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-% gENOguide.tex
-% v4.0 released April 2013
-
-\documentclass{gENO2e}
-%\usepackage[linesnumbered,ruled,vlined,commentsnumbered]{algorithm2e}
-%\renewcommand{\algorithmcfname}{ALGORITHM}
-\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}};}
-
-
-\maketitle
-
-\begin{abstract}
-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
-scheduling which ensures sensing coverage while minimizing the energy cost. In
-this paper,  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 our protocol is then
-distributed among sensor nodes in each  subregion.
-The novelty of our 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 have been performed using
-OMNeT++, the  discrete event simulator, to  demonstrate that PeCO  can
-offer longer lifetime coverage for WSNs in comparison with some other protocols.
-
-\begin{keywords}Wireless Sensor Networks, Area Coverage, Network Lifetime, Optimization, Scheduling.
-\end{keywords}
-
-\end{abstract}
-
-
-\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 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,
-military, and so on~\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  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
-energy-efficient mechanisms have been suggested  to retain energy and extend the
-lifetime of the WSNs~\cite{rault2014energy}.
-
-This paper makes the following contributions.
-\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 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.  Hence, an
-  optimal scheduling  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
-  our   PeCO   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}
-
-
-
-
-
-
-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 PeCO Protocol Description}
-is devoted to the PeCO 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 are  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 PeCO 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} refers
-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 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
-\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
-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  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)\cite{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
-\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},  \cite{yan2008design}, 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,  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 \cite{zhou2009variable}.   In distributed algorithms~\cite{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~\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  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.}
-
-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
-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 the PeCO
-  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.
-
-%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.
-
-%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.
-
-%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 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.
-
-%Several algorithms to retain the coverage and maximize the network lifetime were proposed in~\cite{cardei2006energy,wang2011coverage}. 
-
-%\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.
-
-%\uppercase{\textbf{Our Protocol}}. In this paper, a Lifetime Coverage Optimization Protocol, called (PeCO) in WSNs is suggested. The sensing field is divided into smaller subregions by means of divide-and-conquer method, and a PeCO 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. PeCO 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.
-
-\section{ The P{\scshape e}CO Protocol Description}
-\label{sec:The PeCO Protocol Description}
-
-\noindent  In  this  section,  we  describe in  details  our Perimeter-based  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.
-
-% 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}
-\label{CI}
-
-\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
-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  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,  the
-complete coverage of a convex area implies connectivity among active nodes.
-
-The PeCO protocol  uses the  same perimeter-coverage  model as  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 (perimeter covered by at least $k$ sensors).
-%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 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 neighboing  point of  view.  The  resulting couples  of
-intersection points subdivide  the perimeter of sensor~$0$  into portions called
-arcs.
-
-\begin{figure}[ht!]
-  \centering
-  \begin{tabular}{@{}cr@{}}
-    \includegraphics[width=75mm]{figure1a.tiff} & \raisebox{3.25cm}{(a)} \\
-    \includegraphics[width=75mm]{figure1b.tiff} & \raisebox{2.75cm}{(b)}
-  \end{tabular}
-  \caption{(a) Perimeter  coverage of sensor node  0 and (b) finding  the arc of
-    $u$'s perimeter covered by $v$.}
-  \label{pcm2sensors}
-\end{figure} 
-
-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~$v$ 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:
- \[
-\alpha =  \arccos \left(\frac{Dist(u,v)}{2R_s}
-\right).
-\] 
-The arc on the perimeter of~$u$ defined by the angular interval $[\pi
-  - \alpha,\pi + \alpha]$ is said to be perimeter-covered by sensor~$v$.
-
-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 the 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*}[t!]
-\centering
-\includegraphics[width=127.5mm]{figure2.tiff}  
-\caption{Maximum coverage levels for perimeter of sensor node $0$.}
-\label{expcm}
-\end{figure*} 
-
-%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.
