X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/LiCO.git/blobdiff_plain/7faa23683c6117ff229363e291bcc97046907e22..48963cfb012ce95bb5b2576804c8a22036d57748:/LiCO_Journal.tex

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@@ -1,12 +1,9 @@
-
 \documentclass[journal]{IEEEtran}
 
-
 \ifCLASSINFOpdf
 \else
-\fi
+\fi  
  
-
 \hyphenation{op-tical net-works semi-conduc-tor}
 \usepackage{float} 
 \usepackage{epsfig}
@@ -39,63 +36,112 @@
 \DeclareGraphicsRule{.ps}{pdf}{.pdf}{`ps2pdf -dEPSCrop -dNOSAFER #1 \noexpand\OutputFile}
 \begin{document}
 
-\title{Lifetime Coverage Optimization Protocol in Wireless Sensor Networks}  %LiCO Protocol
-
- 
+\title{Lifetime Coverage Optimization Protocol \\
+  in Wireless Sensor Networks}  %LiCO Protocol 
 
 \author{Ali Kadhum Idrees,~\IEEEmembership{}
         Karine Deschinkel,~\IEEEmembership{}
         Michel Salomon,~\IEEEmembership{}
         and~Rapha\"el Couturier ~\IEEEmembership{} 
-\thanks{The authors are with FEMTO-ST Institute, UMR 6174 CNRS, University of Franche-Comt\'e, Belfort, France. Email: ali.idness@edu.univ-fcomte.fr, $\lbrace$karine.deschinkel, michel.salomon, raphael.couturier$\rbrace$@univ-fcomte.fr}}
-%\thanks{J. Doe and J. Doe are with Anonymous University.}% <-this % stops a space
-%\thanks{Manuscript received April 19, 2005; revised December 27, 2012.}}
- 
-\markboth{IEEE Communications Letters,~Vol.~11, No.~4, December~2014}%
-{Shell \MakeLowercase{\textit{et al.}}: Bare Demo of IEEEtran.cls for Journals}
+        \thanks{The authors are with FEMTO-ST Institute, UMR 6174 CNRS, University of Franche-Comt\'e,
+          Belfort, France. Email: ali.idness@edu.univ-fcomte.fr, $\lbrace$karine.deschinkel,
+          michel.salomon, raphael.couturier$\rbrace$@univ-fcomte.fr}}
 
+\markboth{IEEE Communications Letters,~Vol.~XX, No.~Y, January 2015}%
+{Shell \MakeLowercase{\textit{et al.}}: Bare Demo of IEEEtran.cls for Journals}
 
 \maketitle
 
-
 \begin{abstract}
-
-
- One fundamental issue in Wireless Sensor Networks (WSNs) is the lifetime coverage optimization, which reflects how well a WSN is covered  so that the network lifetime can be maximized. In this paper, a Lifetime Coverage Optimization Protocol (LiCO) in WSNs is proposed. The surveillance region is divided into subregions and LiCO protocol is distributed among sensor nodes in each subregion. LiCO protocols works with periods, each period is divided into four stages: Information exchange, Leader Election, Optimization Decision, and Sensing. Schedules node activities (wakeup and sleep of sensors) is performed in each subregion by a leader whose selection is the result of cooperation between nodes within the same subregion. The novelty of approach lies essentially in the formulation of a new mathematical optimization model based on perimeter coverage level to schedule sensors activities. Extensive simulation experiments have been performed using OMNeT++, the discrete event simulator, to demonstrate that LiCO is capable to extend the lifetime coverage of WSN as longer time as possible in comparison with some other protocols.
-
-\end{abstract}
-
+The most important problem in Wireless Sensor Networks (WSNs) is to optimize the
+use of its limited energy provision, so  that it can fulfill its monitoring task
+as  long as  possible. Among  known  available approaches  that can  be used  to
+improve  power  management,  lifetime coverage  optimization  provides  activity
+scheduling which ensures  sensing coverage while minimizing the  energy cost. In
+this paper,  we propose  a such approach  called Lifetime  Coverage Optimization
+protocol (LiCO).   It is a  hybrid of  centralized and distributed  methods: the
+region of interest is first subdivided  into subregions and our protocol is then
+distributed among sensor nodes in each  subregion. A sensor node which runs LiCO
+protocol  repeats   periodically  four  stages:  information   exchange,  leader
+election, optimization decision, and sensing.  More precisely, the scheduling of
+nodes activities (sleep/wakeup  duty cycles) is achieved in each  subregion by a
+leader selected after  cooperation between nodes within the  same subregion. The
+novelty of  approach lies essentially in  the formulation of a  new mathematical
+optimization  model  based  on  perimeter coverage  level  to  schedule  sensors
+activities.  Extensive simulation experiments have been performed using OMNeT++,
+the  discrete event  simulator, to  demonstrate that  LiCO is  capable to  offer
+longer lifetime coverage for WSNs in comparison with some other protols.
+\end{abstract} 
 
