From: Michel Salomon Date: Wed, 25 Jun 2014 11:30:29 +0000 (+0200) Subject: Abstract + Introduction X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/JournalMultiRounds.git/commitdiff_plain/HEAD?ds=sidebyside Abstract + Introduction --- diff --git a/elsarticle-template-num.aux b/elsarticle-template-num.aux index 6ef86de..a562f7c 100644 --- a/elsarticle-template-num.aux +++ b/elsarticle-template-num.aux @@ -5,7 +5,6 @@ \citation{Nayak04} \citation{Sudip03} \citation{Misra05} -\citation{Akyildiz02} \@writefile{toc}{\contentsline {section}{\numberline {1}Introduction}{2}} \citation{varga} \citation{Slijepcevic01powerefficient,abrams2004set,cardei2005improving,zorbas2010solving,pujari2011high,cardei2005energy,berman04} @@ -52,32 +51,32 @@ \@writefile{toc}{\contentsline {subsection}{\numberline {4.1} Assumptions and Models}{9}} \@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces Wireless Sensor Node represented by (a)5, (b)9 and (c)13 primary points respectively}}{10}} \newlabel{fig1}{{1}{10}} -\@writefile{toc}{\contentsline {subsection}{\numberline {4.2}The Main Idea}{10}} +\@writefile{toc}{\contentsline {subsection}{\numberline {4.2}The Main Idea}{11}} \@writefile{lof}{\contentsline {figure}{\numberline {2}{\ignorespaces DiLCO protocol}}{11}} \newlabel{fig2}{{2}{11}} \@writefile{toc}{\contentsline {subsubsection}{\numberline {4.2.1}Information Exchange Phase}{12}} \@writefile{toc}{\contentsline {subsubsection}{\numberline {4.2.2}Leader Election Phase}{12}} -\@writefile{loa}{\contentsline {algocf}{\numberline {1}{\ignorespaces LEADER ELECTION}}{12}} -\newlabel{alg:LEADER}{{1}{12}} -\@writefile{toc}{\contentsline {subsubsection}{\numberline {4.2.3}Decision phase}{13}} -\@writefile{toc}{\contentsline {subsubsection}{\numberline {4.2.4}Sensing phase}{13}} +\@writefile{toc}{\contentsline {subsubsection}{\numberline {4.2.3}Decision phase}{12}} +\@writefile{toc}{\contentsline {subsubsection}{\numberline {4.2.4}Sensing phase}{12}} +\@writefile{loa}{\contentsline {algocf}{\numberline {1}{\ignorespaces LEADER ELECTION}}{13}} +\newlabel{alg:LEADER}{{1}{13}} \@writefile{toc}{\contentsline {subsection}{\numberline {4.3}DiLCO protocol Algorithm}{13}} \@writefile{loa}{\contentsline {algocf}{\numberline {2}{\ignorespaces DiLCO($s_j$)}}{14}} \newlabel{alg:DMRCLOP}{{2}{14}} \citation{pedraza2006} \@writefile{toc}{\contentsline {section}{\numberline {5}Coverage problem formulation}{15}} \newlabel{cp}{{5}{15}} -\newlabel{eq13}{{3}{15}} \citation{varga} +\newlabel{eq13}{{3}{16}} \newlabel{eq14}{{4}{16}} \newlabel{eq:ip2r}{{5}{16}} -\@writefile{toc}{\contentsline {section}{\numberline {6}Simulation Results and Analysis}{16}} -\newlabel{exp}{{6}{16}} -\@writefile{toc}{\contentsline {subsection}{\numberline {6.1}Simulation framework, energy consumption model and performance metrics}{16}} \citation{ChinhVu} \citation{raghunathan2002energy} \citation{raghunathan2002energy} \citation{raghunathan2002energy} +\@writefile{toc}{\contentsline {section}{\numberline {6}Simulation Results and Analysis}{17}} +\newlabel{exp}{{6}{17}} +\@writefile{toc}{\contentsline {subsection}{\numberline {6.1}Simulation framework, energy consumption model and performance metrics}{17}} \@writefile{lot}{\contentsline {table}{\numberline {1}{\ignorespaces Relevant parameters for network initializing.}}{17}} \newlabel{table3}{{1}{17}} \citation{raghunathan2002energy} @@ -89,19 +88,19 @@ \@writefile{lof}{\contentsline {figure}{\numberline {3}{\ignorespaces The impact of the number of rounds on the coverage ratio for 150 deployed nodes}}{21}} \newlabel{fig3}{{3}{21}} \@writefile{toc}{\contentsline {subsubsection}{\numberline {6.2.2}Active Sensors Ratio}{21}} -\@writefile{toc}{\contentsline {subsubsection}{\numberline {6.2.3}The percentage of stopped simulation runs}{21}} \@writefile{lof}{\contentsline {figure}{\numberline {4}{\ignorespaces The impact of the number of rounds on the active sensors ratio for 150 deployed nodes }}{22}} \newlabel{fig4}{{4}{22}} +\@writefile{toc}{\contentsline {subsubsection}{\numberline {6.2.3}The percentage of stopped simulation runs}{22}} \@writefile{lof}{\contentsline {figure}{\numberline {5}{\ignorespaces The percentage of stopped simulation runs compared to the number of rounds for 150 deployed nodes }}{22}} \newlabel{fig6}{{5}{22}} -\@writefile{toc}{\contentsline {subsubsection}{\numberline {6.2.4}The Energy Consumption}{22}} +\@writefile{toc}{\contentsline {subsubsection}{\numberline {6.2.4}The Energy Consumption}{23}} \@writefile{lof}{\contentsline {figure}{\numberline {6}{\ignorespaces The Energy Consumption for Lifetime95}}{23}} \newlabel{fig95}{{6}{23}} -\@writefile{toc}{\contentsline {subsubsection}{\numberline {6.2.5}Execution Time}{23}} \@writefile{lof}{\contentsline {figure}{\numberline {7}{\ignorespaces The Energy Consumption for Lifetime50}}{24}} \newlabel{fig50}{{7}{24}} -\@writefile{lof}{\contentsline {figure}{\numberline {8}{\ignorespaces Execution Time (in seconds)}}{24}} -\newlabel{fig8}{{8}{24}} +\@writefile{toc}{\contentsline {subsubsection}{\numberline {6.2.5}Execution Time}{24}} +\@writefile{lof}{\contentsline {figure}{\numberline {8}{\ignorespaces Execution Time (in seconds)}}{25}} +\newlabel{fig8}{{8}{25}} \@writefile{toc}{\contentsline {subsubsection}{\numberline {6.2.6}The Network Lifetime}{25}} \@writefile{lof}{\contentsline {figure}{\numberline {9}{\ignorespaces The Network Lifetime for $Lifetime95$}}{25}} \newlabel{figLT95}{{9}{25}} @@ -119,8 +118,8 @@ \@writefile{lof}{\contentsline {figure}{\numberline {13}{\ignorespaces The percentage of stopped simulation runs compared to the number of rounds for 150 deployed nodes }}{29}} \newlabel{fig66}{{13}{29}} \@writefile{toc}{\contentsline {subsubsection}{\numberline {6.3.4}The Energy Consumption}{29}} -\@writefile{lof}{\contentsline {figure}{\numberline {14}{\ignorespaces The Energy Consumption with $95\%-Lifetime$}}{29}} -\newlabel{fig2EC95}{{14}{29}} +\@writefile{lof}{\contentsline {figure}{\numberline {14}{\ignorespaces The Energy Consumption with $95\%-Lifetime$}}{30}} +\newlabel{fig2EC95}{{14}{30}} \@writefile{lof}{\contentsline {figure}{\numberline {15}{\ignorespaces The Energy Consumption with $Lifetime50$}}{30}} \newlabel{fig2EC50}{{15}{30}} \@writefile{toc}{\contentsline {subsubsection}{\numberline {6.3.5}Execution Time}{30}} @@ -129,15 +128,15 @@ \@writefile{lof}{\contentsline {figure}{\numberline {16}{\ignorespaces The Execution Time(s) vs The Number of Sensors }}{31}} \newlabel{figt}{{16}{31}} \@writefile{toc}{\contentsline {subsubsection}{\numberline {6.3.6}The Network Lifetime}{31}} -\@writefile{toc}{\contentsline {subsection}{\numberline {6.4}Performance Comparison for Different Approaches}{31}} \@writefile{lof}{\contentsline {figure}{\numberline {17}{\ignorespaces The Network Lifetime for $Lifetime95$}}{32}} \newlabel{fig2LT95}{{17}{32}} \@writefile{lof}{\contentsline {figure}{\numberline {18}{\ignorespaces The Network Lifetime for $Lifetime50$}}{32}} \newlabel{fig2LT50}{{18}{32}} +\@writefile{toc}{\contentsline {subsection}{\numberline {6.4}Performance Comparison for Different Approaches}{32}} \@writefile{toc}{\contentsline {subsubsection}{\numberline {6.4.1}Coverage Ratio}{33}} \@writefile{lof}{\contentsline {figure}{\numberline {19}{\ignorespaces The coverage ratio for 150 deployed nodes}}{33}} \newlabel{fig333}{{19}{33}} -\@writefile{toc}{\contentsline {subsubsection}{\numberline {6.4.2}Active Sensors Ratio}{33}} +\@writefile{toc}{\contentsline {subsubsection}{\numberline {6.4.2}Active Sensors Ratio}{34}} \@writefile{lof}{\contentsline {figure}{\numberline {20}{\ignorespaces The active sensors ratio for 150 deployed nodes }}{34}} \newlabel{fig444}{{20}{34}} \@writefile{toc}{\contentsline {subsubsection}{\numberline {6.4.3}The percentage of stopped simulation runs}{34}} @@ -155,9 +154,9 @@ \newlabel{fig3LT50}{{25}{37}} \bibstyle{elsarticle-num} \bibdata{elsarticle-template-num} 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PDF statistics: - 447 PDF objects out of 1000 (max. 8388607) - 296 compressed objects within 3 object streams + 443 