Michel : Modifs jusqu'à la section III. A incluse

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+\def\bsq#1{%both single quotes
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-\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{} 
 
 \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
 
 
 \maketitle
 

-

 \begin{abstract}
 \begin{abstract}

-
-
- One fundamental issue in Wireless Sensor Networks (WSNs) is the lifetime coverage optimization, which reflects how well a WSN is covered by a wireless sensors so that the network lifetime can be maximized. In this paper, a Lifetime Coverage Optimization Protocol (LiCO) in WSNs is proposed. The network is logically divided into subregions using divide-and-conquer method. LiCO protocol is distributed in each sensor node in the subregion. The lifetime coverage is divided into four stages: Information exchange, Leader Election, Optimization Decision, and Sensing. The optimization decision is made at each subregion, by a leader, who his election comes from the cooperation of the sensor nodes within the same subregion of WSN. A new mathematical optimization model is proposed to optimize the lifetime coverage in each subregion. 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/wake up 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 protocols.
+\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}
 
 
 % Note that keywords are not normally used for peerreview papers.
 \begin{IEEEkeywords}
 Wireless Sensor Networks, Area Coverage, Network lifetime, Optimization, Scheduling.
 \end{IEEEkeywords}
 

-

 \IEEEpeerreviewmaketitle
 
 \IEEEpeerreviewmaketitle
 

+\section{Introduction}
+\label{sec:introduction}

 
 

+\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 collected measurements; 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.
+\begin{enumerate}
+\item We devise a framework to schedule nodes to be activated alternatively such
+  that the network lifetime is prolonged  while ensuring that a certain level of
+  coverage is preserved.  A  key idea in our framework is  to exploit spatial an
+  temporal subdivision.   On the one hand  the area of interest  if divided into
+  several smaller subregions and on the other hand the time line is divided into
+  periods of equal length. In each subregion the sensor nodes will cooperatively
+  choose a  leader which will  schedule nodes  activities, and this  grouping of
+  sensors is similar to typical 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.  So  that an
+  optimal scheduling  will be  obtained by  minimizing a  weighted sum  of these
+  deviations.
+\item We  conducted extensive simulation  experiments, using the  discrete event
+  simulator OMNeT++, to demonstrate the  efficiency of our protocol. We compared
+  our   LiCO   protocol   to   two   approaches   found   in   the   literature:
+  DESK~\cite{ChinhVu} and  GAF~\cite{xu2001geography}, and also to  our previous
+  work published in~\cite{Idrees2} which is  based on another optimization model
+  for sensor scheduling.
+\end{enumerate}
+
+%Two combined integrated energy-efficient techniques have been used by LiCO protocol in order to maximize the lifetime coverage in WSN: the first, by dividing the area of interest into several smaller subregions based on divide-and-conquer method and then one leader elected for each subregion in an independent, distributed, and simultaneous way by the cooperation among the sensor nodes within each subregion, and this similar to cluster architecture;
+% 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 rest  of the paper is  organized as follows.  In the next section  we review
+some related work in the  field. Section~\ref{sec:The LiCO Protocol Description}
+is devoted to the LiCO protocol  description and Section~\ref{cp} focuses on the
+coverage model  formulation which is used  to schedule the activation  of sensor
+nodes.  Section~\ref{sec:Simulation  Results and Analysis}  presents simulations
+results and discusses the comparison  with other approaches. Finally, concluding
+remarks   are  drawn   and  some   suggestions   given  for   future  works   in
+Section~\ref{sec:Conclusion and Future Works}.
+
+% that show that our protocol outperforms others protocols.
+\section{Related Literature}
+\label{sec:Literature Review}

 
 

+\noindent  In  this section,  we  summarize  some  related works  regarding  the
+coverage problem and  distinguish our LiCO protocol from the  works presented in
+the literature.
+
+The most  discussed coverage problems in  literature can be classified  in three
+categories~\cite{li2013survey}   according   to  their   respective   monitoring
+objective.  Hence,  area coverage \cite{Misra}  means that every point  inside a
+fixed area  must be monitored, while  target coverage~\cite{yang2014novel} refer
+to  the objective  of coverage  for a  finite number  of discrete  points called
+targets,  and  barrier coverage~\cite{HeShibo}\cite{kim2013maximum}  focuses  on
+preventing  intruders   from  entering   into  the   region  of   interest.   In
+\cite{Deng2012}  authors  transform the  area  coverage  problem to  the  target
+coverage one taking into account the  intersection points among disks of sensors
+nodes    or   between    disk   of    sensor   nodes    and   boundaries.     In
+\cite{Huang:2003:CPW:941350.941367}  authors prove  that  if  the perimeters  of
+sensors are sufficiently  covered it will be  the case for the  whole area. They
+provide an algorithm in $O(nd~log~d)$  time to compute the perimeter-coverage of
+each  sensor,  where  $d$  denotes  the  maximum  number  of  sensors  that  are
+neighboring  to  a  sensor and  $n$  is  the  total  number of  sensors  in  the
+network. {\it In LiCO protocol, instead  of determining the level of coverage of
+  a set  of discrete  points, our  optimization model is  based on  checking the
+  perimeter-coverage of each sensor to activate a minimal number of sensors.}
+
+The major  approach to extend network  lifetime while preserving coverage  is to
+divide/organize the  sensors into a suitable  number of set covers  (disjoint or
+non-disjoint), where  each set completely  covers a  region of interest,  and to
+activate these set  covers successively. The network activity can  be planned in
+advance and scheduled  for the entire network lifetime or  organized in periods,
+and the set  of active sensor nodes  is decided at the beginning  of each period
+\cite{ling2009energy}.  Active node selection is determined based on the problem
+requirements (e.g.   area monitoring,  connectivity, or power  efficiency).  For
+instance, Jaggi {\em et al.}~\cite{jaggi2006}  address the problem of maximizing
+the lifetime  by dividing sensors  into the  maximum number of  disjoint subsets
+such  that each  subset  can ensure  both coverage  and  connectivity. A  greedy
+algorithm  is applied  once to  solve  this problem  and the  computed sets  are
+activated  in   succession  to  achieve   the  desired  network   lifetime.   Vu
+\cite{chin2007},  Padmatvathy  {\em   et  al.}~\cite{pc10},  propose  algorithms
+working in a periodic fashion where a  cover set is computed at the beginning of
+each period.   {\it Motivated by  these works,  LiCO protocol works  in periods,
+  where each  period contains a  preliminary phase for information  exchange and
+  decisions, followed by a sensing phase where one cover set is in charge of the
+  sensing task.}
+
+Various centralized  and distributed approaches, or  even a mixing of  these two
+concepts, have  been proposed  to extend the  network lifetime.   In distributed
+algorithms~\cite{yangnovel,ChinhVu,qu2013distributed}  each  sensors decides  of
+its own  activity scheduling after  an information exchange with  its neighbors.
+The main interest of  a such approach is to avoid  long range communications and
+thus to reduce the energy dedicated to the communications.  Unfortunately, since
+each node has  only information on its immediate neighbors  (usually the one-hop
+ones)  it may  take a  bad  decision leading  to a  global suboptimal  solution.
+Conversely,                                                          centralized
+algorithms~\cite{cardei2005improving,zorbas2010solving,pujari2011high}    always
+provide nearly  or close to  optimal solution since  the algorithm has  a global
+view of the whole network. The disadvantage of a centralized method is obviously
+its high cost  in communications needed to  transmit to a single  node, the base
+station which will  globally schedule nodes activities, data from  all the other
+sensor nodes in  the area.  The price  in communications can be  very huge since
+long range communications will be needed. In fact the larger the WNS, the higher
+the  communication and  thus energy  cost.   {\it In  order to  be suitable  for
+  large-scale networks,  in the LiCO  protocol the  area of interest  is divided
+  into several smaller subregions, and in each  one, a node called the leader is
+  in charge  for selecting the active  sensors for the current  period. Thus our
+  protocol  is  scalable  and  a  globally distributed  method,  whereas  it  is
+  centralized in each subregion.}
+
+Various  coverage scheduling  algorithms have  been developed  this last  years.
+Many of  them, dealing with  the maximization of the  number of cover  sets, are
+heuristics.   These  heuristics involve  the  construction  of  a cover  set  by
+including in priority the sensor nodes  which cover critical targets, that is to
+say   targets   that  are   covered   by   the   smallest  number   of   sensors
+\cite{berman04,zorbas2010solving}.  Other  approaches are based  on mathematical
+programming formulations~\cite{cardei2005energy,5714480,pujari2011high,Yang2014}
+and dedicated techniques (solving with a branch-and-bound algorithm available in
+optimization  solver).  The  problem is  formulated as  an optimization  problem
+(maximization of the lifetime or number of cover sets) under target coverage and
+energy  constraints.   Column  generation   techniques,  well-known  and  widely
+practiced techniques for  solving linear programs with too  many variables, have
+also                                                                        been
+used~\cite{castano2013column,rossi2012exact,deschinkel2012column}. {\it  In LiCO
+  protocol, each  leader, in charge  of a  subregion, solves an  integer program
+  which has a twofold objective: minimize the overcoverage and the undercoverage
+  of the perimeter of each sensor.}
+
+%\noindent Recently, the coverage problem has been received a high attention, which concentrates on how the physical space could be well monitored  after the deployment. Coverage is one of the Quality of Service (QoS) parameters in WSNs, which is highly concerned with power depletion~\cite{zhu2012survey}. Most of the works about the coverage protocols have been suggested in the literature focused on three types of the coverage in WSNs~\cite{mulligan2010coverage}: the first, area coverage means that each point in the area of interest within the sensing range of at least one sensor node; the second, target coverage in which a fixed set of targets need to be monitored; the third, barrier coverage refers to detect the intruders crossing a boundary of WSN. The work in this paper emphasized on the area coverage, so,  some area coverage protocols have been reviewed in this section, and the shortcomings of reviewed approaches are being summarized.
+
+%The problem of k-coverage in WSNs was addressed~\cite{ammari2012centralized}. It mathematically formulated and the spacial sensor density for full k-coverage determined, where the relation between the communication range and the sensing range constructed by this work to retain the k-coverage and connectivity in WSN. After that, a four configuration protocols have proposed for treating the k-coverage in WSNs.  
+
+%In~\cite{rebai2014branch}, the problem of full grid coverage is formulated using two integer linear programming models: the first, a model that takes into account only the overall coverage constraint; the second, both the connectivity and the full grid coverage constraints have taken into consideration. This work did not take into account the energy constraint.
+
+%Li et al.~\cite{li2011transforming} presented a framework to convert any complete coverage problem to a partial coverage one with any coverage ratio by means of executing a complete coverage algorithm to find a full coverage sets with virtual radii and transforming the coverage sets to a partial coverage sets by adjusting sensing radii.  The properties of the original algorithms can be maintained by this framework and the transformation process has a low execution time.
+
+%The authors in~\cite{liu2014generalized} explained that in some applications of WSNs such as structural health monitoring (SHM) and volcano monitoring, the traditional coverage model which is a geographic area defined for individual sensors is not always valid. For this reason, they define a generalized coverage model, which is not need to have the coverage area of individual nodes, but only based on a function to determine whether a set of
+%sensor nodes is capable of satisfy the requested monitoring task for a certain area. They have proposed two approaches to divide the deployed nodes into suitable cover sets, which can be used to prolong the network lifetime. 
+ 
+%The work in~\cite{wang2010preserving} addressed the target area coverage problem by proposing a geometric-based activity scheduling scheme, named GAS, to fully cover the target area in WSNs. The authors deals with small area (target area coverage), which can be monitored by a single sensor instead of area coverage, which focuses on a large area that should be monitored by many sensors cooperatively. They explained that GAS is capable to monitor the target area by using a few sensors as possible and it can produce as many cover sets as possible.

