version R4 pour soumission

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--- a/PeCO-EO/articleeo.tex
+++ b/PeCO-EO/articleeo.tex
@@ -2,23 +2,21 @@
 % v4.0 released April 2013
 
 \documentclass{gENO2e}
 % v4.0 released April 2013
 
 \documentclass{gENO2e}

-%\usepackage[linesnumbered,ruled,vlined,commentsnumbered]{algorithm2e}
-%\renewcommand{\algorithmcfname}{ALGORITHM}
+

 \usepackage{indentfirst}
 \usepackage{indentfirst}

+\usepackage{color}

 \usepackage[algo2e,ruled,vlined]{algorithm2e}
 \begin{document}
 
 \usepackage[algo2e,ruled,vlined]{algorithm2e}
 \begin{document}
 

-%\jvol{00} \jnum{00} \jyear{2013} \jmonth{April}
-
-%\articletype{GUIDE}
-
-\title{{\itshape Perimeter-based Coverage Optimization to Improve Lifetime \\
-    in Wireless Sensor Networks}}
-
-\author{Ali Kadhum Idrees$^{a}$, Karine Deschinkel$^{a}$$^{\ast}$\thanks{$^\ast$Corresponding author. Email: karine.deschinkel@univ-fcomte.fr}, Michel Salomon$^{a}$ and Rapha\"el Couturier $^{a}$
-$^{a}${\em{FEMTO-ST Institute, UMR 6174 CNRS, University of Franche-Comte,
-          Belfort, France}}}
+\title{{\itshape Perimeter-based Coverage Optimization \\
+  to Improve Lifetime in Wireless Sensor Networks}}

 
 

+\author{Ali Kadhum Idrees$^{a,b}$, Karine Deschinkel$^{a}$$^{\ast}$\thanks{$^\ast$Corresponding author. Email: karine.deschinkel@univ-fcomte.fr}, Michel Salomon$^{a}$, and Rapha\"el Couturier $^{a}$
+  $^{a}${\em{FEMTO-ST Institute, UMR 6174 CNRS, \\
+  University Bourgogne Franche-Comt\'e, Belfort, France}} \\ 
+  $^{b}${\em{Department of Computer Science, University of Babylon, Babylon, Iraq}}
+}         
+         

 \maketitle
 
 \begin{abstract}
 \maketitle
 
 \begin{abstract}


@@ -26,15 +24,15 @@ The most important problem in a Wireless Sensor Network (WSN) 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
 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. We
-propose such  an approach called Perimeter-based  Coverage Optimization protocol
-(PeCO). It  is a hybrid  of centralized and  distributed methods: the  region of
-interest  is  first  subdivided  into   subregions  and  the  protocol  is  then
-distributed among sensor  nodes in each subregion.  The novelty  of our approach
+scheduling which ensures  sensing coverage while minimizing the  energy cost. In
+this  paper an  approach called  Perimeter-based Coverage  Optimization protocol
+(PeCO) is proposed.  It is a hybrid of centralized  and distributed methods: the
+region of interest is first subdivided  into subregions and the protocol is then
+distributed among sensor  nodes in each subregion.  The novelty  of the approach

 lies essentially  in the  formulation of a  new mathematical  optimization model
 based  on  the  perimeter  coverage   level  to  schedule  sensors'  activities.
 Extensive simulation experiments demonstrate that PeCO can offer longer lifetime
 lies essentially  in the  formulation of a  new mathematical  optimization model
 based  on  the  perimeter  coverage   level  to  schedule  sensors'  activities.
 Extensive simulation experiments demonstrate that PeCO can offer longer lifetime

-coverage for WSNs in comparison with some other protocols.
+coverage for WSNs compared to other protocols.

 
 \begin{keywords}
   Wireless Sensor Networks, Area Coverage, Energy efficiency, Optimization, Scheduling.
 
 \begin{keywords}
   Wireless Sensor Networks, Area Coverage, Energy efficiency, Optimization, Scheduling.


@@ -42,62 +40,61 @@ coverage for WSNs in comparison with some other protocols.
 
 \end{abstract}
 
 
 \end{abstract}
 

-

 \section{Introduction}
 \label{sec:introduction}
 
 The continuous progress in Micro  Electro-Mechanical Systems (MEMS) and wireless
 \section{Introduction}
 \label{sec:introduction}
 
 The continuous progress in Micro  Electro-Mechanical Systems (MEMS) and wireless

-communication hardware has  given rise to the opportunity to  use large networks
+communication hardware has given rise to the opportunity of using large networks

 of      tiny       sensors,      called      Wireless       Sensor      Networks
 (WSN)~\citep{akyildiz2002wireless,puccinelli2005wireless}, to fulfill monitoring
 tasks.   A  WSN  consists  of  small low-powered  sensors  working  together  by
 communicating with one another through multi-hop radio communications. Each node
 can send the data  it collects in its environment, thanks to  its sensor, to the
 of      tiny       sensors,      called      Wireless       Sensor      Networks
 (WSN)~\citep{akyildiz2002wireless,puccinelli2005wireless}, to fulfill monitoring
 tasks.   A  WSN  consists  of  small low-powered  sensors  working  together  by
 communicating with one another through multi-hop 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,
+user by means of sink nodes. The features  of a WSN makes it suitable for a wide
+range of applications in areas  such as business, environment, health, industry,

 military, and so on~\citep{yick2008wireless}.  Typically, a sensor node contains
 three main components~\citep{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
 military, and so on~\citep{yick2008wireless}.  Typically, a sensor node contains
 three main components~\citep{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.
+communication unit for data transmission and reception.

 
 The energy needed  by an active sensor node to  perform sensing, processing, and
 
 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
+communication is provided 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
 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 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?   These  past   few  years  many
+recharge in most applications. Therefore it is necessary to deploy WSN with high
+density in order  to increase 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 is it possible to extend the lifetime coverage of a WSN as long
+as possible while ensuring a high level  of coverage?  These past few years many

 energy-efficient mechanisms have been suggested  to retain energy and extend the
 lifetime of the WSNs~\citep{rault2014energy}.
 
 energy-efficient mechanisms have been suggested  to retain energy and extend the
 lifetime of the WSNs~\citep{rault2014energy}.
 

-This paper makes the following contributions.
+This paper makes the following contributions :

 \begin{enumerate}
 \begin{enumerate}

-\item  We  have   devised  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
+\item A  framework is devised  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 the proposed  framework is to

   exploit  spatial and  temporal  subdivision.  On  the one  hand,  the area  of
   interest is  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.
   exploit  spatial and  temporal  subdivision.  On  the one  hand,  the area  of
   interest is  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 have proposed 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.
+\item A new  mathematical optimization model is proposed.  Instead  of trying to
+  cover a set of specified points/targets as  in most of the methods proposed in
+  the literature,  a mixed-integer  program based on  the perimeter  coverage of
+  each sensor  is formulated.  The  model involves integer variables  to capture
+  the deviations  between the actual level  of coverage and the  required level.

   Hence, an  optimal schedule will be  obtained by minimizing a  weighted sum of
   these deviations.
   Hence, an  optimal schedule will be  obtained by minimizing a  weighted sum of
   these deviations.

-\item We  have conducted  extensive simulation  experiments, using  the discrete
-  event simulator  OMNeT++, to  demonstrate the efficiency  of our  protocol. We
-  have compared  the PeCO protocol  to two  approaches found in  the literature:
-  DESK~\citep{ChinhVu} and GAF~\citep{xu2001geography}, and also to our previous
-  protocol DiLCO published in~\citep{Idrees2}. DiLCO  uses the same framework as
-  PeCO but is based on another optimization model for sensor scheduling.
+\item Extensive  simulation experiments are  conducted using the  discrete event
+  simulator OMNeT++,  to demonstrate the  efficiency of the PeCO  protocol.  The
+  PeCO protocol  has been compared  to two  approaches found in  the literature:
+  DESK~\citep{ChinhVu} and GAF~\citep{xu2001geography}, and also to the protocol
+  DiLCO published in~\citep{Idrees2}. DiLCO uses  the same framework as PeCO but
+  is based on another optimization model for sensor scheduling.

 \end{enumerate}
 
 The rest of the paper is organized as follows.  In the next section some related
 \end{enumerate}
 
 The rest of the paper is organized as follows.  In the next section some related


@@ -112,9 +109,9 @@ Section~\ref{sec:Conclusion and Future Works}.
 \section{Related Literature}
 \label{sec:Literature Review}
 
 \section{Related Literature}
 \label{sec:Literature Review}
 

-In  this  section,  some  related   works  regarding  the  coverage  problem  is
-summarized, and specific  aspects of the PeCO protocol from  the works presented
-in the literature are presented.
+This section  summarizes some related  works regarding the coverage  problem and
+presents  specific aspects  of the  PeCO protocol  common with  other literature
+works.

