-\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
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
lifetime and provides improved coverage performance. }
% MODIF - END
-\onecolumn \maketitle \normalsize \vfill
+%\onecolumn
+
+
+%\normalsize \vfill
\section{\uppercase{Introduction}}
\label{sec:introduction}
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
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}
\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
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.
\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
-\bibliographystyle{apalike}
+\bibliographystyle{plain}
{\small
\bibliography{Example}}
