage Optimization protocol
- (DiLCO), which maintains the coverage and improves the lifetime of a wireless
+ (DiLCO), which maintains the coverage and improves the lifetime of a wireless
sensor network. First, we partition the area of interest into subregions using
a classical divide-and-conquer method. Our DiLCO protocol is then distributed
- on the sensor nodes in each subregion in a second step. To fulfill our
- objective, the proposed protocol combines two effective techniques: a leader
+ on the sensor nodes in each subregion in a second step. To fulfill our
+ objective, the proposed protocol combines two effective techniques: a leader
election in each subregion, followed by an optimization-based node activity
- scheduling performed by each elected leader. This two-step process takes
+ scheduling performed by each elected leader. This two-step process takes
place periodically, in order to choose a small set of nodes remaining active
for sensing during a time slot. Each set is built to ensure coverage at a low
- energy cost, allowing to optimize the network lifetime.
- %More precisely, a
- %period consists of four phases: (i)~Information Exchange, (ii)~Leader
- %Election, (iii)~Decision, and (iv)~Sensing.
- The decision process, which
- results in an activity scheduling vector, is carried out by a leader node
- through the solving of an integer program.
- {\color{red} Simulations are conducted using the discret event simulator OMNET++.
- We refer to the characterictics of a Medusa II sensor for the energy consumption and the time computation.
- In comparison with two other existing methods, our approach is able to increase the WSN lifetime and provides
- improved coverage performance. }}
-
-
-\onecolumn \maketitle \normalsize \vfill
+ energy cost, allowing to optimize the network lifetime.
+%More precisely, a
+ %period consists of four phases: (i)~Information Exchange, (ii)~Leader
+ %Election, (iii)~Decision, and (iv)~Sensing. The decision process, which
+% results in an activity scheduling vector, is carried out by a leader node
+% through the solving of an integer program.
+% MODIF - BEGIN
+ Simulations are conducted using the discret event simulator
+ OMNET++. We refer to the characterictics of a Medusa II sensor for
+ the energy consumption and the computation time. In comparison with
+ two other existing methods, our approach is able to increase the WSN
+ lifetime and provides improved coverage performance. }
+% MODIF - END
+
+%\onecolumn
+
+
+%\normalsize \vfill
\section{\uppercase{Introduction}}
\label{sec:introduction}
\noindent
-Energy efficiency is a crucial issue in wireless sensor networks since sensory
+Energy efficiency is a crucial issue in wireless sensor networks since sensory
consumption, in order to maximize the network lifetime, represents the major
difficulty when designing WSNs. As a consequence, one of the 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}. Coverage reflects how well a
+coverage and connectivity~\cite{conti2014mobile}. Coverage reflects how well a
sensor field is monitored. On the one hand we want to monitor the area of
-interest in the most efficient way~\cite{Nayak04}. On the other hand we want to
-use as little energy as possible. Sensor nodes are battery-powered with no
-means of recharging or replacing, usually due to environmental (hostile or
-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.
+interest in the most efficient way~\cite{Nayak04}, \textcolor{blue}{which means
+ that we want to maintain the best coverage as long as possible}. On the other
+hand we want to use as little energy as possible. Sensor nodes are
+battery-powered with no means of recharging or replacing, usually due to
+environmental (hostile or 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, 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
-Distributed Lifetime Coverage Optimization (DILCO) protocol, maintains the
+Distributed Lifetime Coverage Optimization (DiLCO) protocol, maintains the
coverage and improves the lifetime in WSNs. The area of interest is first
divided into subregions using a divide-and-conquer algorithm and an activity
scheduling for sensor nodes is then planned by the elected leader in each
an integer program. The resulting activation vector is broadcast by a leader
to every node of its subregion.
-{\color{red} Our previous paper ~\cite{idrees2014coverage} relies almost exclusively on the framework of the DiLCO approach and the coverage problem formulation.