 
 The most  discussed coverage problems in  literature can be classified  in three
 categories~\citep{li2013survey}   according  to   their  respective   monitoring
 
 The most  discussed coverage problems in  literature can be classified  in three
 categories~\citep{li2013survey}   according  to   their  respective   monitoring


@@ -124,60 +121,59 @@ to  the objective  of coverage  for a  finite number  of discrete  points called
 targets,   and   barrier  coverage~\citep{HeShibo,kim2013maximum}   focuses   on
 preventing  intruders   from  entering   into  the   region  of   interest.   In
 \citep{Deng2012} authors  transform the  area coverage  problem into  the target
 targets,   and   barrier  coverage~\citep{HeShibo,kim2013maximum}   focuses   on
 preventing  intruders   from  entering   into  the   region  of   interest.   In
 \citep{Deng2012} authors  transform the  area coverage  problem into  the target

-coverage one taking into account the  intersection points among disks of sensors
-nodes    or   between    disk   of    sensor   nodes    and   boundaries.     In
-\citep{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. $d$ denotes  the maximum number of sensors that  are neighbors to a
+coverage one, taking into account the intersection points among disks of sensors
+nodes   or    between   disks    of   sensor    nodes   and    boundaries.    In
+\citep{huang2005coverage} authors  prove that if  the perimeters of  the 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.  $d$ denotes  the maximum  number  of sensors  that are  neighbors to  a

 sensor, and  $n$ is the  total number  of sensors in  the network. {\it  In PeCO
   protocol, instead  of determining the level  of coverage of a  set of discrete
 sensor, and  $n$ is the  total number  of sensors in  the network. {\it  In PeCO
   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
+  points, the 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
   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)\citep{wang2011coverage}, 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  \citep{ling2009energy}.   Active  node selection  is
-determined   based  on   the  problem   requirements  (e.g.    area  monitoring,
-connectivity, or power efficiency).  For instance, \citet{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.    \citet{chin2007},   \citet{yan2008design},  \citet{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, PeCO 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.}
+non-disjoint)  \citep{wang2011coverage},  where  each set  completely  covers  a
+region of interest,  and to successively activate these set covers. 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 decided  at the
+beginning of each  period \citep{ling2009energy}. In fact,  many authors propose
+algorithms       working       in       such      a       periodic       fashion
+\citep{chin2007,yan2008design,pc10}.  Active node  selection is determined based
+on  the problem  requirements  (e.g.  area  monitoring,  connectivity, or  power
+efficiency).  For instance, \citet{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. {\it Motivated
+  by these works,  PeCO 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
 \citep{zhou2009variable}.                      In                    distributed
 
 Various centralized  and distributed approaches, or  even a mixing of  these two
 concepts,    have   been    proposed    to   extend    the   network    lifetime
 \citep{zhou2009variable}.                      In                    distributed

-algorithms~\citep{Tian02,yangnovel,ChinhVu,qu2013distributed}     each    sensor
-decides of  its own activity scheduling  after an information exchange  with its
-neighbors.   The main  interest  of such  an  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 make  a bad  decision leading  to a  global
-suboptimal             solution.             Conversely,             centralized
+algorithms~\citep{ChinhVu,qu2013distributed,yangnovel}  each  sensor decides  of
+its own  activity scheduling after  an information exchange with  its neighbors.
+The main interest of such an approach  is to avoid long range communications and
+thus to reduce the energy dedicated to the communications.  Unfortunately, since
+each node has  information on its immediate neighbors only  (usually the one-hop
+ones),  it may  make a  bad decision  leading to  a global  suboptimal solution.
+Conversely,                                                          centralized

 algorithms~\citep{cardei2005improving,zorbas2010solving,pujari2011high}   always
 algorithms~\citep{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  huge since long
-range communications will be  needed. In fact the larger the  WNS is, the higher
-the communication and  thus the energy cost  are.  {\it In order  to be suitable
-  for  large-scale networks,  in  the PeCO  protocol, the  area  of interest  is
-  divided into  several smaller subregions, and  in each one, a  node called the
-  leader is  in charge of selecting  the active sensors for  the current period.
-  Thus our protocol is scalable and is a globally distributed method, whereas it
-  is centralized in each subregion.}
+provide nearly  optimal solutions 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 huge since  long range
+communications  will be  needed. In  fact  the larger  the WSN,  the higher  the
+communication  energy  cost.  {\it  In  order  to  be suitable  for  large-scale
+  networks, in  the PeCO protocol the  area of interest is  divided into several
+  smaller subregions, and in each one, a  node called the leader is in charge of
+  selecting the active  sensors for the current period.  Thus  the PeCO protocol
+  is scalable  and a globally distributed  method, whereas it is  centralized in
+  each subregion.}

 
 Various coverage scheduling algorithms have been developed these past few years.
 Many of  them, dealing with  the maximization of the  number of cover  sets, are
 
 Various coverage scheduling algorithms have been developed these past few years.
 Many of  them, dealing with  the maximization of the  number of cover  sets, are


@@ -195,54 +191,47 @@ practiced techniques for  solving linear programs with too  many variables, have
 also                                                                        been
 used~\citep{castano2013column,doi:10.1080/0305215X.2012.687732,deschinkel2012column}.
 {\it In  the PeCO  protocol, each leader,  in charge of  a subregion,  solves an
 also                                                                        been
 used~\citep{castano2013column,doi:10.1080/0305215X.2012.687732,deschinkel2012column}.
 {\it In  the PeCO  protocol, each leader,  in charge of  a subregion,  solves an

-  integer program which  has a twofold objective: minimize  the overcoverage and
+  integer program which  has a twofold objective: minimizing  the overcoverage and

   the undercoverage of the perimeter of each sensor.}
 
 The  authors   in  \citep{Idrees2}  propose  a   Distributed  Lifetime  Coverage
 Optimization (DiLCO)  protocol, which  maintains the  coverage and  improves the
 lifetime  in WSNs.   It is  an  improved version  of a  research work  presented
 in~\citep{idrees2014coverage}.  First, the area  of interest is partitioned into
   the undercoverage of the perimeter of each sensor.}
 
 The  authors   in  \citep{Idrees2}  propose  a   Distributed  Lifetime  Coverage
 Optimization (DiLCO)  protocol, which  maintains the  coverage and  improves the
 lifetime  in WSNs.   It is  an  improved version  of a  research work  presented
 in~\citep{idrees2014coverage}.  First, the area  of interest is partitioned into

-subregions using a divide-and-conquer method. DiLCO protocol is then distributed
-on the  sensor nodes  in each  subregion in  a second  step. Hence this protocol
-combines two  techniques: a leader  election in  each subregion, followed  by an
-optimization-based   node  activity   scheduling  performed   by  each   elected
+subregions  using  a  divide-and-conquer  method. The  DiLCO  protocol  is  then
+distributed on the sensor  nodes in each subregion in a  second step. Hence this
+protocol combines two techniques: a  leader election in each subregion, followed
+by  an optimization-based  node activity  scheduling performed  by each  elected

 leader. The proposed DiLCO protocol is  a periodic protocol where each period is
 decomposed into 4  phases: information exchange, leader  election, decision, and
 sensing. The  simulations show that DiLCO  is able to increase  the WSN lifetime
 and provides  improved coverage performance.  {\it  In the PeCO protocol,  a new
   mathematical optimization model is proposed. Instead  of trying to cover a set
 leader. The proposed DiLCO protocol is  a periodic protocol where each period is
 decomposed into 4  phases: information exchange, leader  election, decision, and
 sensing. The  simulations show that DiLCO  is able to increase  the WSN lifetime
 and provides  improved coverage performance.  {\it  In the PeCO protocol,  a new
   mathematical optimization model is proposed. Instead  of trying to cover a set

-  of  specified points/targets  as in  DiLCO protocol,  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. The idea is that an optimal scheduling will be obtained by
-  minimizing a weighted sum of these deviations.}
+  of specified points/targets as in the DiLCO protocol, an integer program based
+  on the  perimeter coverage of  each sensor  is formulated. The  model involves
+  integer  variables to  capture  the  deviations between  the  actual level  of
+  coverage and the  required level. The idea is that  an optimal scheduling will
+  be obtained by minimizing a weighted sum of these deviations.}

   
 \section{ The P{\scshape e}CO Protocol Description}
 \label{sec:The PeCO Protocol Description}
 
   
 \section{ The P{\scshape e}CO Protocol Description}
 \label{sec:The PeCO Protocol Description}
 

-%In  this  section,  the Perimeter-based  Coverage
-%Optimization protocol is decribed in details.  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.
-
-

 \subsection{Assumptions and Models}
 \label{CI}
 
 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
 \subsection{Assumptions and Models}
 \label{CI}
 
 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
+of interest.  All  the sensor nodes are  supposed to be homogeneous  in terms of

 communication, sensing,  and processing capabilities and  heterogeneous from the
 energy provision  point of  view.  The  location information  is available  to a
 sensor node either  through hardware such as embedded GPS  or location discovery
 communication, sensing,  and processing capabilities and  heterogeneous from the
 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 consider a Boolean disk  coverage model, which is the most widely
-used  sensor coverage  model in  the  literature, and  all sensor  nodes have  a
+algorithms. A Boolean disk coverage model,  which is the most widely used sensor
+coverage model  in the  literature, is  considered 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
 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,  \citet{Zhang05} proved  that if  the transmission
+this sensor.  The communication range  $R_c$ is assumed to satisfy : $R_c
+\geq 2  \cdot R_s$.  In  fact, \citet{Zhang05}  proved that if  the transmission

 range fulfills the  previous hypothesis, the complete coverage of  a convex area
 implies connectivity among active nodes.
 
 range fulfills the  previous hypothesis, the complete coverage of  a convex area
 implies connectivity among active nodes.
 


@@ -255,11 +244,11 @@ $k$~sensors) if and only if each  sensor in the network is $k$-perimeter-covered
 (perimeter covered by at least $k$ sensors).
  
 Figure~\ref{figure1}(a) shows the coverage of  sensor node~$0$.  On this figure,
 (perimeter covered by at least $k$ sensors).
  
