X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/Sensornets15.git/blobdiff_plain/34d0f50338c2813bf9cc5b22535e90523d3a5926..8abf40a6144c2ae97b77ec718fef360e652d01b9:/Example.tex?ds=inline

diff --git a/Example.tex b/Example.tex
index 3f5d44c..38cc14f 100644
--- a/Example.tex
+++ b/Example.tex
@@ -1,40 +1,45 @@
-\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{calc}
+%\usepackage{calc}
 \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}  
 
-\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{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
@@ -50,11 +55,12 @@ Optimization, Scheduling.}
   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
+  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.
+%  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
@@ -63,7 +69,10 @@ Optimization, Scheduling.}
   lifetime and provides improved coverage performance. }
 % MODIF - END
 
-\onecolumn \maketitle \normalsize \vfill
+%\onecolumn
+
+
+%\normalsize \vfill
 
 \section{\uppercase{Introduction}}
 \label{sec:introduction}
@@ -82,11 +91,11 @@ 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.
+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
@@ -107,10 +116,9 @@ 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
+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. }
+subregions. 
 % MODIF - END
 
 The remainder  of the  paper continues with  Section~\ref{sec:Literature Review}
@@ -235,7 +243,8 @@ 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
-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
@@ -248,8 +257,9 @@ 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
-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
@@ -283,10 +293,10 @@ and each sensor node will have five possible status in the network:
 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
+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. Once  the  first phase  is
+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.
@@ -704,7 +714,7 @@ nodes, and thus enables the extension of the network lifetime.
 \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} 
@@ -725,7 +735,7 @@ used for the different performance metrics.
 
 \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} 
@@ -761,7 +771,7 @@ Figure~\ref{fig8}.
 
 \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} 
@@ -788,7 +798,7 @@ network lifetime.
 
 \begin{figure}[h!]
 \centering
-\includegraphics[scale=0.45]{R/LT.pdf}  
+\includegraphics[scale=0.45]{LT.pdf}  
 \caption{Network lifetime}
 \label{figLT95}
 \end{figure} 
@@ -836,7 +846,7 @@ Campus France for  the received support. This paper is  also partially funded by
 the Labex ACTION program (contract ANR-11-LABX-01-01).
 
 %\vfill
-\bibliographystyle{apalike}
+\bibliographystyle{plain}
 {\small
 \bibliography{Example}}
 