 
 

+%Cho et al.~\cite{cho2007distributed} proposed a distributed node scheduling protocol, which can retain sensing coverage needed by applications
+%and increase network lifetime via putting in sleep mode some redundant nodes. In this work, the effective sensing area (ESA) concept of a sensor node is used, which refers to the sensing area that is not overlapping with another sensor's sensing area. A sensor node and by compute it's ESA can be determine whether it will be active or sleep. The suggested  work permits to sensor nodes to be in sleep mode opportunistically whilst fulfill the needed sensing coverage.
+ 
+%In~\cite{quang2008algorithm}, the authors defined a maximum sensing coverage region problem (MSCR) in WSNs and then proposed an algorithm to solve it. The
+%maximum observed area fully covered by a minimum active sensors. In this work, the major property is to getting rid from the redundant sensors  in high-density WSNs and putting them in sleep mode, and choosing a smaller number of active sensors so as to be sure  that the full area is k-covered, and all events appeared in that area can be precisely and timely detected. This algorithm minimized the total energy consumption and increased the lifetime.

 
 

-\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. Furthermore, it maybe be unsuitable or impossible to replace or recharge the batteries, since sensor nodes may be deployed in a hostile or unpractical environment. 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.
+%A novel method to divide the sensors in the WSN, called node coverage grouping (NCG) suggested~\cite{lin2010partitioning}. The sensors in the connectivity group are within sensing range of each other, and the data collected by them in the same group are supposed to be similar. They are proved that dividing n sensors via NCG into connectivity groups is a NP-hard problem. So, a heuristic algorithm of NCG with time complexity of $O(n^3)$ is proposed.
+%For some applications, such as monitoring an ecosystem with extremely diversified environment, It might be premature assumption that sensors near to each other sense similar data.

 
 

-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}.
+%In~\cite{zaidi2009minimum}, the problem of minimum cost coverage in which full coverage is performed by using the minimum number of sensors for an arbitrary geometric shape region is addressed.  a geometric solution to the minimum cost coverage problem under a deterministic deployment is proposed. The probabilistic coverage solution which provides a relationship between the probability of coverage and the number of randomly deployed sensors in an arbitrarily-shaped region is suggested. The authors are clarified that with a random deployment about seven times more nodes are required to supply full coverage.

 
 

-\uppercase{\textbf{Our contributions.}} Two combined integrated energy-efficient techniques have been used by LiCO protocol in order to maximize the lifetime coverage in WSN: the first, by dividing the area of interest into several smaller subregions based on divide-and-conquer method and then one leader elected for each subregion in an independent, distributed, and simultaneous way by the cooperation among the sensor nodes within each subregion, and this similar to cluster architecture; 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.
+%A graph theoretical framework for connectivity-based coverage with configurable coverage granularity was proposed~\cite{dong2012distributed}. A new coverage criterion and scheduling approach is proposed based on cycle partition. This method is capable of build a sparse coverage set in distributed way by means of only connectivity information. This work considers only the communication range of the sensor is smaller two times the sensing range of sensor.

 
 

+%Liu et al.~\cite{liu2010energy} formulated maximum disjoint sets problem for retaining coverage and connectivity in WSN. Two algorithms are proposed for solving this problem, heuristic algorithm and network flow algorithm. This work did not take into account the sensor node failure, which is an unpredictable event because the two solutions are full centralized algorithms.

 
 

-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} shows the simulation results.  Finally, we give concluding remarks and some suggestions for
-future works in Section~\ref{sec:Conclusion and Future Works}.
+%The work that presented in~\cite{aslanyan2013optimal} solved the coverage and connectivity problem in sensor networks in
+%an integrated way. The network lifetime is divided in a fixed number of rounds. A coverage bitmap of sensors of the domain has been generated in each round and based on this bitmap,  it has been decided which sensors
+%stay active or turn it to sleep. They checked the connection of the graph via laplacian of adjancy graph of active sensors in each round.  the generation of coverage bitmap by using  Minkowski technique, the network is able to providing the desired ratio of coverage. They have been defined the  connected coverage problem as an optimization problem and a centralized genetic algorithm is used to find the solution.

 
 

-\section{\uppercase{Related Literature}}
-\label{sec:Literature Review}
-\noindent Recently, the coverage problem has been received a high attention, which concentrates on how the physical space could be well monitored  after the deployment. Coverage is one of the Quality of Service (QoS) parameters in WSNs, which is highly concerned with power depletion~\cite{zhu2012survey}. Most of the works about the coverage protocols have been suggested in the literature focused on three types of the coverage in WSNs~\cite{mulligan2010coverage}: the first, area coverage means that each point in the area of interest within the sensing range of at least one sensor node; the second, target coverage in which a fixed set of targets need to be monitored; the third, barrier coverage refers to detect the intruders crossing a boundary of WSN. The work in this paper emphasized on the area coverage, so,  some area coverage protocols have been reviewed in this section, and the shortcomings of reviewed approaches are being summarized.
+%Several algorithms to retain the coverage and maximize the network lifetime were proposed in~\cite{cardei2006energy,wang2011coverage}. 

 
 

-The problem of k-coverage in WSNs was addressed~\cite{ammari2012centralized}. It mathematically formulated and the spacial sensor density for full k-coverage determined, where the relation between the communication range and the sensing range constructed by this work to retain the k-coverage and connectivity in WSN. After that, a four configuration protocols have proposed for treating the k-coverage in WSNs.  
+%\uppercase{\textbf{shortcomings}}. In spite of many energy-efficient protocols for maintaining the coverage and improving the network lifetime in WSNs were proposed, non of them ensure the coverage for the sensing field with optimal minimum number of active sensor nodes, and for a long time as possible. For example, in a full centralized algorithms, an optimal solutions can be given by using optimization approaches, but in the same time, a high energy is consumed for the execution time of the algorithm and the communications among the sensors in the sensing field, so, the  full centralized approaches are not good candidate to use it especially in large WSNs. Whilst, a full distributed algorithms can not give optimal solutions because this algorithms use only local information of the neighboring sensors, but in the same time, the energy consumption during the communications and executing the algorithm is highly lower. Whatever the case, this would result in a shorter lifetime coverage in WSNs.

 
 

-In~\cite{rebai2014branch}, the problem of full grid coverage is formulated using two integer linear programming models: the first, a model that takes into account only the overall coverage constraint; the second, both the connectivity and the full grid coverage constraints have taken into consideration. This work did not take into account the energy constraint.
+%\uppercase{\textbf{Our Protocol}}. In this paper, a Lifetime Coverage Optimization Protocol, called (LiCO) in WSNs is suggested. The sensing field is divided into smaller subregions by means of divide-and-conquer method, and a LiCO protocol is distributed in each sensor in the subregion. The network lifetime in each subregion is divided into periods, each period includes 4 stages: Information Exchange, Leader election, decision based activity scheduling optimization, and sensing. The leaders are elected in an independent, asynchronous, and distributed way in all the subregions of the WSN. After that, energy-efficient activity scheduling mechanism based new optimization model is performed by each leader in the subregions. This optimization model is based on the perimeter coverage model in order to producing the optimal cover set of active sensors, which are taken the responsibility of sensing during the current period. LiCO protocol merges between two energy efficient mechanisms, which are used the main advantages of the centralized and distributed approaches and avoids the most of their disadvantages.

 
 

-Li et al.~\cite{li2011transforming} presented a framework to convert any complete coverage problem to a partial coverage one with any coverage ratio by means of executing a complete coverage algorithm to find a full coverage sets with virtual radii and transforming the coverage sets to a partial coverage sets by adjusting sensing radii.  The properties of the original algorithms can be maintained by this framework and the transformation process has a low execution time.
+\section{ The LiCO Protocol Description}
+\label{sec:The LiCO Protocol Description}

 
 

-The authors in~\cite{liu2014generalized} explained that in some applications of WSNs such as structural health monitoring (SHM) and volcano monitoring, the traditional coverage model which is a geographic area defined for individual sensors is not always valid. For this reason, they define a generalized coverage model, which is not need to have the coverage area of individual nodes, but only based on a function to determine whether a set of
-sensor nodes is capable of satisfy the requested monitoring task for a certain area. They have proposed two approaches to divide the deployed nodes into suitable cover sets, which can be used to prolong the network lifetime. 
- 
-The work in~\cite{wang2010preserving} addressed the target area coverage problem by proposing a geometric-based activity scheduling scheme, named GAS, to fully cover the target area in WSNs. The authors deals with small area (target area coverage), which can be monitored by a single sensor instead of area coverage, which focuses on a large area that should be monitored by many sensors cooperatively. They explained that GAS is capable to monitor the target area by using a few sensors as possible and it can produce as many cover sets as possible.
+\noindent  In  this  section,  we  describe in  details  our  Lifetime  Coverage
+Optimization protocol.  First we present the  assumptions we made and the models
+we considered (in particular the perimeter coverage one), second we describe the
+background idea of our protocol, and third  we give the outline of the algorithm
+executed by each node.