-In this paper we strengthen our 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 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 - BEGIN
+Our previous paper ~\cite{idrees2014coverage} relies almost exclusively on the
+framework of the DiLCO approach and the coverage problem formulation. In this
+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 \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
The remainder of the paper continues with Section~\ref{sec:Literature Review}
where a review of some related works is presented. The next section describes
\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. 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
\end{itemize}
%\end{enumerate}
-An outline of the protocol implementation is given by Algorithm~\ref{alg:DiLCO}
+An outline of the protocol implementation is given by Algorithm~\ref{alg:DiLCO}
which describes the execution of a period by a node (denoted by $s_j$ for a
-sensor node indexed by $j$). At the beginning a node checks whether it has
-enough energy to stay active during the next sensing phase. If yes, it exchanges
-information with all the other nodes belonging to the same subregion: it
-collects from each node its position coordinates, remaining energy ($RE_j$), ID,
-and the number of one-hop neighbors still alive. Once the first phase is
-completed, the nodes of a subregion choose a leader to take the decision based
-on the following criteria with decreasing importance: larger number of
-neighbors, larger remaining energy, and then in case of equality, larger index.
-After that, if the sensor node is leader, it will execute the integer program
-algorithm (see Section~\ref{cp}) which provides a set of sensors planned to be
-active in the next sensing phase. As leader, it will send an Active-Sleep packet
-to each sensor in the same subregion to indicate it if it has to be active or
-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.
-
+sensor node indexed by $j$). At the beginning a node checks whether it has
+enough energy \textcolor{blue}{(its energy should be greater than a fixed
+ treshold $E_{th}$)} to stay active during the next sensing phase. If yes, it
+exchanges information with all the other nodes belonging to the same subregion:
+it collects from each node its position coordinates, remaining energy ($RE_j$),
+ID, and the number of one-hop neighbors still alive. \textcolor{blue}{INFO
+ packet contains two parts: header and data payload. The sensor ID is included
+ in the header, where the header size is 8 bits. The data part includes
+ position coordinates (64 bits), remaining energy (32 bits), and the number of
+ one-hop live neighbors (8 bits). Therefore the size of the INFO packet is 112
+ bits.} Once the first phase is completed, the nodes of a subregion choose a
+leader to take the decision based on the following criteria with decreasing
+importance: larger number of neighbors, larger remaining energy, and then in
+case of equality, larger index. After that, if the sensor node is leader, it
+will solve an integer program (see Section~\ref{cp}). \textcolor{blue}{This
+ integer program contains boolean variables $X_j$ where ($X_j=1$) means that
+ sensor $j$ will be active in the next sensing phase. Only sensors with enough
+ remaining energy are involved in the integer program ($J$ is the set of all
+ sensors involved). As the leader consumes energy (computation energy is
+ denoted by $E^{comp}$) to solve the optimization problem, it will be included
+ in the integer program only if it has enough energy to achieve the computation
+ and to stay alive during the next sensing phase, that is to say if $RE_j >
+ E^{comp}+E_{th}$. Once the optimization problem is solved, each leader will
+ send an ActiveSleep packet to each sensor in the same subregion to indicate it
+ if it has to be active or not. Otherwise, if the sensor is not the leader, it
+ will wait for the ActiveSleep packet to know its state for the coming sensing
+ phase.}
+%which provides a set of sensors planned to be
+%active in the next sensing phase.
\begin{algorithm}[h!]
\section{\uppercase{Coverage problem formulation}}
\label{cp}
-{\color{red}
+% MODIF - BEGIN
We formulate the coverage optimization problem with an integer program.
The objective function consists in minimizing the undercoverage and the overcoverage of the area as suggested in \cite{pedraza2006}.
-The area coverage problem is transformed to the coverage of a fraction of points called primary points.
+The area coverage problem is expressed as the coverage of a fraction of points called primary points.
