X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/JournalMultiRounds.git/blobdiff_plain/c48058fb972ae360e7cd3b759862db6055e16cb5..refs/heads/master:/elsarticle-template-num.tex?ds=sidebyside

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}