 % Note that keywords are not normally used for peerreview papers.
 \begin{IEEEkeywords}
 Wireless Sensor Networks, Area Coverage, Network lifetime, Optimization, Scheduling.
 \end{IEEEkeywords}
 
-
 \IEEEpeerreviewmaketitle
 
-
-
-
-
 \section{\uppercase{Introduction}}
 \label{sec:introduction}
-\noindent The great development in  Micro Electro-Mechanical Systems (MEMS) and wireless communication hardware are being led to emerge networks of tiny distributed sensors called WSN~\cite{akyildiz2002wireless,puccinelli2005wireless}. WSN comprises of small, low-powered sensors working together for perform a typical mission by communicating with one another through multihop wireless connections. They can send the sensed measurements based on local decisions to the user by means of sink nodes. WSN has been used in many applications such as Military, Habitat, Environment, Health, industrial, and Business~\cite{yick2008wireless}. Typically, a sensor node contains three main parts~\cite{anastasi2009energy}: a sensing subsystem, for sense, measure, and gather the measurements from the real environment; processing subsystem, for measurements processing and storage; a communication subsystem, for data transmission and receiving. Moreover, the energy needed by the sensor node is supplied by a power supply, to accomplish the scheduled task. This power supply is composed of a battery with a limited lifetime. And it maybe be unsuitable or impossible to replace or recharge the batteries in most applications. It is then necessary to deploy the WSN with high density so as to increase the reliability and to exploit redundancy by using energy-efficient activity scheduling approaches. So, the main question is: how to extend the lifetime coverage of WSN as long time as possible while ensuring a high level of coverage? Many energy-efficient mechanisms have been suggested to retain energy and extend the lifetime of the WSNs~\cite{rault2014energy}. \\
+
+\noindent The continuous progress in Micro Electro-Mechanical Systems (MEMS) and
+wireless communication hardware  has given rise to the opportunity  to use large
+networks    of     tiny    sensors,    called    Wireless     Sensor    Networks
+(WSN)~\cite{akyildiz2002wireless,puccinelli2005wireless}, to  fulfill monitoring
+tasks.   A  WSN  consists  of  small low-powered  sensors  working  together  by
+communicating with one another through  multihop radio communications. Each node
+can send the data  it collects in its environment, thanks to  its sensor, to the
+user by means of  sink nodes. The features of a WSN made  it suitable for a wide
+range of application  in areas such as business,  environment, health, industry,
+military, and  son~\cite{yick2008wireless}.  Typically,  a sensor  node contains
+three main components~\cite{anastasi2009energy}: a  sensing unit able to measure
+physical,  chemical, or  biological  phenomena observed  in  the environment;  a
+processing  unit  which  will  process  and store  the  measurements  which  are
+collected; a radio communication unit for data transmission and receiving.
+
+The energy needed  by an active sensor node to  perform sensing, processing, and
+communication is supplied by a power supply which is a battery. This battery has
+a limited energy provision and it may  be unsuitable or impossible to replace or
+recharge it in  most applications. Therefore it is necessary  to deploy WSN with
+high density in order to increase the reliability and to exploit node redundancy
+thanks to energy-efficient activity  scheduling approaches.  Indeed, the overlap
+of sensing  areas can be exploited  to schedule alternatively some  sensors in a
+low power sleep mode and thus save  energy. Overall, the main question that must
+be answered is: how to extend the lifetime coverage of a WSN as long as possible
+while  ensuring   a  high  level   of  coverage?   So,  this  last   years  many
+energy-efficient mechanisms have been suggested  to retain energy and extend the
+lifetime of the WSNs~\cite{rault2014energy}.
 
 %The sensor system ought to have a lifetime long enough to satisfy the application necessities. The lifetime coverage maximization is one of the fundamental requirements of any area coverage protocol in WSN implementation~\cite{nayak2010wireless}. In order to increase the reliability and prevent the possession of coverage holes (some parts are not covered in the area of interest) in the WSN, it is necessary to deploy the WSN with high density so as to increase the reliability and to exploit redundancy by using energy-efficient activity scheduling approaches.
 