 
 

-Cho et al.~\cite{cho2007distributed} proposed a distributed node scheduling protocol, which can retain sensing coverage needed by applications
-and increase network lifetime via putting in sleep mode some redundant nodes. In this work, the effective sensing area (ESA) concept of a sensor node is used, which refers to the sensing area that is not overlapping with another sensor's sensing area. A sensor node and by compute it's ESA can be determine whether it will be active or sleep. The suggested  work permits to sensor nodes to be in sleep mode opportunistically whilst fulfill the needed sensing coverage.
- 
-In~\cite{quang2008algorithm}, the authors defined a maximum sensing coverage region problem (MSCR) in WSNs and then proposed an algorithm to solve it. The
-maximum observed area fully covered by a minimum active sensors. In this work, the major property is to getting rid from the redundant sensors  in high-density WSNs and putting them in sleep mode, and choosing a smaller number of active sensors so as to be sure  that the full area is k-covered, and all events appeared in that area can be precisely and timely detected. This algorithm minimized the total energy consumption and increased the lifetime.
+% It is based on two efficient-energy mechanisms: the first, is partitioning the sensing field into smaller subregions, and one leader is elected for each subregion;  the second, a sensor activity scheduling based new optimization model so as to produce the optimal cover set of active sensors for the sensing stage during the period.  Obviously, these two mechanisms can be contribute in extend the network lifetime coverage efficiently. 
+%Before proceeding in the presentation of the main ideas of the protocol, we will briefly describe the perimeter coverage model and give some necessary assumptions and definitions.

 
 

-A novel method to divide the sensors in the WSN, called node coverage grouping (NCG) suggested~\cite{lin2010partitioning}. The sensors in the connectivity group are within sensing range of each other, and the data collected by them in the same group are supposed to be similar. They are proved that dividing n sensors via NCG into connectivity groups is a NP-hard problem. So, a heuristic algorithm of NCG with time complexity of $O(n^3)$ is proposed.
-For some applications, such as monitoring an ecosystem with extremely diversified environment, It might be premature assumption that sensors near to each other sense similar data.
+\subsection{Assumptions and Models}
+
+\noindent A WSN consisting of $J$ stationary sensor nodes randomly and uniformly
+distributed in  a bounded sensor field  is considered. The wireless  sensors are
+deployed in high density  to ensure initially a high coverage  ratio of the area
+of interest.  We  assume that all the  sensor nodes are homogeneous  in terms of
+communication,  sensing,  and  processing capabilities  and  heterogeneous  from
+energy provision  point of  view.  The  location information  is available  to a
+sensor node either  through hardware such as embedded GPS  or location discovery
+algorithms.   We  assume  that  each  sensor  node  can  directly  transmit  its
+measurements to  a mobile  sink node.  For  example, a sink  can be  an unmanned
+aerial  vehicle  (UAV)  flying  regularly  over  the  sensor  field  to  collect
+measurements from sensor nodes. A mobile sink node collects the measurements and
+transmits them to the base station.   We consider a Boolean disk coverage model,
+which is the most  widely used sensor coverage model in  the literature, and all
+sensor nodes  have a constant sensing  range $R_s$.  Thus, all  the space points
+within a disk centered at a sensor with  a radius equal to the sensing range are
+said to be covered  by this sensor. We also assume  that the communication range
+$R_c$ satisfies $R_c  \geq 2 \cdot R_s$. In fact,  Zhang and Zhou~\cite{Zhang05}
+proved  that if  the  transmission  range fulfills  the  previous hypothesis,  a
+complete coverage of a convex area implies connectivity among active nodes.
+
+\indent  LiCO protocol  uses the  same perimeter-coverage  model than  Huang and
+Tseng in~\cite{huang2005coverage}. It  can be expressed as follows:  a sensor is
+said to be perimeter  covered if all the points on its  perimeter are covered by
+at least  one sensor  other than  itself.  They  proved that  a network  area is
+$k$-covered if and only if each sensor in the network is $k$-perimeter-covered.
+%According to this model, we named the intersections among the sensor nodes in the sensing field as intersection points. Instead of working with the coverage area, we consider for each sensor a set of intersection points which are determined by using perimeter-coverage model. 
+Figure~\ref{pcm2sensors}(a)  shows  the coverage  of  sensor  node~$0$. On  this
+figure, we can  see that sensor~$0$ has  nine neighbors and we  have reported on
+its  perimeter (the  perimeter  of the  disk  covered by  the  sensor) for  each
+neighbor  the  two  points  resulting  from  intersection  of  the  two  sensing
+areas. These points are denoted for  neighbor~$i$ by $iL$ and $iR$, respectively
+for  left and  right from  neighbor  point of  view.  The  resulting couples  of
+intersection points subdivide  the perimeter of sensor~$0$  into portions called
+arcs.

 
 

-In~\cite{zaidi2009minimum}, the problem of minimum cost coverage in which full coverage is performed by using the minimum number of sensors for an arbitrary geometric shape region is addressed.  a geometric solution to the minimum cost coverage problem under a deterministic deployment is proposed. The probabilistic coverage solution which provides a relationship between the probability of coverage and the number of randomly deployed sensors in an arbitrarily-shaped region is suggested. The authors are clarified that with a random deployment about seven times more nodes are required to supply full coverage.
+\begin{figure}[ht!]
+  \centering
+  \begin{tabular}{@{}cr@{}}
+    \includegraphics[width=75mm]{pcm.jpg}        &        \raisebox{3.25cm}{(a)}
+    \\ \includegraphics[width=75mm]{twosensors.jpg} & \raisebox{2.75cm}{(b)}
+  \end{tabular}
+  \caption{Perimeter coverage of sensor node 0  (a) and finding the arc of $u$'s
+    perimeter covered by $v$.}
+  \label{pcm2sensors}
+\end{figure} 

 
 

-A graph theoretical framework for connectivity-based coverage with configurable coverage granularity was proposed~\cite{dong2012distributed}. A new coverage criterion and scheduling approach is proposed based on cycle partition. This method is capable of build a sparse coverage set in distributed way by means of only connectivity information. This work considers only the communication range of the sensor is smaller two times the sensing range of sensor.
+Figure~\ref{pcm2sensors}(b) describes the geometric information used to find the
+locations of the  left and right points of  an arc on the perimeter  of a sensor
+node~$u$ covered by a sensor node~$v$. Node~$s$ is supposed to be located on the
+west  side of  sensor~$u$,  with  the following  respective  coordinates in  the
+sensing area~: $(v_x,v_y)$ and $(u_x,u_y)$. From the previous coordinates we can
+compute the euclidean distance between nodes~$u$ and $v$: $Dist(u,v)=\sqrt{\vert
+  u_x  - v_x  \vert^2 +  \vert u_y-v_y  \vert^2}$, while  the angle~$\alpha$  is
+obtained  through the  formula  $\alpha  = arccos  \left(\dfrac{Dist(u,v)}{2R_s}
+\right)$.   So, the  arc on  the perimeter  of node~$u$  defined by  the angular
+interval $[\pi - \alpha,\pi + \alpha]$ is said to be perimeter-covered by sensor
+node $v$.
+
+Every couple of intersection points is placed on the angular interval $[0,2\pi]$
+in  a  counterclockwise manner,  leading  to  a  partitioning of  the  interval.
+Figure~\ref{pcm2sensors}(a)  illustrates  the arcs  for  the  nine neighbors  of
+sensor $0$ and  figure~\ref{expcm} gives the position of  the corresponding arcs
+in  the interval  $[0,2\pi]$. More  precisely, we  can see  that the  points are
+ordered according  to the  measures of  the angles  defined by  their respective
+positions. The intersection points are  then visited one after another, starting
+from  first  intersection point  after  point~zero,  and  the maximum  level  of
+coverage is determined  for each interval defined by two  successive points. The
+maximum  level of  coverage is  equal to  the number  of overlapping  arcs.  For
+example, between~$5L$  and~$6L$ the maximum  level of  coverage is equal  to $3$
+(the value is highlighted in yellow  at the bottom of figure~\ref{expcm}), which
+means that at most 2~neighbors can cover  the perimeter in addition to node $0$.
+Table~\ref{my-label} summarizes for each coverage  interval the maximum level of
+coverage and  the sensor  nodes covering the  perimeter.  The  example discussed
+above is thus given by the sixth line of the table.
+
+%The points reported on the line segment $[0,2\pi]$ separates it in intervals as shown in figure~\ref{expcm}. For example, for each neighboring sensor of sensor 0, place the points  $\alpha^ 1_L$, $\alpha^ 1_R$, $\alpha^ 2_L$, $\alpha^ 2_R$, $\alpha^ 3_L$, $\alpha^ 3_R$, $\alpha^ 4_L$, $\alpha^ 4_R$, $\alpha^ 5_L$, $\alpha^ 5_R$, $\alpha^ 6_L$, $\alpha^ 6_R$, $\alpha^ 7_L$, $\alpha^ 7_R$, $\alpha^ 8_L$, $\alpha^ 8_R$, $\alpha^ 9_L$, and $\alpha^ 9_R$; on the line segment $[0,2\pi]$, and then sort all these points in an ascending order into a list.  Traverse the line segment $[0,2\pi]$ by visiting each point in the sorted list from left to right and determine the coverage level of each interval of the sensor 0 (see figure \ref{expcm}). For each interval, we sum up the number of parts of segments, and we deduce a level of coverage for each interval. For instance, the interval delimited by the points $5L$ and $6L$ contains three parts of segments. That means that this part of the perimeter of the sensor $0$ may be covered by three sensors, sensor $0$ itself and sensors $2$ and $5$. The level of coverage of this interval may reach $3$ if all previously mentioned sensors are active. Let say that sensors $0$, $2$ and $5$ are involved in the coverage of this interval. Table~\ref{my-label} summarizes the level of coverage for each interval and the sensors involved in for sensor node 0 in figure~\ref{pcm2sensors}(a). 
+% to determine the level of the perimeter coverage for each left and right point of a segment.
+
+\begin{figure*}[ht!]
+\centering
+\includegraphics[width=137.5mm]{expcm.pdf}  
+\caption{Maximum coverage levels for perimeter of sensor node $0$.}
+\label{expcm}
+\end{figure*} 

 
 

-Liu et al.~\cite{liu2010energy} formulated maximum disjoint sets problem for retaining coverage and connectivity in WSN. Two algorithms are proposed for solving this problem, heuristic algorithm and network flow algorithm. This work did not take into account the sensor node failure, which is an unpredictable event because the two solutions are full centralized algorithms.
+%For example, consider the sensor node $0$ in figure~\ref{pcmfig}, which has 9 neighbors. Figure~\ref{expcm} shows the perimeter coverage level for all left and right points of a segment that covered by a neighboring sensor nodes. Based on the figure~\ref{expcm}, the set of sensors for each left and right point of the segments illustrated in figure~\ref{ex2pcm} for the sensor node 0.