-
-
-
- \begin{table}
- \tbl{Coverage intervals and contributing sensors for sensor node 0 \label{my-label}}
-{\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
-\hline
-\begin{tabular}[c]{@{}c@{}}Left \\ point \\ angle~$\alpha$ \end{tabular} & \begin{tabular}[c]{@{}c@{}}Interval \\ left \\ point\end{tabular} & \begin{tabular}[c]{@{}c@{}}Interval \\ right \\ point\end{tabular} & \begin{tabular}[c]{@{}c@{}}Maximum \\ coverage\\  level\end{tabular} & \multicolumn{5}{c|}{\begin{tabular}[c]{@{}c@{}}Set of sensors\\ involved \\ in coverage interval\end{tabular}} \\ \hline
-0.0291    & 1L                                                                        & 2L                                                        & 4                                                                     & 0                     & 1                     & 3                    & 4                    &                      \\ \hline
-0.104     & 2L                                                                        & 3R                                                        & 5                                                                     & 0                     & 1                     & 3                    & 4                    & 2                    \\ \hline
-0.3168    & 3R                                                                        & 4R                                                        & 4                                                                     & 0                     & 1                     & 4                    & 2                    &                      \\ \hline
-0.6752    & 4R                                                                        & 1R                                                        & 3                                                                     & 0                     & 1                     & 2                    &                      &                      \\ \hline
-1.8127    & 1R                                                                        & 5L                                                        & 2                                                                     & 0                     & 2                     &                      &                      &                      \\ \hline
-1.9228    & 5L                                                                        & 6L                                                        & 3                                                                     & 0                     & 2                     & 5                    &                      &                      \\ \hline
-2.3959    & 6L                                                                        & 2R                                                        & 4                                                                     & 0                     & 2                     & 5                    & 6                    &                      \\ \hline
-2.4258    & 2R                                                                        & 7L                                                        & 3                                                                     & 0                     & 5                     & 6                    &                      &                      \\ \hline
-2.7868    & 7L                                                                        & 8L                                                        & 4                                                                     & 0                     & 5                     & 6                    & 7                    &                      \\ \hline
-2.8358    & 8L                                                                        & 5R                                                        & 5                                                                     & 0                     & 5                     & 6                    & 7                    & 8                    \\ \hline
-2.9184    & 5R                                                                        & 7R                                                        & 4                                                                     & 0                     & 6                     & 7                    & 8                    &                      \\ \hline
-3.3301    & 7R                                                                        & 9R                                                        & 3                                                                     & 0                     & 6                     & 8                    &                      &                      \\ \hline
-3.9464    & 9R                                                                        & 6R                                                        & 4                                                                     & 0                     & 6                     & 8                    & 9                    &                      \\ \hline
-4.767     & 6R                                                                        & 3L                                                        & 3                                                                     & 0                     & 8                     & 9                    &                      &                      \\ \hline
-4.8425    & 3L                                                                        & 8R                                                        & 4                                                                     & 0                     & 3                     & 8                    & 9                    &                      \\ \hline
-4.9072    & 8R                                                                        & 4L                                                        & 3                                                                     & 0                     & 3                     & 9                    &                      &                      \\ \hline
-5.3804    & 4L                                                                        & 9R                                                        & 4                                                                     & 0                     & 3                     & 4                    & 9                    &                      \\ \hline
-5.9157    & 9R                                                                        & 1L                                                        & 3                                                                     & 0                     & 3                     & 4                    &                      &                      \\ \hline
-\end{tabular}}
-
-
-\end{table}
-
-
-%The optimization algorithm that used by PeCO 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.
-
-In the PeCO  protocol, the scheduling of the 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}[h!]
-\centering
-\includegraphics[width=62.5mm]{figure3.tiff}  
-\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} 
-
-\subsection{The Main Idea}
-
-\noindent 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  will  be  executed  in   a  distributed  way  in  each  subregion
-simultaneously to schedule nodes' activities for one sensing period.
-
-As  shown in  Figure~\ref{fig2}, node  activity  scheduling is  produced by  our
-protocol in a periodic manner. Each period is divided into 4 stages: Information
-(INFO)  Exchange,  Leader Election,  Decision  (the  result of  an  optimization
-problem),  and  Sensing.   For  each  period there  is  exactly  one  set  cover
-responsible for  the sensing task.  Protocols  based on a periodic  scheme, like
-PeCO, are more  robust against an unexpected  node failure. On the  one hand, if
-a node failure is discovered before  taking the decision, the corresponding sensor
-node will  not be considered  by the optimization  algorithm. On  the other
-hand, if the sensor failure happens after  the decision, the sensing task of the
-network will be temporarily affected: only  during the period of sensing until a
-new period starts, since a new set cover will take charge of the sensing task in
-the next period. The energy consumption and some other constraints can easily be
-taken  into  account since  the  sensors  can  update  and then  exchange  their
-information (including their  residual energy) at the beginning  of each period.
-However, the pre-sensing  phases (INFO Exchange, Leader  Election, and Decision)
-are energy consuming, even for nodes that will not join the set cover to monitor
-the area.
-
-\begin{figure}[t!]