 Figure~\ref{figure1}(a) shows the coverage of  sensor node~$0$.  On this figure,

-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  the intersection  of the  two sensing  areas.  These  points are
-denoted for neighbor~$i$ by $iL$ and  $iR$, respectively for left and right from
-a  neighboring point  of view.   The  resulting couples  of intersection  points
+sensor~$0$ has nine  neighbors. For each neighbor the two  points resulting from
+the intersection  of the two sensing  areas have been reported  on its perimeter
+(the perimeter of the disk covered by the sensor~$0$).  These points are denoted
+for  neighbor~$i$ by  $iL$ and  $iR$,  respectively for  left and  right from  a
+neighboring  point  of  view.   The resulting  couples  of  intersection  points

 subdivide the perimeter of sensor~$0$ into portions called arcs.
 
 \begin{figure}[ht!]
 subdivide the perimeter of sensor~$0$ into portions called arcs.
 
 \begin{figure}[ht!]


@@ -280,7 +269,7 @@ 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 the
 euclidean distance between nodes~$u$ and $v$ is computed as follows:
 $$
 sensing area~:  $(v_x,v_y)$ and $(u_x,u_y)$.  From the previous  coordinates the
 euclidean distance between nodes~$u$ and $v$ is computed as follows:
 $$

-  Dist(u,v)=\sqrt{\vert u_x - v_x \vert^2 + \vert u_y-v_y \vert^2},
+  Dist(u,v)=\sqrt{(u_x - v_x)^2 + (u_y-v_y)^2},

 $$
 while the angle~$\alpha$ is obtained through the formula:
  \[
 $$
 while the angle~$\alpha$ is obtained through the formula:
  \[


@@ -300,7 +289,7 @@ from the first  intersection point  after  point~zero,  and  the maximum  level
 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 $3$
 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{figure2}), which
+(the value is given at the bottom of Figure~\ref{figure2}), 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
 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


@@ -308,7 +297,7 @@ above is thus given by the sixth line of the table.
 
 \begin{figure*}[t!]
 \centering
 
 \begin{figure*}[t!]
 \centering

-\includegraphics[width=127.5mm]{figure2.eps}  
+\includegraphics[width=0.95\linewidth]{figure2.eps}  

 \caption{Maximum coverage levels for perimeter of sensor node $0$.}
 \label{figure2}
 \end{figure*} 
 \caption{Maximum coverage levels for perimeter of sensor node $0$.}
 \label{figure2}
 \end{figure*} 


@@ -342,7 +331,7 @@ above is thus given by the sixth line of the table.
 \end{table}
 
 In  the  PeCO protocol,  the  scheduling  of  the  sensor nodes'  activities  is
 \end{table}
 
 In  the  PeCO protocol,  the  scheduling  of  the  sensor nodes'  activities  is

-formulated    with    an    mixed-integer     program    based    on    coverage
+formulated    with    a    mixed-integer     program    based    on    coverage

 intervals~\citep{doi:10.1155/2010/926075}.  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
 intervals~\citep{doi:10.1155/2010/926075}.  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


@@ -350,10 +339,10 @@ Figure~\ref{figure3}, 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.
 
 and  the  corresponding  interval  will  not   be  taken  into  account  by  the
 optimization algorithm.
 

-\newpage
+%\newpage

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

-\includegraphics[width=62.5mm]{figure3.eps}  
+\includegraphics[width=57.5mm]{figure3.eps}  

 \caption{Sensing range outside the WSN's area of interest.}
 \label{figure3}
 \end{figure}
 \caption{Sensing range outside the WSN's area of interest.}
 \label{figure3}
 \end{figure}


@@ -363,9 +352,9 @@ optimization algorithm.
 \subsection{Main Idea}
 
 The WSN area of  interest is, in a first step,  divided into regular homogeneous
 \subsection{Main Idea}
 
 The WSN area of  interest is, in a first step,  divided into regular homogeneous

-subregions using a  divide-and-conquer algorithm. In a second  step our protocol
+subregions using a  divide-and-conquer algorithm. In a second  step the protocol

 will  be executed  in  a distributed  way in  each  subregion simultaneously  to
 will  be executed  in  a distributed  way in  each  subregion simultaneously  to

-schedule nodes' activities  for one sensing period. Node Sensors  are assumed to
+schedule nodes' activities  for one sensing period. Sensor nodes  are assumed to

 be deployed  almost uniformly over the  region. The regular subdivision  is made
 such that the number of hops between  any pairs of sensors inside a subregion is
 less than or equal to 3.
 be deployed  almost uniformly over the  region. The regular subdivision  is made
 such that the number of hops between  any pairs of sensors inside a subregion is
 less than or equal to 3.


@@ -391,18 +380,18 @@ of the application.
 
 \begin{figure}[t!]
 \centering
 
 \begin{figure}[t!]
 \centering

-\includegraphics[width=85mm]{figure4.eps}  
+\includegraphics[width=80mm]{figure4.eps}  

 \caption{PeCO protocol.}
 \label{figure4}
 \end{figure} 
 
 \caption{PeCO protocol.}
 \label{figure4}
 \end{figure} 
 

-We define two types of packets to be used by PeCO protocol:
+Two types of packets used by the PeCO protocol are defined:

 \begin{itemize} 
 \item INFO  packet: sent  by each  sensor node to  all the  nodes inside  a same
   subregion for information exchange.
 \item ActiveSleep packet: sent  by the leader to all the  nodes in its subregion
   to transmit to  them their respective status (stay Active  or go Sleep) during
 \begin{itemize} 
 \item INFO  packet: sent  by each  sensor node to  all the  nodes inside  a same
   subregion for information exchange.
 \item ActiveSleep packet: sent  by the leader to all the  nodes in its subregion
   to transmit to  them their respective status (stay Active  or go Sleep) during

-  sensing phase.
+  the sensing phase.

 \end{itemize}
 
 Five statuses are possible for a sensor node in the network:
 \end{itemize}
 
 Five statuses are possible for a sensor node in the network:


@@ -421,12 +410,8 @@ The  pseudocode implementing  the  protocol  on a  node  is  given below.   More
 precisely, Algorithm~\ref{alg:PeCO}  gives a  brief description of  the protocol
 applied by a sensor node $s_k$ where $k$ is the node index in the WSN.
 
 precisely, Algorithm~\ref{alg:PeCO}  gives a  brief description of  the protocol
 applied by a sensor node $s_k$ where $k$ is the node index in the WSN.
 

-

 \begin{algorithm2e}      
 \begin{algorithm2e}      

- % \KwIn{all the parameters related to information exchange}
-%  \KwOut{$winer-node$ (: the id of the winner sensor node, which is the leader of current round)}
-%  \BlankLine
-  %\emph{Initialize the sensor node and determine it's position and subregion} \;
+  \label{alg:PeCO}

   \caption{PeCO pseudocode}
   \eIf{$RE_k \geq E_{th}$}{
     $s_k.status$ = COMMUNICATION\;
   \caption{PeCO pseudocode}
   \eIf{$RE_k \geq E_{th}$}{
     $s_k.status$ = COMMUNICATION\;


@@ -460,75 +445,53 @@ applied by a sensor node $s_k$ where $k$ is the node index in the WSN.
   }
 \end{algorithm2e}
 