Details on the choice and the number of primary points can be found in \cite{idrees2014coverage}. The set of primary points is denoted by $P$
-and the set of sensors by $J$. As we consider a boolean disk coverage model, we use the boolean indicator $\alpha_{jp}$ which is equal to 1 if the primary point $p$ is in the sensing range of the sensor $j$. The binary variable $X_j$ represents the activation or not of the sensor $j$. So we can express the number of active sensors that cover the primary point $p$ by $\sum_{j \in J} \alpha_{jp} * X_{j}$. We deduce the overcoverage denoted by $\Theta_p$ of the primary point $p$ :
+and the set of alive sensors by $J$. As we consider a boolean disk coverage model, we use the boolean indicator $\alpha_{jp}$ which is equal to 1 if the primary point $p$ is in the sensing range of the sensor $j$. The binary variable $X_j$ represents the activation or not of the sensor $j$. So we can express the number of active sensors that cover the primary point $p$ by $\sum_{j \in J} \alpha_{jp} * X_{j}$. We deduce the overcoverage denoted by $\Theta_p$ of the primary point $p$ :
\begin{equation}
\Theta_{p} = \left \{
\begin{array}{l l}
\end{array} \right.
\label{eq14}
\end{equation}
-There is, of course, a relationship between the three variables $X_j$, $\Theta_p$ and $U_p$ which can be formulated as follows :
+There is, of course, a relationship between the three variables $X_j$, $\Theta_p$, and $U_p$ which can be formulated as follows :
\begin{equation}
\sum_{j \in J} \alpha_{jp} X_{j} - \Theta_{p}+ U_{p} =1, \forall p \in P
\end{equation}
-If the point $p$ is not covered, $U_p=1$, $\sum_{j \in J} \alpha_{jp} X_{j}=0$ and $\Theta_{p}=0$ by defintion, so the equality is satisfied.
+If the point $p$ is not covered, $U_p=1$, $\sum_{j \in J} \alpha_{jp} X_{j}=0$ and $\Theta_{p}=0$ by definition, so the equality is satisfied.
On the contrary, if the point $p$ is covered, $U_p=0$, and $\Theta_{p}=\left( \sum_{j \in J} \alpha_{jp} X_{j} \right)- 1$.
\noindent Our coverage optimization problem can then be formulated as follows:
\begin{equation} \label{eq:ip2r}
\end{array}
\right.
\end{equation}
-The objective function is a weighted sum of overcoverage and undercoverage. The goal is to limit the overcoverage in order to activate a minimal number of sensors while simultaneously preventing undercoverage. Both weights $w_\theta$ and $w_U$ must be carefully chosen in
-order to guarantee that the maximum number of points are covered during each
-period.
-}
+The objective function is a weighted sum of overcoverage and undercoverage. The goal is to limit the overcoverage in order to activate a minimal number of sensors while simultaneously preventing undercoverage. \textcolor{blue}{ By
+ choosing $w_{U}$ much larger than $w_{\theta}$, the coverage of a
+ maximum of primary points is ensured. Then for the same number of covered
+ primary points, the solution with a minimal number of active sensors is
+ preferred. }
+%Both weights $w_\theta$ and $w_U$ must be carefully chosen in
+%order to guarantee that the maximum number of points are covered during each
+%period.
+% MODIF - END
\parskip 0pt
\begin{figure}[t!]
\centering
- \includegraphics[scale=0.45] {R/CR.pdf}
+ \includegraphics[scale=0.45] {CR.pdf}
\caption{Coverage ratio}
\label{fig3}
\end{figure}
\begin{figure}[h!]
\centering
-\includegraphics[scale=0.45]{R/EC.pdf}
+\includegraphics[scale=0.45]{EC.pdf}
\caption{Energy consumption per period}
\label{fig95}
\end{figure}
\begin{figure}[h!]
\centering
-\includegraphics[scale=0.45]{R/T.pdf}
+\includegraphics[scale=0.45]{T.pdf}
\caption{Execution time in seconds}
\label{fig8}
\end{figure}
\begin{figure}[h!]
\centering
-\includegraphics[scale=0.45]{R/LT.pdf}
+\includegraphics[scale=0.45]{LT.pdf}
\caption{Network lifetime}
\label{figLT95}
\end{figure}
the Labex ACTION program (contract ANR-11-LABX-01-01).
%\vfill
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+\bibliographystyle{plain}
{\small
\bibliography{Example}}