-\centering
-\includegraphics[width=80mm]{figure4.tiff}  
-\caption{PeCO protocol.}
-\label{fig2}
-\end{figure} 
-
-We define two types of packets to be used by PeCO protocol:
-%\begin{enumerate}[(a)]
-\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.
-\end{itemize}
-%\end{enumerate}
-
-Five status are possible for a sensor node in the network:
-%\begin{enumerate}[(a)] 
-\begin{itemize} 
-\item LISTENING: waits for a decision (to be active or not);
-\item COMPUTATION: executes the optimization algorithm as leader to
-  determine the activities scheduling;
-\item ACTIVE: node is sensing;
-\item SLEEP: node is turned off;
-\item COMMUNICATION: transmits or receives packets.
-\end{itemize}
-%\end{enumerate}
-%Below, we describe each phase in more details.
-
-\subsection{PeCO Protocol Algorithm}
-
-\noindent 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.
-
-
-\iffalse
-\begin{algorithm}      
- % \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} \; 
-  
-  \If{ $RE_k \geq E_{th}$ }{
-      \emph{$s_k.status$ = COMMUNICATION}\;
-      \emph{Send $INFO()$ packet to other nodes in subregion}\;
-      \emph{Wait $INFO()$ packet from other nodes in subregion}\; 
-      \emph{Update K.CurrentSize}\;
-      \emph{LeaderID = Leader election}\;
-      \If{$ s_k.ID = LeaderID $}{
-         \emph{$s_k.status$ = COMPUTATION}\;
-         
-      \If{$ s_k.ID $ is Not previously selected as a Leader }{
-          \emph{ Execute the 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)}{
-      
-      	 \emph{ Use the same previous cover set for current sensing stage}\;
-      }
-      \Else{
-            \emph{Update $a^j_{ik}$; prepare data for IP~Algorithm}\;
-            \emph{$\left\{\left(X_{1},\dots,X_{l},\dots,X_{K}\right)\right\}$ = Execute Integer Program Algorithm($K$)}\;
-            \emph{K.PreviousSize = K.CurrentSize}\;
-           }
-      
-        \emph{$s_k.status$ = COMMUNICATION}\;
-        \emph{Send $ActiveSleep()$ to each node $l$ in subregion}\;
-        \emph{Update $RE_k $}\;
-      }	  
-      \Else{
-        \emph{$s_k.status$ = LISTENING}\;
-        \emph{Wait $ActiveSleep()$ packet from the Leader}\;
-        \emph{Update $RE_k $}\;
-      }  
-  }
-  \Else { Exclude $s_k$ from entering in the current sensing stage}
-  }
-%\caption{PeCO($s_k$)}
-\label{alg:PeCO}
-\end{algorithm}
-\fi
-
-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.
-
-%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 PeCO 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.
-
-\section{Perimeter-based Coverage Problem Formulation}
-\label{cp}
-
-\noindent In this  section, the coverage model is  mathematically formulated. We
-start  with a  description of the notations that will  be used  throughout the
-section.\\
-First, we have the following sets:
-\begin{itemize}
-\item $S$ represents the set of WSN 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}
-$I_j$ 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
-in coverage interval~$i$ of sensor~$j$, that is:
-\begin{equation}
-a^j_{ik} = \left \{ 
-\begin{array}{lll}
-  1 & \mbox{if sensor $k$ is involved in the } \\
-  	&	\mbox{coverage interval $i$ of sensor $j$}, \\
-  0 & \mbox{otherwise.}\\
-\end{array} \right.
-%\label{eq12} 
-%\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 $k$. .
-Second,  we define  several binary  and integer  variables.  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  an integer
-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 $V^j_i$.
-
-If we decide to sustain a level of coverage equal to $l$ all along the perimeter
-of sensor  $j$, we have  to ensure  that at least  $l$ sensors involved  in each
-coverage  interval $i  \in I_j$  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  $\sum_{k \in A} a^j_{ik}  X_k$.  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 we use 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. 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.
-
-%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 $j$.
-
-Our coverage optimization problem can then be mathematically expressed as follows: 
-%Objective:
-\begin{equation} %\label{eq:ip2r}
-\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\\
-%\label{c1} 
-\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\\
-X_{k} \in \{0,1\}, \forall k \in A
-\end{array}
-\right.
-%\notag
-\end{equation}
-$\alpha^j_i$ 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 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{Performance Evaluation and Analysis}  
-\label{sec:Simulation Results and Analysis}
-%\noindent \subsection{Simulation Framework}
-
-\subsection{Simulation Settings}
-%\label{sub1}
-
-The WSN  area of interest is  supposed to be divided  into 16~regular subregions
-and we use the same energy consumption than in our previous work~\cite{Idrees2}.