   }
 \end{algorithm2e}
 

-%\begin{algorithm}
-%\noindent{\bf If} $RE_k \geq E_{th}$ {\bf then}\\
-%\hspace*{0.6cm} \emph{$s_k.status$ = COMMUNICATION;}\\
-%\hspace*{0.6cm}  \emph{Send $INFO()$ packet to other nodes in subregion;}\\
-%\hspace*{0.6cm}  \emph{Wait $INFO()$ packet from other nodes in subregion;}\\
-%\hspace*{0.6cm} \emph{Update K.CurrentSize;}\\
-%\hspace*{0.6cm}  \emph{LeaderID = Leader election;}\\
-%\hspace*{0.6cm} {\bf If} $ s_k.ID = LeaderID $ {\bf then}\\
-%\hspace*{1.2cm}   \emph{$s_k.status$ = COMPUTATION;}\\
-%\hspace*{1.2cm}{\bf If} \emph{$ s_k.ID $ is Not previously selected as a Leader} {\bf then}\\
-%\hspace*{1.8cm} \emph{ Execute the perimeter coverage model;}\\
-%\hspace*{1.2cm} {\bf end}\\
-%\hspace*{1.2cm}{\bf If} \emph{($s_k.ID $ is the same Previous Leader)~And~(K.CurrentSize = K.PreviousSize)}\\
-%\hspace*{1.8cm} \emph{ Use the same previous cover set for current sensing stage;}\\
-%\hspace*{1.2cm}  {\bf end}\\
-%\hspace*{1.2cm}  {\bf else}\\
-%\hspace*{1.8cm}\emph{Update $a^j_{ik}$; prepare data for IP~Algorithm;}\\
-%\hspace*{1.8cm} \emph{$\left\{\left(X_{1},\dots,X_{l},\dots,X_{K}\right)\right\}$ = Execute Integer Program Algorithm($K$);}\\
-%\hspace*{1.8cm} \emph{K.PreviousSize = K.CurrentSize;}\\
-%\hspace*{1.2cm}  {\bf end}\\
-%\hspace*{1.2cm}\emph{$s_k.status$ = COMMUNICATION;}\\
-%\hspace*{1.2cm}\emph{Send $ActiveSleep()$ to each node $l$ in subregion;}\\
-%\hspace*{1.2cm}\emph{Update $RE_k $;}\\
-%\hspace*{0.6cm}  {\bf end}\\
-%\hspace*{0.6cm}  {\bf else}\\
-%\hspace*{1.2cm}\emph{$s_k.status$ = LISTENING;}\\
-%\hspace*{1.2cm}\emph{Wait $ActiveSleep()$ packet from the Leader;}\\
-%\hspace*{1.2cm}\emph{Update $RE_k $;}\\
-%\hspace*{0.6cm}  {\bf end}\\
-%{\bf end}\\
-%{\bf else}\\
-%\hspace*{0.6cm} \emph{Exclude $s_k$ from entering in the current sensing stage;}\\
-%{\bf end}\\
-%\label{alg:PeCO}
-%\end{algorithm}
-
-In this  algorithm, K.CurrentSize and K.PreviousSize  respectively represent the
-current number and the previous number of  living nodes in the subnetwork of the
-subregion.  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  using an
-embedded GPS  or a  location discovery  algorithm. After  that, all  the sensors
-collect position coordinates,  remaining energy, sensor node ID,  and the number
-of their  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 numbers  of neighbors,  larger
-remaining energy, and then in case  of equality, larger index.  Once chosen, the
-leader collects  information to  formulate and solve  the integer  program which
-allows to construct the set of active sensors in the sensing stage.
-
-% TO BE CONTINUED
+In this  algorithm, $K.CurrentSize$ and $K.PreviousSize$  respectively represent
+the current number and the previous number  of living nodes in the subnetwork of
+the  subregion.   At the  beginning  of  the  first period  $K.PreviousSize$  is
+initialized to  zero.  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 using an  embedded GPS or a location discovery  algorithm. After that,
+all the sensors collect position  coordinates, remaining energy, sensor node ID,
+and the number of their one-hop  live neighbors during the information exchange.
+Both  INFO packet  and ActiveSleep  packet contain  two parts:  header and  data
+payload. The  sensor ID is included  in the header,  where the header size  is 8
+bits. The  data part includes  position coordinates (64 bits),  remaining energy
+(32 bits), and the number of one-hop live neighbors (8 bits). Therefore the size
+of the INFO packet  is 112 bits. The ActiveSleep packet is 16  bits size, 8 bits
+for the header and  8 bits for data part that includes only  sensor status (0 or
+1).   The  sensors inside  a  same  region cooperate  to  elect  a leader.   The
+selection criteria for the leader are (in order of priority):
+\begin{enumerate}
+\item larger number of neighbors;
+\item larger  remaining energy;
+\item and then,  in case  of equality,  larger indexes.
+\end{enumerate}
+Once chosen, the leader collects information  to formulate and solve the integer
+program which allows to build the set of active sensors in the sensing stage.

 
 \section{Perimeter-based Coverage Problem Formulation}
 \label{cp}
 
 
 \section{Perimeter-based Coverage Problem Formulation}
 \label{cp}
 

-In this  section, the perimeter-based coverage problem is  mathematically formulated. It has been proved to be a NP-hard problem by\citep{doi:10.1155/2010/926075}. Authors study the coverage of the perimeter of a large object requiring to be monitored. For the proposed formulation in this paper, the large object to be monitored is the sensor itself (or more precisely its sensing area).
+In  this  section,  the   perimeter-based  coverage  problem  is  mathematically
+formulated.    It    has    been    proved   to    be    a    NP-hard    problem
+by \citep{doi:10.1155/2010/926075}. Authors  study the coverage of  the perimeter
+of a  large object requiring  to be monitored.  For the proposed  formulation in
+this paper,  the large  object to  be monitored  is the  sensor itself  (or more
+precisely its sensing area).
+
+The following notations are used  throughout the section.

 
 

-The following notations are used  throughout the
-section.\\

 First, the following sets:
 \begin{itemize}
 First, the following sets:
 \begin{itemize}

-\item $S$ represents the set of WSN sensor nodes;
+\item $S$ represents the set of sensor nodes;

 \item $A \subseteq S $ is the subset of alive sensors;
 \item  $I_j$  designates  the  set  of  coverage  intervals  (CI)  obtained  for
   sensor~$j$.
 \end{itemize}
 \item $A \subseteq S $ is the subset of alive sensors;
 \item  $I_j$  designates  the  set  of  coverage  intervals  (CI)  obtained  for
   sensor~$j$.
 \end{itemize}

-$I_j$ refers to the set of  coverage intervals which have been defined according
-to the  method introduced in  subsection~\ref{CI}. For a coverage  interval $i$,
-let $a^j_{ik}$ denotes  the indicator function of whether  sensor~$k$ is involved
+$I_j$ refers to the set of  coverage intervals which has been defined according
+to the  method introduced in  Subsection~\ref{CI}. For a coverage  interval $i$,
+let $a^j_{ik}$ denote  the indicator function of whether  sensor~$k$ is involved

 in coverage interval~$i$ of sensor~$j$, that is:
 \begin{equation}
 a^j_{ik} = \left \{ 
 in coverage interval~$i$ of sensor~$j$, that is:
 \begin{equation}
 a^j_{ik} = \left \{ 


@@ -540,130 +503,129 @@ a^j_{ik} = \left \{
 \end{equation}
 Note that $a^k_{ik}=1$ by definition of the interval.
 
 \end{equation}
 Note that $a^k_{ik}=1$ by definition of the interval.
 

-Second, several variables are defined.  Hence,  each binary
-variable $X_{k}$  determines the activation of  sensor $k$ in the  sensing phase
-($X_k=1$ if  the sensor $k$  is active or 0  otherwise).  $M^j_i$ is  a
-variable  which  measures  the  undercoverage  for  the  coverage  interval  $i$
-corresponding to  sensor~$j$. In  the same  way, the  overcoverage for  the same
-coverage interval is given by the variable $V^j_i$.
-
-To sustain a level of coverage equal to $l$ all along the perimeter
-of sensor  $j$, at least  $l$ sensors involved  in each
-coverage  interval $i  \in I_j$  of  sensor $j$ have to be active.   According to  the
-previous notations, the number of active sensors in the coverage interval $i$ of
-sensor $j$  is given by  $\sum_{k \in A} a^j_{ik}  X_k$.  To extend  the network
-lifetime,  the objective  is to  activate 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 reach  the desired level of coverage for all
-coverage intervals. Therefore  variables  $M^j_i$ and $V^j_i$ are introduced as a measure
-of the  deviation between  the desired  number of active  sensors in  a coverage
-interval and  the effective  number. 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 cannot be completely satisfied,
-to reach a coverage level as close as possible to the desired one.
-
-
-
-
-The coverage optimization problem can then be mathematically expressed as follows: 
-
-\begin{equation} 
-\left \{
-\begin{array}{ll}
-\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 :}&\\
-\sum_{k \in A} ( a^j_{ik} ~ X_{k}) + M^j_i  \geq l \quad \forall i \in I_j, \forall j \in S\\
-\sum_{k \in A} ( a^j_{ik} ~ X_{k}) - V^j_i  \leq l \quad \forall i \in I_j, \forall j \in S\\
-X_{k} \in \{0,1\}, \forall k \in A \\
-M^j_i, V^j_i \in  \mathbb{R}^{+}
-\end{array}
-\right.
+Second,  several variables  are defined.   Hence, each  binary variable  $X_{k}$
+determines the  activation of sensor  $k$ in the  sensing phase ($X_k=1$  if the
+sensor $k$ is active or 0 otherwise).   $M^j_i$ is a variable which measures the
+undercoverage for the coverage interval  $i$ corresponding to sensor~$j$. In the
+same  way, the  overcoverage for  the  same coverage  interval is  given by  the
+variable $V^j_i$.
+
+To sustain a  level of coverage equal  to $l$ all along the  perimeter of sensor
+$j$, at  least $l$  sensors involved in  each coverage interval  $i \in  I_j$ of
+sensor $j$ have  to be active.  According to the  previous notations, the number
+of  active sensors  in the  coverage  interval $i$  of  sensor $j$  is given  by
+$\sum_{k \in A} a^j_{ik} X_k$.  To extend the network lifetime, the objective is
+to activate  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 reach  the desired level  of coverage  for all coverage  intervals. Therefore
+variables  $M^j_i$ and  $V^j_i$ are  introduced as  a measure  of the  deviation
+between the  desired number  of active  sensors in a  coverage interval  and the
+effective number.  And these deviations are minimized, first  to force the
+activation of a minimal number of  sensors to ensure the desired coverage level,
+and if  the desired level  cannot be completely  satisfied, to reach  a coverage
+level as close as possible to the desired one.
+
+The coverage optimization problem can then be mathematically expressed as follows:
+\begin{equation}
+  \begin{aligned}
+    \text{Minimize } & \sum_{j \in S} \sum_{i \in I_j} (\alpha^j_i ~ M^j_i + \beta^j_i ~ V^j_i ) \\
+    \text{Subject to:} & \\
+    & \sum_{k \in A} ( a^j_{ik} ~ X_{k}) + M^j_i  \geq l \quad \forall i \in I_j, \forall j \in S  \\
+    & \sum_{k \in A} ( a^j_{ik} ~ X_{k}) - V^j_i  \leq l \quad \forall i \in I_j, \forall j \in S \\
+    & X_{k} \in \{0,1\}, \forall k \in A \\
+    & M^j_i, V^j_i \in \mathbb{R}^{+} 
+  \end{aligned}

