New modifications

[Sensornets15.git] / Example.tex

diff --git a/Example.tex b/Example.tex
index 41f84a1d01dd242e19a9697a7687aa193628ab0e..06be2737b984dd37e7e399b71be63edb87edfb7f 100644 (file)
--- a/Example.tex
+++ b/Example.tex
@@ -1,92 +1,112 @@
-\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}
-%\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
 
 \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
-  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 
 
 \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
 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
 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
 
 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
 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


@@ -101,11 +121,17 @@ 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 \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
 
 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


@@ -229,7 +255,8 @@ less accurate according to the number of primary points.
 \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
 \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
 
 \begin{figure}[ht!]
 \centering


@@ -242,8 +269,9 @@ As  shown  in Figure~\ref{fig2},  the  proposed  DiLCO  protocol is  a  periodic
 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
 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
 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


@@ -274,23 +302,37 @@ and each sensor node will have five possible status in the network:
 \end{itemize}
 %\end{enumerate}
 
 \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
 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!]                
 
 
 \begin{algorithm}[h!]                
 


@@ -333,12 +375,12 @@ 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 $P$
 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}
 \begin{equation}
  \Theta_{p} = \left \{ 
 \begin{array}{l l}


@@ -360,11 +402,11 @@ 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}


@@ -382,10 +424,15 @@ X_{j} \in \{0,1\}, &\forall j \in J
 \end{array}
 \right.
 \end{equation}
 \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

 
 
 
 
 
 


@@ -698,7 +745,7 @@ nodes, and thus enables the extension of the network lifetime.
 \parskip 0pt    
 \begin{figure}[t!]
 \centering
 \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} 
 \caption{Coverage ratio}
 \label{fig3}
 \end{figure} 


@@ -719,7 +766,7 @@ used for the different performance metrics.
 
 \begin{figure}[h!]
 \centering
 
 \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} 
 \caption{Energy consumption per period}
 \label{fig95}
 \end{figure} 


@@ -755,7 +802,7 @@ Figure~\ref{fig8}.
 
 \begin{figure}[h!]
 \centering
 
 \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} 
 \caption{Execution time in seconds}
 \label{fig8}
 \end{figure} 


@@ -782,7 +829,7 @@ network lifetime.
 
 \begin{figure}[h!]
 \centering
 
 \begin{figure}[h!]
 \centering

-\includegraphics[scale=0.45]{R/LT.pdf}  
+\includegraphics[scale=0.45]{LT.pdf}  

 \caption{Network lifetime}
 \label{figLT95}
 \end{figure} 
 \caption{Network lifetime}
 \label{figLT95}
 \end{figure} 


@@ -830,7 +877,7 @@ 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}}