X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/JournalMultiRounds.git/blobdiff_plain/1e248125c11122508fff14c90cf49b57b1e4622f..refs/heads/master:/elsarticle-template-num.tex 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}