 \end{equation}
 
 \end{equation}
 

-If a given level of coverage $l$ is required  for one sensor, the sensor is said to be undercovered (respectively overcovered) if the level of coverage of one of its CI is less (respectively greater) than $l$. If the sensor $j$ is undercovered, there exists at least one of its CI (say $i$) for which the number of active sensors (denoted by $l^{i}$) covering this part of the perimeter is less than $l$ and in this case : $M_{i}^{j}=l-l^{i}$, $V_{i}^{j}=0$. In the contrary, if the sensor $j$ is overcovered, there exists at least one of its CI (say $i$) for which the number of active sensors (denoted by $l^{i}$) covering this part of the perimeter is greater than $l$ and in this case : $M_{i}^{j}=0$, $V_{i}^{j}=l^{i}-l$.  
+
+If a given level of coverage $l$ is  required for one sensor, the sensor is said
+to be undercovered (respectively overcovered) if the level of coverage of one of
+its  CI  is  less  (respectively  greater)  than $l$.   If  the  sensor  $j$  is
+undercovered, there exists at least one of its CI (say $i$) for which the number
+of active  sensors (denoted by $l^{i}$)  covering this part of  the perimeter is
+less than $l$ and in this case : $M_{i}^{j}=l-l^{i}$, $V_{i}^{j}=0$. Conversely,
+if the sensor $j$ is overcovered, there exists  at least one of its CI (say $i$)
+for which the  number of active sensors (denoted by  $l^{i}$) covering this part
+of  the  perimeter  is  greater  than  $l$  and  in  this  case:  $M_{i}^{j}=0$,
+$V_{i}^{j}=l^{i}-l$.

 
 $\alpha^j_i$ and $\beta^j_i$  are nonnegative weights selected  according to the
 relative importance of satisfying the associated level of coverage. For example,
 
 $\alpha^j_i$ 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 by a  relatively larger  magnitude than  weights associated  with another
-region. This  kind of mixed-integer program  is inspired from the  model developed for
-brachytherapy treatment planning  for optimizing dose  distribution
-\citep{0031-9155-44-1-012}.  The choice of variables $\alpha$ and $\beta$ should be made according to the needs of the application. $\alpha$ should be enough large to prevent undercoverage and so to reach the highest possible coverage ratio. $\beta$ should be enough large to prevent overcoverage and so to activate a minimum number of sensors. 
-The mixed-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 
-only alive  sensors (sensors with enough energy to  be alive during one
-sensing phase) are considered in the model. 
+weights associated with  coverage intervals of the specified part  of a region may
+be given by  a relatively larger magnitude than weights  associated with another
+region. This kind of mixed-integer program  is inspired from the model developed
+for   brachytherapy  treatment   planning  to optimize  dose   distribution
+\citep{0031-9155-44-1-012}.  The choice of the values for variables $\alpha$ and
+$\beta$  should be  made according  to the  needs of  the application.  $\alpha$
+should be  large enough  to prevent  undercoverage and so  to reach  the highest
+possible coverage ratio. $\beta$ should  be large enough to prevent overcoverage
+and so to activate a minimum  number of sensors.  The mixed-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 only alive  sensors (sensors with enough energy to
+be alive during one sensing phase) are considered in the model.

 
 \section{Performance Evaluation and Analysis}  
 \label{sec:Simulation Results and Analysis}
 
 
 \section{Performance Evaluation and Analysis}  
 \label{sec:Simulation Results and Analysis}
 

-

 \subsection{Simulation Settings}
 
 \subsection{Simulation Settings}
 

-

 The WSN  area of interest is  supposed to be divided  into 16~regular subregions
 The WSN  area of interest is  supposed to be divided  into 16~regular subregions

-and we use the same energy consumption model as in our previous work~\citep{Idrees2}.
-Table~\ref{table3} gives the chosen parameters settings.
+and the  energy  consumption   model  used is described in previous
+work~\citep{Idrees2}.  Table~\ref{table3} gives the chosen parameters settings.

 
 \begin{table}[ht]
 \tbl{Relevant parameters for network initialization \label{table3}}{
 
 \begin{table}[ht]
 \tbl{Relevant parameters for network initialization \label{table3}}{

-

 \centering
 \centering

-

 \begin{tabular}{c|c}
 \begin{tabular}{c|c}

-

 \hline
 Parameter & Value  \\ [0.5ex]
 \hline
 Parameter & Value  \\ [0.5ex]

-   

 \hline
 % inserts single horizontal line
 \hline
 % inserts single horizontal line

-Sensing field & $(50 \times 25)~m^2 $   \\
-
-WSN size &  100, 150, 200, 250, and 300~nodes   \\
-
-Initial energy  & in range 500-700~Joules  \\  
-
+Sensing field & $(50 \times 25)~m^2 $ \\
+WSN size &  100, 150, 200, 250, and 300~nodes \\
+Initial energy  & in range 500-700~Joules \\  

 Sensing period & duration of 60 minutes \\
 Sensing period & duration of 60 minutes \\

-$E_{th}$ & 36~Joules\\
-$R_s$ & 5~m   \\     
-$R_c$ & 10~m   \\   
-$\alpha^j_i$ & 0.6   \\
-
+$E_{th}$ & 36~Joules \\
+$R_s$ & 5~m \\     
+$R_c$ & 10~m \\   
+$\alpha^j_i$ & 0.6 \\

 $\beta^j_i$ & 0.4
 $\beta^j_i$ & 0.4

-

 \end{tabular}}
 \end{tabular}}

-
-

 \end{table}
 \end{table}

+

 To  obtain  experimental  results  which are  relevant,  simulations  with  five
 different node densities going from  100 to 300~nodes were performed considering
 each time 25~randomly  generated networks. 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
 To  obtain  experimental  results  which are  relevant,  simulations  with  five
 different node densities going from  100 to 300~nodes were performed considering
 each time 25~randomly  generated networks. 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 its energy provision reaches a
+randomly drawn in  the interval $[500-700]$.  If its energy  provision reaches a

 value below  the threshold $E_{th}=36$~Joules,  the minimum energy needed  for a
 value below  the threshold $E_{th}=36$~Joules,  the minimum energy needed  for a

-node  to stay  active during  one period,  it will  no longer  participate in  the
+node to  stay active  during one period,  it will no  longer participate  in the

 coverage task. This value corresponds to the energy needed by the sensing phase,
 coverage task. This value corresponds to the energy needed by the sensing phase,

-obtained by multiplying  the energy consumed in the active state  (9.72 mW) with the
-time in  seconds for one  period (3600 seconds), and  adding the energy  for the
+obtained by multiplying  the energy consumed in the active  state (9.72 mW) with
+the time in seconds for one period (3600 seconds), and adding the energy for the

 pre-sensing phases.  According  to the interval of initial energy,  a sensor may
 pre-sensing phases.  According  to the interval of initial energy,  a sensor may

-be active during at most 20 periods.
+be active  during at  most 20  periods. the information  exchange to  update the
+coverage is executed every  hour, but the length of the  sensing period could be
+reduced and  adapted dynamically. On the  one hand a small  sensing period would
+allow  the  network to  be  more  reliable but  would  have  resulted in  higher
+communication costs.  On the other hand the  choice of a long duration may cause
+problems in case of nodes failure during the sensing period.

 
 The values  of $\alpha^j_i$ and  $\beta^j_i$ have been  chosen to ensure  a good
 
 The values  of $\alpha^j_i$ and  $\beta^j_i$ have been  chosen to ensure  a good

-network coverage and a longer WSN lifetime.  Higher priority is given to
-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,  
-$\beta^j_i$ is assigned to a value which is slightly lower so as to minimize the number of active sensor nodes which contribute
-in covering the interval.
+network coverage  and a longer  WSN lifetime.  Higher  priority is given  to 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, $\beta^j_i$ is assigned to a value which is slightly lower so as
+to minimize the  number of active sensor nodes which  contribute in covering the
+interval. Subsection~\ref{sec:Impact} investigates more deeply how the values of
+both parameters affect the performance of the PeCO protocol.

 
 The following performance metrics are used to evaluate the efficiency of the
 approach.
 
 The following performance metrics are used to evaluate the efficiency of the
 approach.

