X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/Sensornets15.git/blobdiff_plain/b8c6a5f1e74fdd26f663b1c0dd6454e15e84df73..cec3b1150a4effa0c5b6c66627d74813226f79eb:/Example.tex diff --git a/Example.tex b/Example.tex index 41f84a1..29cd9c6 100644 --- 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{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} +\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} -\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} -} -\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 - (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 + 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. }} - + 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} @@ -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. -{\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 @@ -333,10 +347,10 @@ Active-Sleep packet to know its state for the coming sensing phase. \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 $p$ : \begin{equation} @@ -360,11 +374,11 @@ U_{p} = \left \{ \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} @@ -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. -} +% 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 -\bibliographystyle{apalike} +\bibliographystyle{plain} {\small \bibliography{Example}}