 
 

-The work that presented in~\cite{aslanyan2013optimal} solved the coverage and connectivity problem in sensor networks in
-an integrated way. The network lifetime is divided in a fixed number of rounds. A coverage bitmap of sensors of the domain has been generated in each round and based on this bitmap,  it has been decided which sensors
-stay active or turn it to sleep. They checked the connection of the graph via laplacian of adjancy graph of active sensors in each round.  the generation of coverage bitmap by using  Minkowski technique, the network is able to providing the desired ratio of coverage. They have been defined the  connected coverage problem as an optimization problem and a centralized genetic algorithm is used to find the solution.
+\iffalse

 
 

-Several algorithms to retain the coverage and maximize the network lifetime were proposed in~\cite{cardei2006energy,wang2011coverage}. 
+\begin{figure}[ht!]
+\centering
+\includegraphics[width=90mm]{ex2pcm.jpg}  
+\caption{Coverage intervals and contributing sensors for sensor node 0.}
+\label{ex2pcm}
+\end{figure} 

 
 

-\uppercase{\textbf{shortcomings}}. In spite of many energy-efficient protocols for maintaining the coverage and improving the network lifetime in WSNs were proposed, non of them ensure the coverage for the sensing field with optimal minimum number of active sensor nodes, and for a long time as possible. For example, in a full centralized algorithms, an optimal solutions can be given by using optimization approaches, but in the same time, a high energy is consumed for the execution time of the algorithm and the communications among the sensors in the sensing field, so, the  full centralized approaches are not good candidate to use it especially in large WSNs. Whilst, a full distributed algorithms can not give optimal solutions because this algorithms use only local information of the neighboring sensors, but in the same time, the energy consumption during the communications and executing the algorithm is highly lower. Whatever the case, this would result in a shorter lifetime coverage in WSNs.
+\fi

 
 

-\uppercase{\textbf{Our Protocol}}. In this paper, a Lifetime Coverage Optimization Protocol, called (LiCO) in WSNs is suggested. The sensing field is divided into smaller subregions by means of divide-and-conquer method, and a LiCO protocol is distributed in each sensor in the subregion. The network lifetime in each subregion is divided into periods, each period includes 4 stages: Information Exchange, Leader election, decision based activity scheduling optimization, and sensing. The leaders are elected in an independent, asynchronous, and distributed way in all the subregions of the WSN. After that, energy-efficient activity scheduling mechanism based new optimization model is performed by each leader in the subregions. This optimization model is based on the perimeter coverage model in order to producing the optimal cover set of active sensors, which are taken the responsibility of sensing during the current period. LiCO protocol merges between two energy efficient mechanisms, which are used the main advantages of the centralized and distributed approaches and avoids the most of their disadvantages.
+ \begin{table}[h]
+ \caption{Coverage intervals and contributing sensors for sensor node 0.}
+\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
+\hline
+\begin{tabular}[c]{@{}c@{}}The angle \\ $\alpha$ \end{tabular} & \begin{tabular}[c]{@{}c@{}}Segment \\ Left (L) or\\  Right (R)\end{tabular} & \begin{tabular}[c]{@{}c@{}}Sensor \\ Node Id\end{tabular} & \begin{tabular}[c]{@{}c@{}}Interval \\ Coverage\\  Level\end{tabular} & \multicolumn{5}{c|}{\begin{tabular}[c]{@{}c@{}}The Set of Sensors\\ Involved in Interval \\ Coverage\end{tabular}} \\ \hline
+0.0291    & L                                                                         & 1                                                         & 4                                                                     & 0                     & 1                     & 3                    & 4                    &                      \\ \hline
+0.104     & L                                                                         & 2                                                         & 5                                                                     & 0                     & 1                     & 3                    & 4                    & 2                    \\ \hline
+0.3168    & R                                                                         & 3                                                         & 4                                                                     & 0                     & 1                     & 4                    & 2                    &                      \\ \hline
+0.6752    & R                                                                         & 4                                                         & 3                                                                     & 0                     & 1                     & 2                    &                      &                      \\ \hline
+1.8127    & R                                                                         & 1                                                         & 2                                                                     & 0                     & 2                     &                      &                      &                      \\ \hline
+1.9228    & L                                                                         & 5                                                         & 3                                                                     & 0                     & 2                     & 5                    &                      &                      \\ \hline
+2.3959    & L                                                                         & 6                                                         & 4                                                                     & 0                     & 2                     & 5                    & 6                    &                      \\ \hline
+2.4258    & R                                                                         & 2                                                         & 3                                                                     & 0                     & 5                     & 6                    &                      &                      \\ \hline
+2.7868    & L                                                                         & 7                                                         & 4                                                                     & 0                     & 5                     & 6                    & 7                    &                      \\ \hline
+2.8358    & L                                                                         & 8                                                         & 5                                                                     & 0                     & 5                     & 6                    & 7                    & 8                    \\ \hline
+2.9184    & R                                                                         & 5                                                         & 4                                                                     & 0                     & 6                     & 7                    & 8                    &                      \\ \hline
+3.3301    & R                                                                         & 7                                                         & 3                                                                     & 0                     & 6                     & 8                    &                      &                      \\ \hline
+3.9464    & L                                                                         & 9                                                         & 4                                                                     & 0                     & 6                     & 8                    & 9                    &                      \\ \hline
+4.767     & R                                                                         & 6                                                         & 3                                                                     & 0                     & 8                     & 9                    &                      &                      \\ \hline
+4.8425    & L                                                                         & 3                                                         & 4                                                                     & 0                     & 3                     & 8                    & 9                    &                      \\ \hline
+4.9072    & R                                                                         & 8                                                         & 3                                                                     & 0                     & 3                     & 9                    &                      &                      \\ \hline
+5.3804    & L                                                                         & 4                                                         & 4                                                                     & 0                     & 3                     & 4                    & 9                    &                      \\ \hline
+5.9157    & R                                                                         & 9                                                         & 3                                                                     & 0                     & 3                     & 4                    &                      &                      \\ \hline
+\end{tabular}

 
 

+\label{my-label}
+\end{table}

 
 

-\section{ The LiCO Protocol Description}
-\label{sec:The LiCO Protocol Description}
-\noindent In this section, we describe our Lifetime Coverage Optimization Protocol which is called LiCO in more detail. It is based on two efficient-energy mechanisms: the first, is partitioning the sensing field into smaller subregions, and one leader is elected for each subregion;  the second, a sensor activity scheduling based new optimization model so as to produce the optimal cover set of active sensors for the sensing stage during the period.  Obviously, these two mechanisms can be contribute in extend the network lifetime coverage efficiently. 
-%Before proceeding in the presentation of the main ideas of the protocol, we will briefly describe the perimeter coverage model and give some necessary assumptions and definitions.

 
 

-\subsection{ Assumptions and Models}
-\noindent A WSN consisting of $J$ stationary sensor nodes randomly and uniformly distributed in a bounded sensor field is considered. The  wireless sensors  are deployed in high density to ensure initially a high coverage ratio of the interested area. We assume that all the sensor nodes are homogeneous in terms of  communication, sensing, and processing capabilities and heterogeneous in term of energy supply. The  location  information is available to the  sensor node  either through hardware such as embedded GPS or through location discovery algorithms. We assume that each sensor node can directly transmit its measurements to a mobile sink node. For example, a sink can be an unmanned aerial vehicle (UAV) is flying regularly over the sensor field to collect measurements from sensor nodes. A mobile sink node collects the measurements and transmits them to the base station.  We consider a boolean  disk coverage model which is the most widely used sensor coverage model in the literature. Each sensor has a constant sensing range $R_s$. All space points within a disk centered at the sensor with the radius of the sensing range is said to be covered by this sensor. We also assume that the communication range $R_c \geq 2R_s$. In  fact,   Zhang  and Zhou~\cite{Zhang05} proved that if the transmission range fulfills the previous hypothesis, a complete coverage of a convex area implies connectivity among the working nodes in the active mode.
+%The optimization algorithm that used by LiCO protocol based on the perimeter coverage levels of the left and right points of the segments and worked to minimize the number of sensor nodes for each left or right point of the segments within each sensor node. The algorithm minimize the perimeter coverage level of the left and right points of the segments, while, it assures that every perimeter coverage level of the left and right points of the segments greater than or equal to 1.

 
 

-\indent Our protocol is used the perimeter-coverage model which stated in ~\cite{huang2005coverage} as following: The sensor is said to be perimeter covered if all the points on its perimeter are covered by at least one sensor other than itself. According to this model, we named the intersections among the sensor nodes in the sensing field as intersection points. Instead of working with the coverage area, we consider for each sensor a set of intersection points which are determined by using perimeter-coverage model. 
+In LiCO  protocol, scheduling of sensor  nodes activities is formulated  with an
+integer program  based on  coverage intervals. The  formulation of  the coverage
+optimization problem is  detailed in~section~\ref{cp}.  Note that  when a sensor
+node  has a  part of  its sensing  range outside  the WSN  sensing field,  as in
+figure~\ref{ex4pcm}, the maximum coverage level for  this arc is set to $\infty$
+and  the  corresponding  interval  will  not   be  taken  into  account  by  the
+optimization algorithm.
+ 
+\begin{figure}[t!]
+\centering
+\includegraphics[width=62.5mm]{ex4pcm.jpg}  
+\caption{Sensing range outside the WSN's area of interest.}
+\label{ex4pcm}
+\end{figure} 
+%Figure~\ref{ex5pcm} gives an example to compute the perimeter coverage levels for the left and right points of the segments for a sensor node $0$, which has a part of its sensing range exceeding the border of the sensing field of WSN, and it has a six neighbors. In figure~\ref{ex5pcm}, the sensor node $0$ has two segments outside the border of the network sensing field, so the left and right points of the two segments called $-1L$, $-1R$, $-2L$, and $-2R$.
+%\begin{figure}[ht!]
+%\centering
+%\includegraphics[width=75mm]{ex5pcm.jpg}  
+%\caption{Coverage intervals and contributing sensors for sensor node 0 having a  part of its sensing range outside the border.}
+%\label{ex5pcm}
+%\end{figure} 
+
+% MICHEL TO BE CONTINUED FROM HERE