-
-

 \begin{itemize}
 \item {\bf Network Lifetime}: the lifetime  is defined as the time elapsed until
   the  coverage  ratio  falls  below a  fixed  threshold.   $Lifetime_{95}$  and
 \begin{itemize}
 \item {\bf Network Lifetime}: the lifetime  is defined as the time elapsed until
   the  coverage  ratio  falls  below a  fixed  threshold.   $Lifetime_{95}$  and


@@ -674,44 +636,44 @@ approach.
   because without  network connectivity a  sensor may not be  able to send  to a
   base station an event it has sensed.
 \item {\bf  Coverage Ratio (CR)} : it  measures how  well the  WSN is  able to
   because without  network connectivity a  sensor may not be  able to send  to a
   base station an event it has sensed.
 \item {\bf  Coverage Ratio (CR)} : it  measures how  well the  WSN is  able to

-  observe the area of interest. In our  case, the sensor field is discretized as
+  observe the area of interest. Here the sensor field is discretized as

   a regular grid, which yields the following equation:
   a regular grid, which yields the following equation:

-  
-
-\[
+  \begin{equation*}

     \scriptsize
     \mbox{CR}(\%) = \frac{\mbox{$n$}}{\mbox{$N$}} \times 100
     \scriptsize
     \mbox{CR}(\%) = \frac{\mbox{$n$}}{\mbox{$N$}} \times 100

-\]
-
-
+  \end{equation*}

   where $n$  is the  number of covered  grid points by  active sensors  of every
   subregions during  the current sensing phase  and $N$ is total  number of grid
   where $n$  is the  number of covered  grid points by  active sensors  of every
   subregions during  the current sensing phase  and $N$ is total  number of grid

-  points in  the sensing  field.  In  simulations  a  layout of
-  $N~=~51~\times~26~=~1326$~grid points is considered.
-\item {\bf Active Sensors Ratio (ASR)}: a  major objective of our protocol is to
-  activate  as few nodes as possible,  in order  to minimize  the communication
-  overhead and maximize the WSN lifetime. The active sensors ratio is defined as
-  follows:
- 
-\[
-    \scriptsize
-    \mbox{ASR}(\%) =  \frac{\sum\limits_{r=1}^R \mbox{$|A_r^p|$}}{\mbox{$|J|$}} \times 100
-\]
-
+  points in the sensing field. A layout of $N~=~51~\times~26~=~1326$~grid points
+  is considered in the simulations.
+\item  {\bf Active  Sensors  Ratio (ASR)}:  a major  objective  of the  proposed
+  protocol is  to activate as  few nodes as possible,  in order to  minimize the
+  communication overhead and maximize the WSN 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 
+  \end{equation*}

   where $|A_r^p|$ is  the number of active  sensors in the subregion  $r$ in the
   where $|A_r^p|$ is  the number of active  sensors in the subregion  $r$ in the

-  current sensing period~$p$, $|J|$ is the number of sensors in the network, and
-  $R$ is the number of subregions.
+  sensing period~$p$, $R$  is the number of subregions, and  $|J|$ is the number
+  of sensors in the network.
+  
+\item {\bf Energy Saving Ratio (ESR)}: this metric, which shows the ability of a
+  protocol to save energy, is defined by:
+\begin{equation*}
+\scriptsize
+\mbox{ESR}(\%) = \frac{\mbox{Number of alive sensors during this round}}
+{\mbox{Total number of sensors in the network}} \times 100.
+\end{equation*}  

 \item {\bf Energy Consumption (EC)}: energy consumption can be seen as the total
   energy  consumed by  the  sensors during  $Lifetime_{95}$ or  $Lifetime_{50}$,
   divided by  the number of  periods. The value of  EC is computed  according to
   this formula:
 \item {\bf Energy Consumption (EC)}: energy consumption can be seen as the total
   energy  consumed by  the  sensors during  $Lifetime_{95}$ or  $Lifetime_{50}$,
   divided by  the number of  periods. The value of  EC is computed  according to
   this formula:

-
-\[  
-  \scriptsize
+  \begin{equation*} 
+    \scriptsize

     \mbox{EC} = \frac{\sum\limits_{p=1}^{P} \left( E^{\mbox{com}}_p+E^{\mbox{list}}_p+E^{\mbox{comp}}_p  
       + E^{a}_p+E^{s}_p \right)}{P},
     \mbox{EC} = \frac{\sum\limits_{p=1}^{P} \left( E^{\mbox{com}}_p+E^{\mbox{list}}_p+E^{\mbox{comp}}_p  
       + E^{a}_p+E^{s}_p \right)}{P},

-\]
- 
+  \end{equation*}

   where $P$ corresponds  to the number of periods. The  total energy consumed by
   the  sensors  comes  through  taking   into  consideration  four  main  energy
   factors. The first one, denoted $E^{\scriptsize \mbox{com}}_p$, represents the
   where $P$ corresponds  to the number of periods. The  total energy consumed by
   the  sensors  comes  through  taking   into  consideration  four  main  energy
   factors. The first one, denoted $E^{\scriptsize \mbox{com}}_p$, represents the


@@ -720,57 +682,88 @@ approach.
   the energy  consumed by the sensors  in LISTENING status before  receiving the
   decision to go active or sleep in period $p$.  $E^{\scriptsize \mbox{comp}}_p$
   refers to  the energy  needed by  all the  leader nodes  to solve  the integer
   the energy  consumed by the sensors  in LISTENING status before  receiving the
   decision to go active or sleep in period $p$.  $E^{\scriptsize \mbox{comp}}_p$
   refers to  the energy  needed by  all the  leader nodes  to solve  the integer

-  program during a period.  Finally, $E^a_{p}$ and $E^s_{p}$ indicate the energy
-  consumed by the WSN during the sensing phase (active and sleeping nodes).
+  program  during  a  period   (COMPUTATION  status).   Finally,  $E^a_{p}$  and
+  $E^s_{p}$ indicate  the energy consumed  by the  WSN during the  sensing phase
+  ({\it active} and {\it sleeping} nodes).

 \end{itemize}
 
 \end{itemize}
 

-

 \subsection{Simulation Results}
 
 \subsection{Simulation Results}
 

-In  order  to  assess and  analyze  the  performance  of  our protocol  we  have
-implemented PeCO protocol in  OMNeT++~\citep{varga} simulator.  Besides PeCO, two
-other  protocols,  described  in  the  next paragraph,  will  be  evaluated  for
-comparison purposes.   The simulations were run  on a DELL laptop  with an Intel
-Core~i3~2370~M (1.8~GHz)  processor (2  cores) whose MIPS  (Million Instructions
-Per Second) rate  is equal to 35330. To  be consistent with the use  of a sensor
-node based on  Atmels AVR ATmega103L microcontroller (6~MHz) having  a MIPS rate
-equal to 6,  the original execution time  on the laptop is  multiplied by 2944.2
-$\left(\frac{35330}{2} \times  \frac{1}{6} \right)$.  The modeling  language for
-Mathematical Programming (AMPL)~\citep{AMPL} is  employed to generate the integer
-program instance  in a  standard format, which  is then read  and solved  by the
-optimization solver  GLPK (GNU  linear Programming Kit  available in  the public
-domain) \citep{glpk} through a Branch-and-Bound method.
-
-As said previously, the PeCO is  compared to three other approaches. The first
-one,  called  DESK,  is  a  fully distributed  coverage  algorithm  proposed  by
-\citep{ChinhVu}. The second one,  called GAF~\citep{xu2001geography}, consists in
-dividing  the monitoring  area into  fixed  squares. Then,  during the  decision
-phase, in each square, one sensor is  chosen to remain active during the sensing
-phase. The last  one, the DiLCO protocol~\citep{Idrees2}, is  an improved version
-of a research work we presented in~\citep{idrees2014coverage}. Let us notice that
-PeCO and  DiLCO protocols are  based on the  same framework. In  particular, the
-choice for the simulations of a partitioning in 16~subregions was made because
-it corresponds to the configuration producing  the best results for DiLCO. The
-protocols are distinguished  from one another by the formulation  of the integer
-program providing the set of sensors which  have to be activated in each sensing
-phase. DiLCO protocol tries to satisfy the coverage of a set of primary points,
-whereas the PeCO protocol objective is to reach a desired level of coverage for each
-sensor perimeter. In our experimentations, we chose a level of coverage equal to
-one ($l=1$).
-
-\subsubsection{\bf Coverage Ratio}
-
-Figure~\ref{figure5}  shows the  average coverage  ratio for  200 deployed  nodes
-obtained with the  four protocols. DESK, GAF, and DiLCO  provide a slightly better
-coverage ratio with respectively 99.99\%,  99.91\%, and 99.02\%, compared to the 98.76\%
-produced by  PeCO for the  first periods. This  is due to  the fact that  at the
-beginning the DiLCO protocol  puts to  sleep status  more redundant  sensors (which
-slightly decreases the coverage ratio), while the three other protocols activate
-more sensor  nodes. Later, when the  number of periods is  beyond~70, it clearly
-appears that  PeCO 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). The energy saved by  PeCO in the early periods allows later a
-substantial increase of the coverage performance.
+The PeCO  protocol has  been implemented  in OMNeT++~\citep{varga}  simulator in
+order to assess and analyze its performance.  The simulations were run on a DELL
+laptop with  an Intel  Core~i3~2370~M (1.8~GHz) processor  (2 cores)  whose MIPS
+(Million Instructions Per Second) rate is equal to 35330.  To be consistent with
+the use of a sensor node  based on Atmels AVR ATmega103L microcontroller (6~MHz)
+having a  MIPS rate equal  to 6,  the original execution  time on the  laptop is
+multiplied by 2944.2 $\left(\frac{35330}{2} \times \frac{1}{6} \right)$.  Energy
+consumption  is  calculated  according  to  the  power  consumption  values,  in
+milliWatt per  second, given  in Table~\ref{tab:EC}, based  on the  energy model
+proposed in \citep{ChinhVu}.
+
+\begin{table}[h]
+\centering
+\caption{Power consumption values}
+\label{tab:EC}
+\begin{tabular}{|l||cccc|}
+  \hline
+  {\bf Sensor status} & MCU & Radio & Sensing & {\it Power (mW)} \\
+  \hline
+  LISTENING & On & On & On & 20.05 \\
+  ACTIVE & On & Off & On & 9.72 \\
+  SLEEP & Off & Off & Off & 0.02 \\
+  COMPUTATION & On & On & On & 26.83 \\
+  \hline
+  \multicolumn{4}{|l}{Energy needed to send or receive a 2-bit content message} & 0.515 \\
+  \hline
+\end{tabular}
+\end{table}
+
+The modeling  language for Mathematical Programming  (AMPL)~\citep{AMPL} is used
+to generate  the integer program  instance in a  standard format, which  is then
+read and  solved by  the optimization  solver GLPK  (GNU Linear  Programming Kit
+available in the public domain)  \citep{glpk} through a Branch-and-Bound method.
+In practice, executing GLPK on a sensor node is obviously intractable due to the
+huge memory  use. Fortunately,  to solve  the optimization  problem, the  use of
+commercial  solvers  like  CPLEX  \citep{iamigo:cplex}  which  are  less  memory
+consuming and  more efficient  is possible,  or a  lightweight heuristic  may be
+implemented. For example,  for a WSN of  200 sensor nodes, a leader  node has to
+deal with constraints induced by about 12  sensor nodes.  In that case, to solve
+the optimization problem a memory consumption  of more than 1~MB can be observed
+with GLPK, whereas less than 300~KB would be needed with CPLEX.
+
+Besides  PeCO,   three  other  protocols   will  be  evaluated   for  comparison
+purposes. The first one, called DESK,  is a fully distributed coverage algorithm
+proposed      by     \citep{ChinhVu}.       The      second     one,      called
+GAF~\citep{xu2001geography}, consists in dividing the monitoring area into fixed
+squares. Then, during  the decision phase, in each square,  one sensor is chosen
+to  remain  active   during  the  sensing  phase.   The  last   one,  the  DiLCO
+protocol~\citep{Idrees2}, is an improved version of a research work presented
+in~\citep{idrees2014coverage}. PeCO  and DiLCO protocols
+are based on  the same framework. In particular, the  choice for the simulations
+of  a partitioning  in  16~subregions was  made because  it  corresponds to  the
+configuration producing  the best results for  DiLCO. Of course, this  number of
+subregions should be adapted  according to the size of the  area of interest and
+the number of sensors.  The protocols  are distinguished from one another by the
+formulation of the integer program providing the set of sensors which have to be
+activated  in each  sensing  phase.  The DiLCO  protocol  tries  to satisfy  the
+coverage of a set of primary points,  whereas the objective of the PeCO protocol
+is  to reach  a desired  level of  coverage for  each sensor  perimeter. In the
+experimentations, a level of coverage equal to one ($l=1$) is chosen
+.
+
+\subsubsection{Coverage Ratio}
+
+Figure~\ref{figure5} shows  the average  coverage ratio  for 200  deployed nodes
+obtained with the four protocols. DESK, GAF, and DiLCO provide a slightly better
+coverage ratio with respectively 99.99\%,  99.91\%, and 99.02\%, compared to the
+98.76\% produced by PeCO for the first periods.  This is due to the fact that at
+the beginning the  DiLCO and PeCO protocols put more  redundant sensors to sleep
+status  (which slightly  decreases  the  coverage ratio),  while  the two  other
+protocols activate  more sensor  nodes.  Later,  when the  number of  periods is
+beyond~70, it  clearly appears that  PeCO 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). The  energy saved by PeCO in the early periods
+allows later a substantial increase of the coverage performance.

