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[Sensornets15.git] / Example.tex

diff --git a/Example.tex b/Example.tex
index 41f84a1d01dd242e19a9697a7687aa193628ab0e..29cd9c6d0010bc9b1b60e056fa075d6b651479e0 100644 (file)
--- a/Example.tex
+++ b/Example.tex
@@ -1,69 +1,77 @@
-\documentclass[a4paper,twoside]{article}
+\documentclass[a4,12pt]{article}

 
 

+
+\usepackage[paper=a4paper,dvips,top=1.5cm,left=1.5cm,right=1.5cm,foot=1cm,bottom=1.5cm]{geometry}

 \usepackage{epsfig}
 \usepackage{subfigure}
 \usepackage{epsfig}
 \usepackage{subfigure}

-\usepackage{calc}
+%\usepackage{calc}

 \usepackage{amssymb}
 \usepackage{amssymb}

-\usepackage{amstext}
-\usepackage{amsmath}
-\usepackage{amsthm}
-\usepackage{multicol}
-\usepackage{pslatex}
-\usepackage{apalike}
-\usepackage{SCITEPRESS}
+%\usepackage{amstext}
+%\usepackage{amsmath}
+%\usepackage{amsthm}
+%\usepackage{multicol}
+%\usepackage{pslatex}
+%\usepackage{apalike}
+%\usepackage{SCITEPRESS}

 \usepackage[small]{caption}
 \usepackage{color}
 \usepackage[linesnumbered,ruled,vlined,commentsnumbered]{algorithm2e}
 \usepackage{mathtools}  
 
 \usepackage[small]{caption}
 \usepackage{color}
 \usepackage[linesnumbered,ruled,vlined,commentsnumbered]{algorithm2e}
 \usepackage{mathtools}  
 

-\subfigtopskip=0pt
-\subfigcapskip=0pt
-\subfigbottomskip=0pt
+%\subfigtopskip=0pt
+%\subfigcapskip=0pt
+%\subfigbottomskip=0pt
+

 
 

-\begin{document}

 
 %\title{Authors' Instructions  \subtitle{Preparation of Camera-Ready Contributions to SCITEPRESS Proceedings} }
 
 
 %\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}
+\author{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}
 %\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: 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}
 %\email{\{f\_author, s\_author\}@ips.xyz.edu, t\_author@dc.mu.edu}

-}

 
 

-\keywords{Wireless   Sensor   Networks,   Area   Coverage,   Network   lifetime,
-Optimization, Scheduling.}
+\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
 
 \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
   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
   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
   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
+  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
   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. }}
-       
+  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 \maketitle \normalsize \vfill
+%\onecolumn
+
+
+%\normalsize \vfill

 
 \section{\uppercase{Introduction}}
 \label{sec:introduction}
 
 \section{\uppercase{Introduction}}
 \label{sec:introduction}


@@ -101,11 +109,17 @@ the sensors for  the sensing phase of the current period  is obtained by solving
 an integer program.  The resulting activation vector is  broadcast by a leader
 to every node of its subregion. 
 
 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 approaches (a distributed one,  DESK ~\cite{ChinhVu}, and a centralized
+one called 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
 
 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


@@ -333,10 +347,10 @@ Active-Sleep packet to know its state for the coming sensing phase.
 \section{\uppercase{Coverage problem formulation}}
 \label{cp}
 
 \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}. 
 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 $p$ :
 \begin{equation}
 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 $p$ :
 \begin{equation}


@@ -360,11 +374,11 @@ U_{p} = \left \{
 \end{array} \right.
 \label{eq14} 
 \end{equation}
 \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}
 \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}
 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}


@@ -385,7 +399,7 @@ X_{j} \in \{0,1\}, &\forall j \in J
 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. 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

 
 
 
 
 
 


@@ -830,7 +844,7 @@ Campus France for  the received support. This paper is  also partially funded by
 the Labex ACTION program (contract ANR-11-LABX-01-01).
 
 %\vfill
 the Labex ACTION program (contract ANR-11-LABX-01-01).
 
 %\vfill

-\bibliographystyle{apalike}
+\bibliographystyle{plain}

 {\small
 \bibliography{Example}}
 
 {\small
 \bibliography{Example}}