PDF objects out of 1000 (max. 8388607) 0 named destinations out of 1000 (max. 500000) 136 words of extra memory for PDF output out of 10000 (max. 10000000) diff --git a/elsarticle-template-num.pdf b/elsarticle-template-num.pdf index 218981a..ebdeb03 100644 Binary files a/elsarticle-template-num.pdf and b/elsarticle-template-num.pdf differ diff --git a/elsarticle-template-num.synctex.gz b/elsarticle-template-num.synctex.gz deleted file mode 100644 index d2b92bb..0000000 Binary files a/elsarticle-template-num.synctex.gz and /dev/null differ diff --git a/elsarticle-template-num.tex b/elsarticle-template-num.tex index 63e8018..05f7884 100644 --- a/elsarticle-template-num.tex +++ b/elsarticle-template-num.tex @@ -81,33 +81,41 @@ %% \address{Address\fnref{label3}} %% \fntext[label3]{} -\title{Distributed Lifetime Coverage Optimization Protocol in Wireless Sensor Networks} +\title{Distributed Lifetime Coverage Optimization Protocol \\ +in Wireless Sensor Networks} %% use optional labels to link authors explicitly to addresses: %% \author[label1,label2]{} %% \address[label1]{} %% \address[label2]{} -\author{Ali Kadhum Idrees, Karine Deschinkel, Michel Salomon, and Rapha\"el Couturier} +\author{Ali Kadhum Idrees, Karine Deschinkel, \\ +Michel Salomon, and Rapha\"el Couturier} %\thanks{are members in the AND team - DISC department - FEMTO-ST Institute, University of Franche-Comt\'e, Belfort, France. % e-mail: ali.idness@edu.univ-fcomte.fr, $\lbrace$karine.deschinkel, michel.salomon, raphael.couturier$\rbrace$@univ-fcomte.fr.}% <-this % stops a space %\thanks{}% <-this % stops a space - -\address{FEMTO-ST Institute, University of Franche-Comt\'e, Belfort, France. \\ e-mail: ali.idness@edu.univ-fcomte.fr, $\lbrace$karine.deschinkel, michel.salomon, raphael.couturier$\rbrace$@univ-fcomte.fr.} +\address{FEMTO-ST Institute, University of Franche-Comt\'e, Belfort, France. \\ +e-mail: ali.idness@edu.univ-fcomte.fr, \\ +$\lbrace$karine.deschinkel, michel.salomon, raphael.couturier$\rbrace$@univ-fcomte.fr.} \begin{abstract} -One of the fundamental challenges in Wireless Sensor Networks (WSNs) -is the coverage preservation and the extension of the network lifetime -continuously and effectively when monitoring a certain area (or -region) of interest. In this paper, a Distributed Lifetime Coverage Optimization Protocol (DiLCO) -to maintain the coverage and to improve the lifetime in wireless sensor networks is proposed. The area of interest is first divided into subregions using a divide-and-conquer method and then the DiLCO protocol is distributed on the sensor nodes in each subregion. The DiLCO combines two efficient techniques: Leader election for each subregion after that activity scheduling based optimization is planned for each subregion. The proposed -DiLCO works into rounds during which a small number of nodes, -remaining active for sensing, is selected to ensure coverage so as to maximize the lifetime of wireless sensor network. Each round consists of four phases: (i)~Information Exchange, (ii)~Leader -Election, (iii)~Decision, and (iv)~Sensing. The decision process is -carried out by a leader node, which solves an integer program. Compared with some existing -protocols, simulation results show that the proposed protocol can prolong the -network lifetime and improve the coverage performance effectively. - +One of the fundamental challenges in Wireless Sensor Networks (WSNs) is the +coverage preservation and the extension of the network lifetime continuously and +effectively when monitoring a certain area (or region) of interest. In this +paper, a Distributed Lifetime Coverage Optimization protocol (DiLCO) to maintain +the coverage and to improve the lifetime in wireless sensor networks is +proposed. The area of interest is first divided into subregions using a +divide-and-conquer method and then the DiLCO protocol is distributed on the +sensor nodes in each subregion. The DiLCO combines two efficient techniques: +leader election for each subregion, followed by an optimization-based