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


@@ -780,20 +773,17 @@ substantial increase of the coverage performance.
 \label{figure5}
 \end{figure} 
 
 \label{figure5}
 \end{figure} 
 

+\subsubsection{Active Sensors Ratio}

 
 

-
-
-\subsubsection{\bf Active Sensors Ratio}
-
-Having the less active sensor nodes in  each period is essential to minimize the
-energy consumption  and thus to  maximize the network  lifetime.  Figure~\ref{figure6}
-shows the  average active nodes ratio  for 200 deployed nodes.   We observe that
-DESK and  GAF have 30.36  \% and  34.96 \% active  nodes for the  first fourteen
-rounds and  DiLCO and PeCO  protocols compete perfectly  with only 17.92~\% and
-20.16~\% active  nodes during the same  time interval. As the  number of periods
-increases, PeCO protocol  has a lower number of active  nodes in comparison with
-the three other approaches, while keeping a greater coverage ratio as shown in
-Figure \ref{figure5}.
+Minimizing the  number of  active sensor  nodes in each  period is  essential to
+minimize  the energy  consumption and  thus  to maximize  the network  lifetime.
+Figure~\ref{figure6}  shows the  average  active nodes  ratio  for 200  deployed
+nodes.  DESK and  GAF have  30.36~\%  and 34.96~\%  active nodes  for the  first
+fourteen rounds,  and the DiLCO and  PeCO protocols compete perfectly  with only
+17.92~\% and 20.16~\% active nodes during  the same time interval. As the number
+of periods increases,  the PeCO protocol has  a lower number of  active nodes in
+comparison with the  three other approaches and exhibits a  slow decrease, while
+keeping a greater coverage ratio as shown in Figure \ref{figure5}.

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


@@ -802,83 +792,117 @@ Figure \ref{figure5}.
 \label{figure6}
 \end{figure} 
 
 \label{figure6}
 \end{figure} 
 

-\subsubsection{\bf Energy Consumption}
-
-We studied the effect of the energy  consumed by the WSN during the communication,
-computation, listening, active, and sleep status for different network densities
-and  compared  it for  the  four  approaches.  Figures~\ref{figure7}(a)  and  (b)
-illustrate  the  energy   consumption  for  different  network   sizes  and  for
-$Lifetime95$ and  $Lifetime50$. The results show  that our PeCO protocol  is the
-most competitive  from the energy  consumption point of  view. As shown  in both
-figures, PeCO 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 the  optimization program  allows to
-reduce significantly the number of active  sensors and so the energy consumption
-while keeping a good coverage level.
+\subsubsection{Energy Saving Ratio} 
+
+The simulation results  show that the protocol PeCO saves  efficiently energy by
+turning   off  some   sensors   during   the  sensing   phase.    As  shown   in
+Figure~\ref{figure7}, GAF provides better energy  saving than PeCO for the first
+fifty  rounds. Indeed  GAF balances  the energy  consumption among  sensor nodes
+inside each small fixed  grid and thus permits to extend the  life of sensors in
+each  grid fairly.  However, at  the same  time it  turns on  a large  number of
+sensors  and that  leads  later  to quickly  deplete  sensor's batteries.   DESK
+algorithm  shows  less  energy  saving   compared  with  other  approaches.   In
+comparison  with  PeCO, DiLCO  protocol  usually  provides lower  energy  saving
+ratios.  Moreover, it  can  be noticed  that after  round  fifty, PeCO  protocol
+exhibits the slowest decrease among all the considered protocols.
+
+\begin{figure}[h!]
+%\centering
+% \begin{multicols}{6}
+\centering
+\includegraphics[scale=0.5]{figure7.eps} %\\~ ~ ~(a)
+\caption{Energy Saving Ratio for 200 deployed nodes.}
+\label{figure7}
+\end{figure}
+
+\subsubsection{Energy Consumption}
+
+The  effect  of  the  energy  consumed by  the  WSN  during  the  communication,
+computation,  listening,  active, and  sleep  status  is studied  for  different
+network densities  and the  four approaches  compared.  Figures~\ref{figure8}(a)
+and (b)  illustrate the energy consumption  for different network sizes  and for
+$Lifetime_{95}$ and $Lifetime_{50}$.  The results show that the PeCO protocol is
+the most competitive from the energy consumption point of view. As shown by both
+figures, PeCO consumes much less energy  than the 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  the optimization  program  allows to  reduce
+significantly the number of active sensors and also the energy consumption while
+keeping a good coverage level. The  energy overhead when increasing network size
+is the lowest with PeCO.

 
 \begin{figure}[h!]
   \centering
   \begin{tabular}{@{}cr@{}}
 
 \begin{figure}[h!]
   \centering
   \begin{tabular}{@{}cr@{}}

-    \includegraphics[scale=0.475]{figure7a.eps} & \raisebox{2.75cm}{(a)} \\
-    \includegraphics[scale=0.475]{figure7b.eps} & \raisebox{2.75cm}{(b)}
+    \includegraphics[scale=0.5]{figure8a.eps} & \raisebox{2.75cm}{(a)} \\
+    \includegraphics[scale=0.5]{figure8b.eps} & \raisebox{2.75cm}{(b)}

   \end{tabular}
   \caption{Energy consumption per period for (a)~$Lifetime_{95}$ and (b)~$Lifetime_{50}$.}
   \end{tabular}
   \caption{Energy consumption per period for (a)~$Lifetime_{95}$ and (b)~$Lifetime_{50}$.}

-  \label{figure7}
+  \label{figure8}

 \end{figure} 
 
 \end{figure} 
 

+\subsubsection{Network Lifetime}

 
 

-
-\subsubsection{\bf Network Lifetime}
-
-We observe the superiority of PeCO and DiLCO protocols in comparison with the
-two    other   approaches    in    prolonging   the    network   lifetime.    In
-Figures~\ref{figure8}(a)  and (b),  $Lifetime95$ and  $Lifetime50$ are  shown for
-different  network  sizes.   As  highlighted  by  these  figures,  the  lifetime
-increases with the size  of the network, and it is clearly   largest for DiLCO
-and PeCO  protocols.  For instance,  for a  network of 300~sensors  and coverage
-ratio greater than 50\%, we can  see on Figure~\ref{figure8}(b) that the lifetime
-is about twice longer with  PeCO compared to DESK protocol.  The performance
-difference    is    more    obvious   in    Figure~\ref{figure8}(b)    than    in
-Figure~\ref{figure8}(a) because the gain induced  by our protocols increases with
- time, and the lifetime with a coverage  over 50\% is far  longer than with
-95\%. 
+In comparison with the two other approaches, PeCO and DiLCO protocols are better
+for  prolonging  the network  lifetime.   In  Figures~\ref{figure9}(a) and  (b),
+$Lifetime_{95}$ and $Lifetime_{50}$  are shown for different  network sizes.  As
+can  be seen  in these  figures, the  lifetime increases  with the  size of  the
+network,  and it  is  clearly larger  for  the DiLCO  and  PeCO protocols.   For
+instance, for a network of 300~sensors  and coverage ratio greater than 50\%, it
+can  be observed  on Figure~\ref{figure9}(b)  that the  lifetime is  about twice
+longer with PeCO  compared to the DESK protocol.  The  performance difference is
+more obvious in Figure~\ref{figure9}(b)  than in Figure~\ref{figure9}(a) because
+the gain  induced by  protocols (PeCO  and DiLCO) increases  with time,  and the
+lifetime with a coverage over 50\% is far longer than with 95\%.