 %From a certain standpoint, the high coverage ratio is required by many applications such as military and health-care. Therefore, a suitable number of sensors are being chosen so as to cover the area of interest, is the first challenge. Meanwhile, the sensor nodes have a limited capabilities in terms of memory, processing, communication, and battery power being the most important and critical one.  So, the main question is: how to extend the lifetime coverage of WSN as long time as possible?. There are many energy-efficient mechanisms have been suggested to retain energy and extend the lifetime of the WSNs~\cite{rault2014energy}.
 
-%\uppercase{\textbf{Our contributions.}} 
-This paper makes the following contributions.\\
+%\uppercase{\textbf{Our contributions.}}
+
+% MICHEL - TO CONTINUED FROM HERE
+This paper makes the following contributions.
 \begin{enumerate}
-\item We devise a framework to schedules nodes to be activated alternatively, such that the network lifetime may be prolonged ans certain coverage requirement can still be met. 
-This framework is based on the division of the area of interest into several smaller subregions; on the division of timeline into periods of equal length. 
-One leader is elected for each subregion in an independent, distributed, and simultaneous way by the cooperation among the sensor nodes within each subregion, and this is similar to cluster architecture
-\item We propose a new mathematical optimization model. Instead of trying to cover a set of specified points/targets as in most of the methods proposed in the literature, 
-we formulate an integer program based on perimeter coverage of each sensor. The model involves integer variables to capture the deviations between the 
-actual level of coverage and the required level. And a weighted sum of these deviations is minimized. 
-\item We conducted extensive simulation experiments using the discrete event simulator OMNeT++, to demonstrate the efficiency of our protocol, compared to two approaches found in the literature, DESK \cite{ChinhVu} and  GAF \cite{xu2001geography}, and compared to our previous work using another optimization model for sensor scheduling \cite{Idrees2}. 
+\item  We  devise a  framework  to  schedules  nodes to  be  activated
+  alternatively, such that  the network lifetime may  be prolonged ans
+  certain coverage  requirement can still  be met.  This  framework is
+  based on the  division of the area of interest  into several smaller
+  subregions;  on  the division  of  timeline  into periods  of  equal
+  length.  One leader is elected for each subregion in an independent,
+  distributed,  and  simultaneous way  by  the  cooperation among  the
+  sensor nodes within  each subregion, and this is  similar to cluster
+  architecture
+\item We  propose a  new mathematical  optimization model.  Instead of
+  trying to cover a set of  specified points/targets as in most of the
+  methods proposed in the literature,  we formulate an integer program
+  based  on perimeter  coverage  of each  sensor.  The model  involves
+  integer variables to capture the deviations between the actual level
+  of coverage  and the  required level.  And a  weighted sum  of these
+  deviations is minimized.
+\item We conducted extensive simulation experiments using the discrete
+  event  simulator  OMNeT++,  to  demonstrate the  efficiency  of  our
+  protocol, compared to  two approaches found in  the literature, DESK
+  \cite{ChinhVu} and  GAF \cite{xu2001geography}, and compared  to our
+  previous work using another optimization model for sensor scheduling
+  \cite{Idrees2}.
 \end{enumerate}
 
 
@@ -103,9 +149,13 @@ actual level of coverage and the required level. And a weighted sum of these dev
 % the second, activity scheduling based new optimization model has been used to provide the optimal cover set that will take the mission of sensing during current period. This optimization algorithm is based on a perimeter-coverage model so as to optimize the shared perimeter among the sensors in each subregion, and this represents as a energu-efficient control topology mechanism in WSN.
 
 
-The remainder of the paper is organized as follows. The next section reviews  the related  work  in  the field.  Section~\ref{sec:The LiCO Protocol Description} is devoted to the LiCO protocol Description. Section~\ref{cp}  gives the coverage model
-formulation which is used to schedule the activation of sensors.
-Section~\ref{sec:Simulation Results and Analysis} presents simulations results. Finally, we give concluding remarks and some suggestions for
+The remainder of  the paper is organized as follows.  The next section
+reviews  the related  work  in the  field.  Section~\ref{sec:The  LiCO
+  Protocol   Description}   is   devoted    to   the   LiCO   protocol
+Description.  Section~\ref{cp} gives  the  coverage model  formulation
+which   is    used   to   schedule   the    activation   of   sensors.
+Section~\ref{sec:Simulation Results and Analysis} presents simulations
+results. Finally, we give concluding  remarks and some suggestions for
 future works in Section~\ref{sec:Conclusion and Future Works}.
 
 % that show that our protocol outperforms others protocols.