 
 \subsection{The Main Idea}
 
 \subsection{The Main Idea}

-\noindent The area  of  interest can  be  divided using the
-divide-and-conquer strategy into smaller areas called subregions and
+\noindent The area  of  interest can  be  divided into smaller areas called subregions and

 then our protocol will be implemented in each subregion simultaneously. LiCO protocol works into periods fashion as shown in figure~\ref{fig2}.
 \begin{figure}[ht!]
 \centering
 then our protocol will be implemented in each subregion simultaneously. LiCO protocol works into periods fashion as shown in figure~\ref{fig2}.
 \begin{figure}[ht!]
 \centering


@@ -194,7 +500,7 @@ The pseudo-code for LiCO Protocol is illustrated as follows:
          
       \If{$ s_k.ID $ is Not previously selected as a Leader }{
           \emph{ Execute the perimeter coverage model}\;
          
       \If{$ s_k.ID $ is Not previously selected as a Leader }{
           \emph{ Execute the perimeter coverage model}\;

-         % \emph{ Determine the intersection points using perimeter coverage model}\;
+         % \emph{ Determine the segment points using perimeter coverage model}\;

       }
       
       \If{$ (s_k.ID $ is the same Previous Leader) AND (K.CurrentSize = K.PreviousSize)}{
       }
       
       \If{$ (s_k.ID $ is the same Previous Leader) AND (K.CurrentSize = K.PreviousSize)}{


@@ -225,53 +531,67 @@ The pseudo-code for LiCO Protocol is illustrated as follows:
 
 \end{algorithm}
 
 
 \end{algorithm}
 

-\noindent Algorithm 1 gives a brief description of the protocol applied by each sensor node (denoted by $s_k$ for a sensor node indexed by $k$). In this algorithm, the K.CurrentSize and K.PreviousSize refer to the current size and the previous size of sensor nodes in the subregion respectively.
-Initially, the sensor node checks its remaining energy in order to participate in the current period. Each sensor node determines its position and its subregion based Embedded GPS  or Location Discovery Algorithm. After that, all the sensors collect position coordinates, remaining energy $RE_k$, sensor node id, and the number of its one-hop live neighbors during the information exchange. 
-After the cooperation among the sensor nodes in the same subregion, the leader will be elected in distributed way, where each sensor node and based on it's information decide who is the leader. The selection criteria for the leader in order  of priority  are: larger number of neighbors,  larger remaining  energy, and  then in  case of equality, larger index. Thereafter,  if the sensor node is leader, it will execute the perimeter-coverage model for each sensor in the subregion in order to determine the intersection points which would be used in the next stage by the optimization algorithm of the LiCO protocol. Every sensor node is selected as a leader, it is executed the perimeter coverage model only one time during it's life in the network. The leader has the responsibility of applying the integer program algorithm (see section~\ref{cp}), which provides a set of sensors planned to be active in the sensing stage.  As leader, it will send an Active-Sleep packet to each sensor in the same subregion to inform it if it has to be active or not. On the contrary, if the sensor is not the leader, it will wait for the Active-Sleep packet to know its state for the sensing stage.
+\noindent Algorithm 1 gives a brief description of the protocol applied by each sensor node (denoted by $s_k$ for a sensor node indexed by $k$). In this algorithm, the K.CurrentSize and K.PreviousSize refer to the current size and the previous size of sensor nodes still alive in the subregion respectively.
+Initially, the sensor node checks its remaining energy $RE_k$, which must be greater than a threshold $E_{th}$ in order to participate in the current period. Each sensor node determines its position and its subregion based Embedded GPS  or Location Discovery Algorithm. After that, all the sensors collect position coordinates, remaining energy, sensor node id, and the number of its one-hop live neighbors during the information exchange. The sensors inside a same region cooperate to elect a leader. The selection criteria for the leader in order  of priority  are: larger number of neighbors,  larger remaining  energy, and  then in  case of equality, larger index. Thereafter the leader collects information to formulate and solve the integer program which allows to construct the set of active sensors in the sensing stage.  

 
 
 
 

-\section{Lifetime Coverage problem formulation}
-\label{cp}
-In this section, the coverage model are mathematically formulated, where the objective is to find the maximum number of non-disjoint sets of sensor nodes such that each set cover can assure the coverage for the whole region so as to extend the network lifetime in WSN. Our model will use the intersection points which are produced by using the perimeter coverage model~\cite{huang2005coverage} in order to optimize the lifetime coverage in each subregion.
-We defined some parameters, which are related to our optimization model. In our model,  we  consider binary variables $X_{k}$, which determine the activation of sensor $k$ in the sensing round. We also consider the intersection points as targets.   
+%After the cooperation among the sensor nodes in the same subregion, the leader will be elected in distributed way, where each sensor node and based on it's information decide who is the leader. The selection criteria for the leader in order  of priority  are: larger number of neighbors,  larger remaining  energy, and  then in  case of equality, larger index. Thereafter,  if the sensor node is leader, it will execute the perimeter-coverage model for each sensor in the subregion in order to determine the segment points which would be used in the next stage by the optimization algorithm of the LiCO protocol. Every sensor node is selected as a leader, it is executed the perimeter coverage model only one time during it's life in the network.

 
 

+% The leader has the responsibility of applying the integer program algorithm (see section~\ref{cp}), which provides a set of sensors planned to be active in the sensing stage.  As leader, it will send an Active-Sleep packet to each sensor in the same subregion to inform it if it has to be active or not. On the contrary, if the sensor is not the leader, it will wait for the Active-Sleep packet to know its state for the sensing stage.

 
 

-\noindent In this paper, let us define some parameters, which are used in our protocol.
-%the set of intersection points is denoted by $I$, the set of all sensors in the network by $J$, and the set of alive sensors within $J$ by $K$.

 
 

-\noindent $J :$ the set of all sensors in the network.\\
-\noindent $K :$ the set of alive sensors within $J$.\\
-%\noindent $I :$ the set of intersection points.\\
-\noindent $I_j :$ the set of intersection points for sensor $j$.\\
+\section{Lifetime Coverage problem formulation}
+\label{cp}
+In this section, the coverage model is mathematically formulated.
+For convenience, the notations are described first. 
+%Then the lifetime problem of sensor network is formulated. 
+\noindent $S :$ the set of all sensors in the network.\\
+\noindent $A :$ the set of alive sensors within $S$.\\
+%\noindent $I :$ the set of segment points.\\
+\noindent $I_j :$ the set of coverage intervals (CI)  for sensor $j$.\\
+\noindent $I_j$ refers to the set of intervals which have been defined for each sensor $j$ in section~\ref{sec:The LiCO Protocol Description}.
+\noindent For a coverage interval  $i$,  let  $a^j_{ik}$ denote the indicator function of whether the sensor $k$ is involved in the coverage interval $i$ of sensor $j$, that is:

 
 

-\noindent \begin{equation}
-X_{k} = \left \{ 
-\begin{array}{l l}
-  1& \mbox{if sensor $k$  is active,} \\
-  0 &  \mbox{otherwise.}\\
+\begin{equation}
+a^j_{ik} = \left \{ 
+\begin{array}{lll}
+  1 & \mbox{if the sensor $k$ is involved in the } \\
+       &       \mbox{coverage interval $i$ of sensor $j$}, \\
+  0 & \mbox{Otherwise.}\\

 \end{array} \right.
 \end{array} \right.

-%\label{eq11} 
+%\label{eq12} 

 \notag
 \end{equation}
 \notag
 \end{equation}

+Note that $a^k_{ik}=1$ by definition of the interval.\\
+%, where the objective is to find the maximum number of non-disjoint sets of sensor nodes such that each set cover can assure the coverage for the whole region so as to extend the network lifetime in WSN. Our model uses the PCL~\cite{huang2005coverage} in order to optimize the lifetime coverage in each subregion.
+%We defined some parameters, which are related to our optimization model. In our model,  we  consider binary variables $X_{k}$, which determine the activation of sensor $k$ in the sensing round $k$. .   
+\noindent We  consider binary variables $X_{k}$ ($X_k=1$ if the sensor $k$ is active or 0 otherwise), which determine the activation of sensor $k$ in the sensing phase. We define the integer variable $M^j_i$ which measures the undercoverage for the coverage interval $i$ for sensor $j$. In the same way, we define the integer variable $V^j_i$, which measures the overcoverage for the coverage interval $i$ for sensor $j$. If we decide to sustain a level of coverage equal to $l$ all along the perimeter of the sensor $j$, we have to ensure that at least $l$ sensors involved in each coverage interval $i$ ($i \in I_j$) of sensor $j$ are active. According to the previous notations, the number of active sensors in the coverage interval $i$ of sensor $j$ is given by $\sum_{k \in K} a^j_{ik} X_k$. To extend the network lifetime, the objective is to active a minimal number of sensors in each period to ensure the desired coverage level. As the number of alive sensors decreases, it becomes impossible to satisfy the level of coverage for all covergae intervals. We uses variables $M^j_i$ and $V^j_i$ as a measure of the deviation between the desired number of active sensors in a coverage interval and the effective number of active sensors. And we try to minimize these deviations, first to force the activation of a minimal number of sensors to ensure the desired coverage level, and if the desired level can not be completely  satisfied, to reach a coverage level as close as possible that the desired one.

 
 

-\noindent $M^j_i (undercoverage): $ integer value $\in  \mathbb{N}$ for intersection point $i$ of sensor $j$.

 
 

-\noindent $V^j_i (overcoverage): $ integer value $\in  \mathbb{N}$ for intersection point $i$ of sensor $j$.
+
+%A system of linear constraints is imposed to attempt to keep the coverage level in each coverage interval to within specified PCL. Since it is physically impossible to satisfy all constraints simultaneously, each constraint uses a variable to either record when the coverage level is achieved, or to record the deviation from the desired coverage level. These additional variables are embedded into an objective function to be minimized. 
+
+%\noindent In this paper, let us define some parameters, which are used in our protocol.
+%the set of segment points is denoted by $I$, the set of all sensors in the network by $J$, and the set of alive sensors within $J$ by $K$.
+
+
+%\noindent \begin{equation}
+%X_{k} = \left \{ 
+%\begin{array}{l l}
+ % 1& \mbox{if sensor $k$  is active,} \\
+%  0 &  \mbox{otherwise.}\\
+%\end{array} \right.
+%\label{eq11} 
+%\notag
+%\end{equation}
+
+%\noindent $M^j_i (undercoverage): $ integer value $\in  \mathbb{N}$ for segment point $i$ of sensor $j$.
+
+%\noindent $V^j_i (overcoverage): $ integer value $\in  \mathbb{N}$ for segment point $i$ of sensor $j$.