planning +of activity scheduling decisions for each subregion. The proposed DiLCO works +into rounds during which a small number of nodes, remaining active for sensing, +is selected to ensure coverage so as to maximize the lifetime of wireless sensor +network. Each round consists of four phases: (i)~Information Exchange, +(ii)~Leader Election, (iii)~Decision, and (iv)~Sensing. The decision process is +carried out by a leader node, which solves an integer program. Compared with +some existing protocols, simulation results show that the proposed protocol can +prolong the network lifetime and improve the coverage performance effectively. \end{abstract} \begin{keyword} @@ -119,51 +127,61 @@ Optimization, Scheduling. \end{frontmatter} \section{Introduction} -\indent In the last years, there has been increasing development in wireless networking, -Micro-Electro-Mechanical Systems (MEMS), and embedded computing technologies, which are led to construct low-cost, small-sized and low-power sensor nodes that can perform detection, computation and data communication of surrounding environment. WSN -includes a large number of small, limited-power sensors that can -sense, process and transmit data over a wireless communication. They -communicate with each other by using multi-hop wireless communications, cooperate together to monitor the area of interest, -and the measured data can be reported to a monitoring center called sink -for analysis it~\cite{Sudip03}. There are several applications used the -WSN including health, home, environmental, military, and industrial -applications~\cite{Akyildiz02}. One of the major scientific research challenges in WSNs, which are addressed by a large number of literature during the last few years is to design energy efficient approches for coverage and connectivity in WSNs~\cite{conti2014mobile}.The coverage problem is one of the -fundamental challenges in WSNs~\cite{Nayak04} that consists in monitoring efficiently and continuously -the area of interest. The limited energy of sensors represents the main challenge in WSNs -design~\cite{Sudip03}, where it is impossible or inconvenient to replace and/or recharge their batteries because the the area of interest nature (such -as remote, hostile or unpractical environments) and the cost. So, it is necessary that a WSN -deployed with high density because spatial redundancy can then be -exploited to increase the lifetime of the network. However, turn on -all the sensor nodes, which monitor the same region at the same time -leads to decrease the lifetime of the network. To extend the lifetime -of the network, the main idea is to take advantage of the overlapping -sensing regions of some sensor nodes to save energy by turning off -some of them during the sensing phase~\cite{Misra05}. WSNs require -energy-efficient solutions to improve the network lifetime that is -constrained by the limited power of each sensor node ~\cite{Akyildiz02}. In this paper, we concentrate on the area -coverage problem, with the objective of maximizing the network -lifetime by using an adaptive scheduling. The area of interest is -divided into subregions and an activity scheduling for sensor nodes is -planned for each subregion. In fact, the nodes in a subregion can be -seen as a cluster where each node sends sensing data to the cluster head or the sink node. Furthermore, the activities in a -subregion/cluster can continue even if another cluster stops due to -too many node failures. Our scheduling scheme considers rounds, where -a round starts with a discovery phase to exchange information between -sensors of the subregion, in order to choose in a suitable manner a -sensor node to carry out a coverage strategy. This coverage strategy -involves the solving of an integer program, which provides the -activation of the sensors for the sensing phase of the current round. +\indent In the last years, there has been an increasing development in wireless +networking, Micro-Electro-Mechanical Systems (MEMS), and embedded