 
 \begin{figure}[h!]
   \centering
   \begin{tabular}{@{}cr@{}}
 
 \begin{figure}[h!]
   \centering
   \begin{tabular}{@{}cr@{}}

-    \includegraphics[scale=0.475]{figure8a.eps} & \raisebox{2.75cm}{(a)} \\  
-    \includegraphics[scale=0.475]{figure8b.eps} & \raisebox{2.75cm}{(b)}
+    \includegraphics[scale=0.5]{figure9a.eps} & \raisebox{2.75cm}{(a)} \\  
+    \includegraphics[scale=0.5]{figure9b.eps} & \raisebox{2.75cm}{(b)}

   \end{tabular}
   \end{tabular}

-  \caption{Network Lifetime for (a)~$Lifetime_{95}$ \\
-    and (b)~$Lifetime_{50}$.}
-  \label{figure8}
+  \caption{Network Lifetime for (a)~$Lifetime_{95}$ and (b)~$Lifetime_{50}$.}
+  \label{figure9}

 \end{figure} 
 
 \end{figure} 
 

-
-
-Figure~\ref{figure9}  compares  the  lifetime  coverage of  our  protocols  for
-different coverage  ratios. 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, where the term Protocol refers to  DiLCO  or PeCO.  Indeed there  are applications
-that do not require a 100\% coverage of  the area to be monitored. PeCO might be
-an interesting  method since  it achieves  a good balance  between a  high level
-coverage ratio and network lifetime. PeCO always outperforms DiLCO for the three
-lower  coverage  ratios,  moreover  the   improvements  grow  with  the  network
-size. DiLCO is better  for coverage ratios near 100\%, but in  that case PeCO is
-not ineffective for the smallest network sizes.
+Figure~\ref{figure10}  compares the  lifetime  coverage of  the  DiLCO and  PeCO
+protocols for different coverage ratios.  Protocol/70, Protocol/80, Protocol/85,
+Protocol/90, and Protocol/95  correspond to the amount of time  during which the
+network  can satisfy  an  area  coverage greater  than  $70\%$, $80\%$,  $85\%$,
+$90\%$, and  $95\%$ respectively,  where the  term Protocol  refers to  DiLCO or
+PeCO. Indeed there are applications that do  not require a 100\% coverage of the
+area  to  be monitored.  For  example,  forest  fire application  might  require
+complete coverage in summer seasons while only  require 80$\%$ of the area to be
+covered in  rainy seasons~\citep{li2011transforming}. As another  example, birds
+habit  study requires  only  70$\%$-coverage  at nighttime  when  the birds  are
+sleeping  while  requires  100$\%$-coverage  at   daytime  when  the  birds  are
+active~\citep{1279193}.   PeCO  always outperforms  DiLCO  for  the three  lower
+coverage ratios,  moreover the  improvements grow with  the network  size. DiLCO
+outperforms PeCO  when the coverage  ratio is required  to be $>90\%$,  but PeCO
+extends the network lifetime significantly when coverage ratio can be relaxed.

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

-\centering \includegraphics[scale=0.5]{figure9.eps}
+\centering \includegraphics[scale=0.55]{figure10.eps}

 \caption{Network lifetime for different coverage ratios.}
 \caption{Network lifetime for different coverage ratios.}

-\label{figure9}
+\label{figure10}

 \end{figure} 
 
 \end{figure} 
 

-
-\subsubsection{\bf Impact of $\alpha$ and $\beta$ on PeCO's performance}
-Table~\ref{my-labelx} shows network lifetime results for the different values of $\alpha$ and $\beta$, and for a network size equal to 200 sensor nodes. The choice of $\beta \gg \alpha$  prevents the overcoverage, and so limit the activation of a large number of sensors, but as $\alpha$ is  low, some areas may be poorly covered. This explains the results obtained for {\it Lifetime50} with $\beta \gg \alpha$: a large number of periods with low coverage ratio. With $\alpha \gg \beta$, we priviligie the coverage even if some areas may be overcovered, so high coverage ratio is reached, but a large number of sensors are activated to achieve this goal. Therefore network lifetime is reduced. The choice $\alpha=0.6$ and $\beta=0.4$ seems to achieve the best compromise between lifetime and coverage ratio.     
-%As can be seen in Table~\ref{my-labelx},  it is obvious and clear that when $\alpha$ decreased and $\beta$ increased by any step, the network lifetime for $Lifetime_{50}$ increased and the $Lifetime_{95}$ decreased. Therefore, selecting the values of $\alpha$ and $\beta$ depend on the application type used in the sensor nework. In PeCO protocol, $\alpha$ and $\beta$ are chosen based on the largest value of network lifetime for $Lifetime_{95}$.
+\subsubsection{Impact of $\alpha$ and $\beta$ on PeCO's performance}
+\label{sec:Impact}
+
+Table~\ref{my-labelx}  shows network  lifetime results  for different  values of
+$\alpha$ and $\beta$, and  a network size equal to 200 sensor  nodes. On the one
+hand,  the choice  of $\beta  \gg \alpha$  prevents the  overcoverage, and  also
+limits the activation of a large number of sensors, but as $\alpha$ is low, some
+areas  may  be   poorly  covered.   This  explains  the   results  obtained  for
+$Lifetime_{50}$ with  $\beta \gg  \alpha$: a  large number  of periods  with low
+coverage  ratio.  On  the other  hand, when  $\alpha \gg  \beta$ is  chosen, the
+coverage is favored  even if some areas  may be overcovered, so  a high coverage
+ratio is reached,  but a large number  of sensors are activated  to achieve this
+goal.  Therefore the  network lifetime is reduced.  The  choice $\alpha=0.6$ and
+$\beta=0.4$ seems to  achieve the best compromise between  lifetime and coverage
+ratio.   That explains  why this  setting has  been chosen  for the  experiments
+presented in the previous subsections.

 
 \begin{table}[h]
 \centering
 
 \begin{table}[h]
 \centering


@@ -905,16 +929,34 @@ $\alpha$ & $\beta$ & $Lifetime_{50}$ & $Lifetime_{95}$ \\ \hline
 \section{Conclusion and Future Works}
 \label{sec:Conclusion and Future Works}
 
 \section{Conclusion and Future Works}
 \label{sec:Conclusion and Future Works}
 

-In this paper  we have studied the problem of  Perimeter-based Coverage Optimization in WSNs. We have designed  a new protocol, called Perimeter-based  Coverage Optimization, which schedules nodes'  activities (wake up  and sleep  stages) with the  objective of maintaining a  good coverage ratio  while maximizing the network  lifetime. This protocol is  applied in a distributed  way in regular subregions  obtained after partitioning the area of interest in a preliminary step. It works in periods and
-is based on the resolution of an integer program to select the subset of sensors operating in active status 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.   
-
-
-We have carried out  several simulations  to  evaluate the  proposed protocol. The simulation  results  show   that  PeCO  is  more   energy-efficient  than  other approaches, with respect to lifetime,  coverage ratio, active sensors ratio, and energy consumption. 
-
-We plan to extend our framework so that the schedules are planned for multiple sensing periods. We also want to improve our integer program to  take into account heterogeneous sensors  from both  energy and node characteristics point of views. Finally,  it would  be interesting to implement our protocol using  a sensor-testbed to evaluate it in real world applications.
-
+In this  paper the problem of  perimeter coverage optimization in  WSNs has been
+studied.   A  new  protocol  called  Perimeter-based  Coverage  Optimization  is
+designed. This protocol  schedules nodes' activities (wake up  and sleep stages)
+with the  objective of maintaining  a good  coverage ratio while  maximizing the
+network lifetime.   This protocol  is applied  in a  distributed way  in regular
+subregions obtained  after partitioning  the area of  interest in  a preliminary
+step. It works in  periods and is based on the resolution  of an integer program
+to select  the subset  of sensors  operating in active  status for  each period.
+This 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.  Several
+simulations  have  been carried  out  to  evaluate  the proposed  protocol.  The
+simulation  results  show   that  PeCO  is  more   energy-efficient  than  other
+approaches, with respect to lifetime,  coverage ratio, active sensors ratio, and
+energy consumption.
+
+This framework will  be extented so that the schedules  are planned for multiple
+sensing periods.  The integer  program would  be improved  to take  into account
+heterogeneous sensors from both energy  and node characteristics point of views.
+Finally,  it  would be  interesting  to  implement  the  PeCO protocol  using  a
+sensor-testbed to evaluate it in real world applications.
+
+\subsection*{Acknowledgments}
+Ali  Kadhum Idrees' PhD thesis is financially supported in part by University of Babylon (Iraq). 
+This work is also partially funded by the Labex ACTION program
+(contract ANR-11-LABX-01-01).  
+ 

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