 
 
 
 
 
 

-\noindent For an intersection point  $i$,  let  $a^j_{ik}$ denote the indicator function of whether the sensor $k$ is involved in the intersection point $i$ of sensor $j$, that is:

 
 

-\begin{equation}
-a^j_{ik} = \left \{ 
-\begin{array}{lll}
-  1 & \mbox{If the sensor $k$ is involved in the } \\
-       &       \mbox{intersection point $i$ of sensor $j$}, \\
-  0 & \mbox{Otherwise.}\\
-\end{array} \right.
-%\label{eq12} 
-\notag
-\end{equation}

 
 \noindent Our coverage optimization problem can be mathematically formulated as follows: \\
 %Objective:
 
 \noindent Our coverage optimization problem can be mathematically formulated as follows: \\
 %Objective:


@@ -279,28 +599,26 @@ a^j_{ik} = \left \{
 \begin{equation} \label{eq:ip2r}
 \left \{
 \begin{array}{ll}
 \begin{equation} \label{eq:ip2r}
 \left \{
 \begin{array}{ll}

-\min \sum_{j \in J} \sum_{i \in I_j} (\alpha^j_i ~ M^j_i + \beta^j_i ~ V^j_i )&\\
+\min \sum_{j \in S} \sum_{i \in I_j} (\alpha^j_i ~ M^j_i + \beta^j_i ~ V^j_i )&\\

 \textrm{subject to :}&\\
 \textrm{subject to :}&\\

-\sum_{k \in K} ( a^j_{ik} ~ X_{k}) + M^j_i  \geq 1 \\
+\sum_{k \in A} ( a^j_{ik} ~ X_{k}) + M^j_i  \geq l \quad \forall i \in I_j, \forall j \in S\\

 %\label{c1} 
 %\label{c1} 

-\sum_{k \in K} ( a^j_{ik} ~ X_{k}) - V^j_i  \leq 1 \\
+\sum_{k \in A} ( a^j_{ik} ~ X_{k}) - V^j_i  \leq l \quad \forall i \in I_j, \forall j \in S\\

 % \label{c2}
 % \Theta_{p}\in \mathbb{N}, &\forall p \in P\\
 % U_{p} \in \{0,1\}, &\forall p \in P\\
 % \label{c2}
 % \Theta_{p}\in \mathbb{N}, &\forall p \in P\\
 % U_{p} \in \{0,1\}, &\forall p \in P\\

-X_{k} \in \{0,1\}, &\forall k \in K
+X_{k} \in \{0,1\}, \forall k \in A

 \end{array}
 \right.
 \end{equation}
 
 \end{array}
 \right.
 \end{equation}
 

-The first group  of constraints indicates that some intersection points $i$
-should be covered by at least one  sensor node and, if it is not always the
-case, overcoverage and undercoverage variables help balancing the
-restriction equations by taking positive values. There are two main         
-objectives. First, we limit the overcoverage of intersection points in order to
-activate a minimum number of sensors.  Second, we prevent the absence of monitoring on
-some parts of the subregion by minimizing the undercoverage. The
-weights $\alpha$ and $\beta$ must be properly chosen so as to
-guarantee that the maximum number of intersection points are covered during each round.
+
+\noindent $\alpha^j_i$ and $\beta^j_i$ are nonnegative weights selected according to the
+relative importance of satisfying the associated
+level of coverage. For example, weights associated with coverage intervals of a specified part of a region
+may be given a relatively
+larger magnitude than weights associated
+with another region. This kind of integer program is inspired from the model developed for brachytherapy treatment planning for optimizing dose distribution \cite{0031-9155-44-1-012}. The integer program must be solved by the leader in each subregion at the beginning of each sensing phase, whenever the environment has changed (new leader, death of some sensors). Note that the number of constraints in the model is constant (constraints of coverage expressed for all sensors), whereas the number of variables $X_k$ decreases over periods, since we consider only alive sensors (sensors with enough energy to be alive during one sensing phase) in the model. 

 
 
 \section{\uppercase{PERFORMANCE EVALUATION AND ANALYSIS}}  
 
 
 \section{\uppercase{PERFORMANCE EVALUATION AND ANALYSIS}}  


@@ -309,7 +627,7 @@ guarantee that the maximum number of intersection points are covered during each
 
 \subsection{Simulation Settings}
 %\label{sub1}
 
 \subsection{Simulation Settings}
 %\label{sub1}

-In this section, we focused on the performance of LiCO protocol, which is distributed in each sensor node in the sixteen subregions of WSN. We used the same energy consumption model which are used in~\cite{Idrees2}. Table~\ref{table3} gives the chosen parameters setting.
+In this section, we focus on the performance of LiCO protocol, which is distributed in each sensor node in the sixteen subregions of WSN. We use the same energy consumption model which is used in~\cite{Idrees2}. Table~\ref{table3} gives the chosen parameters setting.

 
 \begin{table}[ht]
 \caption{Relevant parameters for network initializing.}
 
 \begin{table}[ht]
 \caption{Relevant parameters for network initializing.}


@@ -344,7 +662,7 @@ $\beta^j_i$ & 0.4
 \end{table}
 Simulations with five  different node densities going from  100 to 250~nodes were
 performed  considering  each  time  25~randomly generated  networks,  to  obtain
 \end{table}
 Simulations with five  different node densities going from  100 to 250~nodes were
 performed  considering  each  time  25~randomly generated  networks,  to  obtain

-experimental results  which are relevant.All simulations are repeated 25 times and the results are averaged. The  nodes are deployed on a field of interest of $(50 \times 25)~m^2 $ in such a way that they cover the field with a high coverage ratio.
+experimental results  which are relevant. All simulations are repeated 25 times and the results are averaged. The  nodes are deployed on a field of interest of $(50 \times 25)~m^2 $ in such a way that they cover the field with a high coverage ratio.

 
 Each node has an initial energy level, in Joules, which is randomly drawn in the
 interval  $[500-700]$.  If  it's  energy  provision reaches  a  value below  the
 
 Each node has an initial energy level, in Joules, which is randomly drawn in the
 interval  $[500-700]$.  If  it's  energy  provision reaches  a  value below  the


@@ -356,6 +674,8 @@ for one period (3600 seconds), and  adding the energy for the pre-sensing phases
 According to  the interval of initial energy,  a sensor may be  active during at
 most 20 rounds.
 
 According to  the interval of initial energy,  a sensor may be  active during at
 most 20 rounds.
 

+The values of $\alpha^j_i$ and $\beta^j_i$ have been chosen in a way that ensuring a good network coverage and for a longer time during the lifetime of the WSN.  We have given a higher priority for the undercoverage ( by setting the $\alpha^j_i$ with a larger value than $\beta^j_i$) so as to prevent the non-coverage for the interval i of the sensor j. On the other hand, we have given a little bit lower value for $\beta^j_i$ so as to minimize the number of active sensor nodes that contribute in covering the interval i in sensor j.
+

 In the simulations,  we introduce the following performance  metrics to evaluate
 the efficiency of our approach:
 
 In the simulations,  we introduce the following performance  metrics to evaluate
 the efficiency of our approach:
 


@@ -391,9 +711,9 @@ in  order to  minimize  the communication  overhead  and maximize  the
 network lifetime. The Active Sensors Ratio is defined as follows:
 \begin{equation*}
 \scriptsize
 network lifetime. The Active Sensors Ratio is defined as follows:
 \begin{equation*}
 \scriptsize

-\mbox{ASR}(\%) =  \frac{\sum\limits_{r=1}^R \mbox{$A_r^p$}}{\mbox{$S$}} \times 100 .
+\mbox{ASR}(\%) =  \frac{\sum\limits_{r=1}^R \mbox{$A_r$}}{\mbox{$S$}} \times 100 .

 \end{equation*}
 \end{equation*}

-Where: $A_r^t$ is the number of active sensors in the subregion $r$ during period $p$ in the current sensing stage, $S$ is the total number of sensors in the network, and $R$ is the total number of the subregions in the network.
+Where: $A_r^t$ is the number of active sensors in the subregion $r$ in the current sensing stage, $S$ is the total number of sensors in the network, and $R$ is the total number of the subregions in the network.

 
  
 
 
  
 


@@ -422,135 +742,125 @@ by the whole network in the sensing phase (active and sleeping nodes).
 %\end{enumerate}
 
 \subsection{Simulation Results}
 %\end{enumerate}
 
 \subsection{Simulation Results}

-In this section, we present the simulation results of LiCO protocol and the other protocols using a discrete event simulator OMNeT++ \cite{varga} to run different series of simulations. We implemented all protocols precisely on a laptop DELL with Intel Core~i3~2370~M (2.4 GHz)  processor (2 cores) and the MIPS (Million Instructions  Per Second) rate equal to 35330. To be consistent  with the use of a sensor node with Atmels AVR ATmega103L microcontroller (6 MHz) and  a MIPS rate  equal to 6,  the original execution time on the laptop is multiplied by  2944.2 $\left(\frac{35330}{2} \times  \frac{1}{6} \right)$  so as to use it by the energy consumption model especially, after the computation and listening.
+In this section, we present the simulation results of LiCO protocol and the other protocols using a discrete event simulator OMNeT++ \cite{varga} to run different series of simulations. We implemented all protocols precisely on a laptop DELL with Intel Core~i3~2370~M (2.4 GHz)  processor (2 cores) and the MIPS (Million Instructions  Per Second) rate equal to 35330. To be consistent  with the use of a sensor node with Atmels AVR ATmega103L microcontroller (6 MHz) and  a MIPS rate  equal to 6,  the original execution time on the laptop is multiplied by  2944.2 $\left(\frac{35330}{2} \times  \frac{1}{6} \right)$  so as to use it by the energy consumption model especially, after the computation and listening. Employing the modeling language for Mathematical Programming (AMPL)~\cite{AMPL}, the associated integer program instance is generated in a standard format, which is then read and solved by the optimization solver GLPK (GNU linear Programming Kit available in the public domain) \cite{glpk} through a Branch-and-Bound method. 