computing +technologies, which have led to construct low-cost, small-sized, and low-power +sensor nodes that can perform detection, computation, and data communication of +surrounding environment. A WSN includes a large number of small, limited-power +sensors that can sense, process, and transmit data over a wireless +communication. They communicate with each other by using multi-hop wireless +communications and cooperate together to monitor the area of interest, so that +each measured data can be reported to a monitoring center called sink for +further analysis~\cite{Sudip03}. + +There are several fields of application covering a wide spectrum for a WSN, +including health, home, environmental, military, and industrial +applications~\cite{Akyildiz02}. One of the major 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}. On the one hand an optimal +coverage~\cite{Nayak04} is required to monitor efficiently and continuously the +area of interest and on the other hand the energy consumption must be as low as +possible, due to the limited energy of sensors~\cite{Sudip03} and the +impossibility or difficulty to replace and/or recharge their batteries because +of the area of interest nature (such as remote, hostile, or unpractical +environments) and the cost. So, it is of great relevance for a WSN to be +deployed with high density, because spatial redundancy can then be exploited to +increase the lifetime of the network. However, turning on all the sensor nodes +which monitor the same region at the same time reduces the the lifetime of the +network. Therefore, to extend the lifetime of the network, the main idea is to +take advantage of the overlapping sensing regions of some sensor nodes to save +energy by turning off some of them during the sensing phase~\cite{Misra05}. + +In this paper we concentrate on the area coverage problem with the objective of +maximizing the network lifetime by using an adaptive scheduling. The area of +interest is divided into subregions and an activity scheduling for sensor nodes +is planned for each subregion. In fact, the nodes in a subregion can be seen as +a cluster where each node sends sensing data to the cluster head or the sink +node. Furthermore, the activities in a subregion/cluster can continue even if +another cluster stops due to too many node failures. Our scheduling scheme +considers rounds, where a round starts with a discovery phase to exchange +information between sensors of the subregion, in order to choose in a suitable +manner a sensor node to carry out a coverage strategy. This coverage strategy +involves the solving of an integer program, which provides the activation of the +sensors for the sensing phase of the current round. The remainder of the paper is organized as follows. The next section % Section~\ref{rw} -reviews the related work in the field. In section~\ref{prel}, the problem definition and some background are described. Section~\ref{pd} is devoted to -the DiLCO Protocol Description. Section~\ref{cp} gives the coverage model formulation, which is used -to schedule the activation of sensors. Section~\ref{exp} shows the -simulation results obtained using the discrete event simulator OMNeT++ -\cite{varga}. They fully demonstrate the usefulness of the proposed -approach. Finally, we give concluding remarks and some suggestions -for future works in Section~\ref{sec:conclusion}. - +reviews the related work in the field. In section~\ref{prel}, the problem +definition and some background are described. Section~\ref{pd} is devoted to the +DiLCO protocol Description. Section~\ref{cp} gives the coverage model +formulation which is used to schedule the activation of sensors. +Section~\ref{exp} shows the simulation results obtained using the discrete event +simulator OMNeT++ \cite{varga}. They fully demonstrate the usefulness of the +proposed approach. Finally, we give concluding remarks and some suggestions for +future works in Section~\ref{sec:conclusion}. + +% MICHEL - OK up to here \section{Related works} \label{rw}