X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/Sensornets15.git/blobdiff_plain/f6ed458a211eb088e2e1f4e18f77423682ea034f..c5d35ebf9a8ac33354f3f6514280bce5ec92a729:/Example.tex?ds=sidebyside diff --git a/Example.tex b/Example.tex index dc5dd18..c30cff5 100644 --- a/Example.tex +++ b/Example.tex @@ -1,65 +1,78 @@ -\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} + +%\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}} } -\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 - results in an activity scheduling vector, is carried out by a leader node - through the solving of an integer program. In comparison with some other - protocols, the simulations done using the discrete event simulator OMNeT++ - show that our approach is able to increase the WSN lifetime and provides - improved coverage performance. } + 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 \maketitle \normalsize \vfill +%\onecolumn + + +%\normalsize \vfill \section{\uppercase{Introduction}} \label{sec:introduction} @@ -78,11 +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 @@ -95,7 +108,18 @@ same subregion, in order to choose in a suitable manner a sensor node (the leader) to carry out the coverage strategy. In each subregion the activation of 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. +to every node of its subregion. + +% 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 @@ -219,7 +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 @@ -232,8 +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 @@ -323,6 +349,68 @@ Active-Sleep packet to know its state for the coming sensing phase. \section{\uppercase{Coverage problem formulation}} \label{cp} +% 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 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} + \Theta_{p} = \left \{ +\begin{array}{l l} + 0 & \mbox{if the primary point}\\ + & \mbox{$p$ is not covered,}\\ + \left( \sum_{j \in J} \alpha_{jp} * X_{j} \right)- 1 & \mbox{otherwise.}\\ +\end{array} \right. +\label{eq13} +\end{equation} +More precisely, $\Theta_{p}$ represents the number of active sensor +nodes minus one that cover the primary point~$p$. +In the same way, we define the undercoverage variable +$U_{p}$ of the primary point $p$ as: +\begin{equation} +U_{p} = \left \{ +\begin{array}{l l} + 1 &\mbox{if the primary point $p$ is not covered,} \\ + 0 & \mbox{otherwise.}\\ +\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 : +\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 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} +\left \{ +\begin{array}{ll} +\min \sum_{p \in P} (w_{\theta} \Theta_{p} + w_{U} U_{p})&\\ +\textrm{subject to :}&\\ +\sum_{j \in J} \alpha_{jp} X_{j} - \Theta_{p}+ U_{p} =1, &\forall p \in P\\ +%\label{c1} +%\sum_{t \in T} X_{j,t} \leq \frac{RE_j}{e_t} &\forall j \in J \\ +%\label{c2} +\Theta_{p}\in \mathbb{N}, &\forall p \in P\\ +U_{p} \in \{0,1\}, &\forall p \in P \\ +X_{j} \in \{0,1\}, &\forall j \in J +\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. +% MODIF - END + + + + + + + +\iffalse + \indent Our model is based on the model proposed by \cite{pedraza2006} where the objective is to find a maximum number of disjoint cover sets. To accomplish this goal, the authors proposed an integer program which forces undercoverage @@ -411,6 +499,8 @@ 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. +\fi + \section{\uppercase{Protocol evaluation}} \label{sec:Simulation Results and Analysis} \noindent \subsection{Simulation framework} @@ -624,7 +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} @@ -645,7 +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} @@ -681,7 +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} @@ -708,7 +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} @@ -756,7 +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}}