  
 We compared LiCO protocol to three other approaches: the first, called DESK and proposed  by ~\cite{ChinhVu}  is a fully distributed  coverage  algorithm;  the second, called GAF  ~\cite{xu2001geography}, consists in dividing the region
 into fixed  squares.  During the decision  phase, in each square,  one sensor is
  
 We compared LiCO protocol to three other approaches: the first, called DESK and proposed  by ~\cite{ChinhVu}  is a fully distributed  coverage  algorithm;  the second, called GAF  ~\cite{xu2001geography}, consists in dividing the region
 into fixed  squares.  During the decision  phase, in each square,  one sensor is

-chosen to remain active during the sensing phase; the third, DiLCO protocol~\cite{Idrees2}, which is improved version on the work in ~\cite{idrees2014coverage}. 
+chosen to remain active during the sensing phase; the third, DiLCO protocol~\cite{Idrees2} is an improved version on the work presented in ~\cite{idrees2014coverage}. Note that the LiCO protocol is based on the same framework as that of DiLCO. For these two protocols, the division of the region of interest in 16 subregions was chosen since it produces the best results. The difference between the two protocols relies on the use of the integer programming to provide the set of sensors that have to be activated in each sensing phase. Whereas DiLCO protocol tries to satisfy the coverage of a set of primary points, LiCO protocol tries to reach a desired level of coverage $l$ for each sensor's perimeter. In the experimentations, we chose a level of coverage equal to 1 ($l=1$).

 
 \subsubsection{\textbf{Coverage Ratio}}
 
 \subsubsection{\textbf{Coverage Ratio}}

-In this experiment, Figure~\ref{fig333} shows the average coverage ratio for 150 deployed nodes. 
+Figure~\ref{fig333} shows the average coverage ratio for 200 deployed nodes obtained with the four methods.

  
 \parskip 0pt    
 \begin{figure}[h!]
 \centering
  
 \parskip 0pt    
 \begin{figure}[h!]
 \centering

- \includegraphics[scale=0.5] {R/CR.pdf} 
-\caption{The coverage ratio for 150 deployed nodes}
+ \includegraphics[scale=0.5] {R/CR.eps} 
+\caption{The coverage ratio for 200 deployed nodes}

 \label{fig333}
 \end{figure} 
 
 \label{fig333}
 \end{figure} 
 

-It is shown that DESK,  GAF, and LiCO provides a little better coverage ratio with 99.99\%, 99.91\%, and 99.25\% against 99.02\% produced by DiLCO-16 for the lowest number of rounds. This is due to the fact that DiLCO protocol put in sleep mode redundant sensors using optimization (which lightly decreases the coverage ratio) while there are more nodes are active in the case of DESK and GAF, and a little higher in comparison with the optimization algorithm used by LiCO.
-Moreover, when the number of rounds increases, coverage ratio produced by DESK and GAF protocols decreases. This is due to dead nodes. However, DiLCO-16 protocol maintains almost a good coverage from the round 31 to the round 50 and it is close to LiCO protocol. This is because it optimizes the coverage and the lifetime in WSN based on the primary points by selecting the best representative sensor nodes for the sensing stage. The coverage ratio of LiCO Protocol seems to be better than other approaches starting from the round 50 because the optimization algorithm used by LiCO has been optimized the lifetime coverage based on the perimeter coverage model, so it provided acceptable coverage for a larger number of periods and prolonging the network lifetime based on the perimeter of the sensor nodes in each subregion of WSN. Although some nodes are dead, sensor activity scheduling based optimization of LiCO selected another nodes to ensure the coverage of the area of interest.
+DESK,  GAF, and DiLCO provide a little better coverage ratio with 99.99\%, 99.91\%, and 99.02\% against 98.76\% produced by LiCO for the first periods. This is due to the fact that DiLCO protocol put in sleep mode redundant sensors using optimization (which lightly decreases the coverage ratio) while there are more active nodes in the case of others methods. But when the number of periods exceeds 70 periods, it clearly appears that LiCO provides a better coverage ratio and keeps a coverage ratio greater than 50\% for longer periods (15 more compared to DiLCO, 40 more compared to DESK).

 
 

-Figure~\ref{figCR200} represents the average coverage ratio provided by
-DiLCO-16, DESK, GAF, and LiCO for 200 deployed nodes while varying the number of periods. The same observation is made as in Figure~\ref{fig333}, i.e. DiLCO-16 showed a good coverage in the beginning then when the number of periods increases, the coverage ratio decreases due to died sensor nodes. Meanwhile, thanks to sensor activity scheduling based new optimization model, which is used by LiCO protocol to ensure a longer lifetime coverage in comparison with other approaches.
- 
-\parskip 0pt    
-\begin{figure}[h!]
-\centering
- \includegraphics[scale=0.5] {R/CR200.pdf} 
-\caption{The coverage ratio for 200 deployed nodes}
-\label{figCR200}
-\end{figure} 
+%When the number of periods increases, coverage ratio produced by DESK and GAF protocols decreases. This is due to dead nodes. However, DiLCO protocol maintains almost a good coverage from the round 31 to the round 63 and it is close to LiCO protocol. The coverage ratio of LiCO protocol is better than other approaches from the period 64.

 
 

+%because the optimization algorithm used by LiCO has been optimized the lifetime coverage based on the perimeter coverage model, so it provided acceptable coverage for a larger number of periods and prolonging the network lifetime based on the perimeter of the sensor nodes in each subregion of WSN. Although some nodes are dead, sensor activity scheduling based optimization of LiCO selected another nodes to ensure the coverage of the area of interest. i.e. DiLCO-16 showed a good coverage in the beginning then LiCO, when the number of periods increases, the coverage ratio decreases due to died sensor nodes. Meanwhile, thanks to sensor activity scheduling based new optimization model, which is used by LiCO protocol to ensure a longer lifetime coverage in comparison with other approaches. 

 
 

-\subsubsection{\textbf{Active Sensors Ratio}} 
-It is important to have as few active nodes as possible in each period, in order to minimize the energy consumption and maximize the network lifetime. Figure~\ref{fig444} shows the average active nodes ratio for 150 deployed nodes. 
-
-\begin{figure}[h!]
-\centering
-\includegraphics[scale=0.5]{R/ASR.pdf}  
-\caption{The active sensors ratio for 150 deployed nodes }
-\label{fig444}
-\end{figure} 
-
-We can observe that DESK and GAF have 37.62 \% and 44.77 \% active nodes for the first fourteen rounds and DiLCO-16 and LiCO protocols competes perfectly with only 24.82 \% and 29.70 \% active nodes for the first 14 rounds. Then as the number of rounds increases our LiCO protocol has a lower number of active nodes in comparison with DiLCO-16, DESK and GAF, especially from the round $15^{th}$ because it optimizes the lifetime coverage into the subregion based on the perimeter coverage model, which made LiCO improves the coverage ratio in comparison with other approaches. 
-
-The variation of average active sensor nodes
-against the number of periods for 200 deployed sensors is illuminated in figure~\ref{figASR200}. Observe that the number of active nodes, which are provided by DiLCO-16 is lower than the case of LiCO protocol (17.92 of active nodes against 21.8 respectively, for first $17^{th}$ periods). After that, LiCO protocol generates a lower number of active sensors using our optimization algorithm that contributed in extend the lifetime coverage as long as possible. 

 
 

+\subsubsection{\textbf{Active Sensors Ratio}} 
+Having active nodes as few as possible in each period is essential in order to minimize the energy consumption and so maximize the network lifetime. Figure~\ref{fig444} shows the average active nodes ratio for 200 deployed nodes. 

 
 \begin{figure}[h!]
 \centering
 
 \begin{figure}[h!]
 \centering

-\includegraphics[scale=0.5]{R/ASR200.pdf}  
+\includegraphics[scale=0.5]{R/ASR.eps}  

 \caption{The active sensors ratio for 200 deployed nodes }
 \caption{The active sensors ratio for 200 deployed nodes }

-\label{figASR200}
+\label{fig444}

 \end{figure} 
 
 \end{figure} 
 

-
-%We see that the DESK and GAF have less number of active nodes beginning at the rounds $35^{th}$ and $32^{th}$ because there are many nodes are died due to the high energy consumption by the redundant nodes during the sensing phase. 
+We observe that DESK and GAF have 30.36 \% and 34.96 \% active nodes for the first fourteen rounds and DiLCO and LiCO protocols compete perfectly with only 17.92 \% and 20.16 \% active nodes during the same time interval. As the number of periods increases, LiCO protocol has a lower number of active nodes in comparison with the three other approaches, while keeping of greater coverage ratio as shown in figure \ref{fig333}.

 
 \subsubsection{\textbf{The Energy Consumption}}
 
 \subsubsection{\textbf{The Energy Consumption}}

-In this experiment, we study the effect of the energy consumed by the WSN during the communication, computation, listening, active, and sleep modes for different network densities and compare it with other approaches. Figures~\ref{fig3EC95} and ~\ref{fig3EC50} illustrate the energy consumption for different network sizes for $Lifetime95$ and $Lifetime50$. 
+We study the effect of the energy consumed by the WSN during the communication, computation, listening, active, and sleep modes for different network densities and compare it for the four approaches. Figures~\ref{fig3EC95} and \ref{fig3EC50} illustrate the energy consumption for different network sizes and for $Lifetime95$ and $Lifetime50$. 

 
 \begin{figure}[h!]
 \centering
 
 \begin{figure}[h!]
 \centering

-\includegraphics[scale=0.5]{R/EC95.pdf} 
-\caption{The Energy Consumption with $95\%-Lifetime$}
+\includegraphics[scale=0.5]{R/EC95.eps} 
+\caption{The Energy Consumption per period with $Lifetime_{95}$}

 \label{fig3EC95}
 \end{figure} 
 \label{fig3EC95}
 \end{figure} 

-
+                                           

 \begin{figure}[h!]
 \centering
 \begin{figure}[h!]
 \centering

-\includegraphics[scale=0.5]{R/EC50.pdf} 
-\caption{The Energy Consumption with $Lifetime50$}
+\includegraphics[scale=0.5]{R/EC50.eps} 
+\caption{The Energy Consumption per period with $Lifetime_{50}$}

 \label{fig3EC50}
 \end{figure} 
 
 \label{fig3EC50}
 \end{figure} 
 

-The results show that our LiCO protocol is the most competitive from the energy consumption point of view. As shown in figures Figures~\ref{fig3EC95} and ~\ref{fig3EC50}, LiCO consumes less energy  especially when the network size increases because it puts in sleep mode less active sensor number as possible in most periods of the network lifetime. The optimization algorithm, which used by our LiCO protocol,  was optimized the lifetime coverage efficiently based on the perimeter coverage model.
+The results show that our LiCO protocol is the most competitive from the energy consumption point of view. As shown in figures~\ref{fig3EC95} and \ref{fig3EC50}, LiCO consumes much less energy  than the three other methods. One might think that the resolution of the integer program is too costly in energy, but the results show that it is very beneficial to lose a bit of time in the selection of sensors to activate. Indeed this optimization program allows to reduce significantly the number of active sensors and so the energy consumption while keeping a good coverage level.
+%The optimization algorithm, which used by LiCO protocol,  was improved the lifetime coverage efficiently based on the perimeter coverage model.

 
 

- The other approaches have a high energy consumption due to activating a larger number of redundant nodes as well as the energy consumed during the different modes of sensor nodes. In fact,  a distributed  method on the subregions greatly reduces the number of communications and the time of listening so thanks to the partitioning of the initial network into several independent subnetworks. 
+ %The other approaches have a high energy consumption due to activating a larger number of sensors. In fact,  a distributed  method on the subregions greatly reduces the number of communications and the time of listening so thanks to the partitioning of the initial network into several independent subnetworks. 

 
 
 %\subsubsection{Execution Time}
 
 \subsubsection{\textbf{The Network Lifetime}}
 
 
 %\subsubsection{Execution Time}
 
 \subsubsection{\textbf{The Network Lifetime}}

-In this experiment, we are observed the superiority of LiCO and DiLCO-16 protocols against other two approaches in prolonging the network lifetime. In figures~\ref{fig3LT95} and \ref{fig3LT50}, network lifetime, $Lifetime95$ and $Lifetime50$ respectively, are illustrated for different network sizes.  
+We observe the superiority of LiCO and DiLCO protocols against other two approaches in prolonging the network lifetime. In figures~\ref{fig3LT95} and \ref{fig3LT50}, network lifetime, $Lifetime95$ and $Lifetime50$ respectively, are illustrated for different network sizes.  

 
 \begin{figure}[h!]
 \centering
 
 \begin{figure}[h!]
 \centering

-\includegraphics[scale=0.5]{R/LT95.pdf}  
-\caption{The Network Lifetime for $Lifetime95$}
+\includegraphics[scale=0.5]{R/LT95.eps}  
+\caption{The Network Lifetime for $Lifetime_{95}$}

 \label{fig3LT95}
 \end{figure}
 
 
 \begin{figure}[h!]
 \centering
 \label{fig3LT95}
 \end{figure}
 
 
 \begin{figure}[h!]
 \centering

-\includegraphics[scale=0.5]{R/LT50.pdf}  
-\caption{The Network Lifetime for $Lifetime50$}
+\includegraphics[scale=0.5]{R/LT50.eps}  
+\caption{The Network Lifetime for $Lifetime_{50}$}

 \label{fig3LT50}
 \end{figure} 
 
 \label{fig3LT50}
 \end{figure} 
 

-As highlighted by figures~\ref{fig3LT95} and \ref{fig3LT50}, the network lifetime obviously increases when the size of the network increases, with our LiCO and DiLCO-16 protocols that leads to maximize the lifetime of the network compared with other approaches. 
+As highlighted by figures~\ref{fig3LT95} and \ref{fig3LT50}, the network lifetime obviously increases when the size of the network increases, and it is clearly larger with DiLCO and LiCO protocols compared with the two other methods. For instance, for a network of 300 sensors, the coverage ratio is greater than 50\% about two times longer with LiCO compared to DESK method.

 
 

-By choosing the best suited nodes, for each round, by optimizing the coverage and lifetime of the network to cover the area of interest and by letting the other ones sleep in order to be used later in next rounds, LiCO protocol efficiently prolonged the network lifetime especially for a coverage ratio greater than $50 \%$, whilst it stayed very near to  DiLCO-16 protocol for $95 \%$.  Figure~\ref{figLTALL} introduces the comparisons of the lifetime coverage for different coverage ratios between our LiCO and DiLCO-16 protocols.
+%By choosing the best suited nodes, for each period, by optimizing the coverage and lifetime of the network to cover the area of interest and by letting the other ones sleep in order to be used later in next rounds, LiCO protocol efficiently prolonged the network lifetime especially for a coverage ratio greater than $50 \%$, whilst it stayed very near to  DiLCO-16 protocol for $95 \%$.  
+Figure~\ref{figLTALL} introduces the comparisons of the lifetime coverage for different coverage ratios for LiCO and DiLCO protocols. 
+We denote by Protocol/50, Protocol/80, Protocol/85, Protocol/90, and Protocol/95 the amount of time during which the network can satisfy an area coverage greater than $50\%$, $80\%$, $85\%$, $90\%$, and $95\%$ respectively. Indeed there are applications that do not require a 100\% coverage of the surveillance region. LiCO might be an interesting  method  since it achieves a good balance between a high level coverage ratio and network lifetime.

 
 \begin{figure}[h!]
 \centering
 
 \begin{figure}[h!]
 \centering

-\includegraphics[scale=0.5]{R/LTALL.pdf}  
-\caption{The Network Lifetime for $LifetimeDif$}
+\includegraphics[scale=0.5]{R/LTa.eps}  
+\caption{The Network Lifetime for different coverage ratios}

 \label{figLTALL}
 \end{figure} 
 
 
 \label{figLTALL}
 \end{figure} 
 
 

-Comparison shows that our LiCO protocol, which are used distributed optimization over the subregions, is the more relevance one because it is robust to network disconnection during the network lifetime as well as it consume less energy in comparison with other approaches. LiCO protocol gave acceptable coverage ratio for a larger number of periods using new optimization algorithm that based on a perimeter coverage model. It also means that distributing the algorithm in each node and subdividing the sensing field into many subregions, which are managed independently and simultaneously, is the most relevant way to maximize the lifetime of a network.
+%Comparison shows that LiCO protocol, which are used distributed optimization over the subregions, is the more relevance one for most coverage ratios and WSN sizes because it is robust to network disconnection during the network lifetime as well as it consume less energy in comparison with other approaches. LiCO protocol gave acceptable coverage ratio for a larger number of periods using new optimization algorithm that based on a perimeter coverage model. It also means that distributing the algorithm in each node and subdividing the sensing field into many subregions, which are managed independently and simultaneously, is the most relevant way to maximize the lifetime of a network.

 
 
 \section{\uppercase{Conclusion and Future Works}}
 \label{sec:Conclusion and Future Works}
 
 
 \section{\uppercase{Conclusion and Future Works}}
 \label{sec:Conclusion and Future Works}

-In this paper, we have studied the problem of lifetime coverage optimization in
-WSNs. To cope with this problem, the area of interest is divided into a smaller subregions using  divide-and-conquer method, and then a LiCO protocol for optimizing the lifetime coverage in each subregion. LiCO protocol combines two efficient techniques:  the first, network
-leader election, which executes the perimeter coverage model (only one time), the optimization algorithm, and sending the schedule produced by the optimization algorithm to other nodes in the subregion ; the second, sensor activity scheduling based optimization in which a new lifetime coverage optimization model is proposed. The main challenges include how to select the  most efficient leader in each subregion and the best schedule of sensor nodes that will optimize the network lifetime coverage
-in the subregion. The network lifetime coverage in each subregion is divided into
-periods, each period consists  of four stages: (i) Information Exchange,
-(ii) Leader Election, (iii) a Decision based new optimization model in order to
-select the  nodes remaining  active for the last stage,  and  (iv) Sensing.
-The  simulation results show that LiCO is  is more energy-efficient than other approaches, with respect to lifetime, coverage ratio, active sensors ratio, and energy consumption. Indeed, when dealing with large and dense WSNs, a distributed optimization approach on the subregions of WSN like the one we are proposed allows to reduce the difficulty of a single global optimization problem by partitioning it in many smaller problems, one per subregion, that can be solved more easily.
-
-Our future work is four-fold: the first, we plan to extend a lifetime coverage optimization problem in order to computes all active sensor schedules in only one step for many periods;
-the second, we focus on extend our protocol and optimization algorithm to take into account the heterogeneous sensors, which do not have the same energy, processing,  sensing and communication capabilities;
-the third, we are investigating new optimization model based on the sensing range so as to maximize the lifetime coverage in WSN;
+In this paper we have studied the problem of lifetime coverage optimization in
+WSNs. We designed a protocol LiCO that schedules node activities (wakeup and sleep) with the objective of maintaining a good coverage ratio while maximizing the network lifetime. This protocol is applied on each subregion of the area of interest. It works in periods and is based on the resolution of an integer program to select the subset of sensors operating in active mode for each period. Our work is original in so far as it proposes for the first time an integer program scheduling the activation of sensors based on their perimeter coverage level instead of using a set of targets/points to be covered.
+
+ 
+
+
+%To cope with this problem, the area of interest is divided into a smaller subregions using  divide-and-conquer method, and then a LiCO protocol for optimizing the lifetime coverage in each subregion. LiCO protocol combines two efficient techniques:  network
+%leader election, which executes the perimeter coverage model (only one time), the optimization algorithm, and sending the schedule produced by the optimization algorithm to other nodes in the subregion ; the second, sensor activity scheduling based optimization in which a new lifetime coverage optimization model is proposed. The main challenges include how to select the  most efficient leader in each subregion and the best schedule of sensor nodes that will optimize the network lifetime coverage
+%in the subregion. 
+%The network lifetime coverage in each subregion is divided into
+%periods, each period consists  of four stages: (i) Information Exchange,
+%(ii) Leader Election, (iii) a Decision based new optimization model in order to
+%select the  nodes remaining  active for the last stage,  and  (iv) Sensing.
+We carried out severals simulations to evaluate the proposed protocol.  The  simulation results show that LiCO is  is more energy-efficient than other approaches, with respect to lifetime, coverage ratio, active sensors ratio, and energy consumption. 
+%Indeed, when dealing with large and dense WSNs, a distributed optimization approach on the subregions of WSN like the one we are proposed allows to reduce the difficulty of a single global optimization problem by partitioning it in many smaller problems, one per subregion, that can be solved more easily.
+
+We have identified different research directions that arise out of the work presented here. 
+We plan to extend our framework such that the schedules are planned for multiple periods in advance.
+%in order to compute all active sensor schedules in only one step for many periods;
+We also want to improve our integer program to take into account the heterogeneous sensors, which do not have the same energy, processing,  sensing and communication capabilities;
+%the third, we are investigating new optimization model based on the sensing range so as to maximize the lifetime coverage in WSN;

 Finally, our final goal is to implement our protocol using a sensor-testbed to evaluate their performance in real world applications.
 
 \section*{\uppercase{Acknowledgements}}
 Finally, our final goal is to implement our protocol using a sensor-testbed to evaluate their performance in real world applications.
 
 \section*{\uppercase{Acknowledgements}}