-Table~\ref{table3} gives the chosen parameters settings.
-
-\begin{table}[ht]
-\tbl{Relevant parameters for network initialization \label{table3}}{
-% title of Table
-\centering
-% used for centering table
-\begin{tabular}{c|c}
-% centered columns (4 columns)
-\hline
-Parameter & Value  \\ [0.5ex]
-   
-\hline
-% inserts single horizontal line
-Sensing field & $(50 \times 25)~m^2 $   \\
-
-WSN size &  100, 150, 200, 250, and 300~nodes   \\
-%\hline
-Initial energy  & in range 500-700~Joules  \\  
-%\hline
-Sensing period & duration of 60 minutes \\
-$E_{th}$ & 36~Joules\\
-$R_s$ & 5~m   \\     
-%\hline
-$\alpha^j_i$ & 0.6   \\
-% [1ex] adds vertical space
-%\hline
-$\beta^j_i$ & 0.4
-%inserts single line
-\end{tabular}}
-
-% is used to refer this table in the text
-\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
-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
-node  to stay  active during  one period,  it will  no more  participate in  the
-coverage task. This value corresponds to the energy needed by the sensing phase,
-obtained by multiplying  the energy consumed in 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
-be active during at most 20 periods.
-
-The values  of $\alpha^j_i$ and  $\beta^j_i$ have been  chosen to ensure  a good
-network coverage and a longer WSN lifetime.  We have given a higher priority 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,  we have assigned to
-$\beta^j_i$ a value which is slightly lower so as to minimize the number of active sensor nodes which contribute
-in covering the interval.
-
-We introduce the following performance metrics to evaluate the efficiency of our
-approach.
-
-%\begin{enumerate}[i)]
-\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
-  $Lifetime_{50}$  denote, respectively,  the  amount of  time  during which  is
-  guaranteed a  level of coverage  greater than $95\%$  and $50\%$. The  WSN can
-  fulfill the expected  monitoring task until all its nodes  have depleted their
-  energy or if the network is no  more connected. This last condition is crucial
-  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, we discretized the sensor field as
-  a regular grid, which yields the following equation:
-  
-%\begin{equation*}
-\[
-    \scriptsize
-    \mbox{CR}(\%) = \frac{\mbox{$n$}}{\mbox{$N$}} \times 100
-\]
-%  \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
-  points in  the sensing  field.  In  our simulations  we have  set a  layout of
-  $N~=~51~\times~26~=~1326$~grid points.
-\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:
-  %\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
-  current sensing period~$p$, $|S|$ is the number of sensors in the network, and
-  $R$ is the number of subregions.
-\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:
-  %\begin{equation*}
-\[  
-  \scriptsize
-    \mbox{EC} = \frac{\sum\limits_{p=1}^{P} \left( E^{\mbox{com}}_p+E^{\mbox{list}}_p+E^{\mbox{comp}}_p  
-      + E^{a}_p+E^{s}_p \right)}{P},
-\]
- % \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
-  energy consumption spent  by all the nodes for  wireless communications during
-  period $p$.  $E^{\scriptsize \mbox{list}}_p$,  the next factor, corresponds to
-  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).
-\end{itemize}
-%\end{enumerate}
-
-\subsection{Simulation Results}
-
-In  order  to  assess and  analyze  the  performance  of  our protocol  we  have
-implemented PeCO protocol in  OMNeT++~\cite{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 (2.4~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)~\cite{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) \cite{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
-\cite{ChinhVu}. The second one,  called GAF~\cite{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~\cite{Idrees2}, is  an improved version
-of a research work we presented in~\cite{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{fig333}  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.
-
-\parskip 0pt    
-\begin{figure}[h!]
-\centering
- \includegraphics[scale=0.5] {figure5.eps} 
-\caption{Coverage ratio for 200 deployed nodes.}
-\label{fig333}
-\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 PeCO protocol. The coverage ratio of PeCO protocol is better than other approaches from the period 64.
-
-%because the optimization algorithm used by PeCO 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 PeCO selected another nodes to ensure the coverage of the area of interest. i.e. DiLCO-16 showed a good coverage in the beginning then PeCO, 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 PeCO protocol to ensure a longer lifetime coverage in comparison with other approaches. 
-
-
-\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{fig444}
-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{fig333}.
-
-\begin{figure}[h!]
-\centering
-\includegraphics[scale=0.5]{figure6.eps}  
-\caption{Active sensors ratio for 200 deployed nodes.}
-\label{fig444}
-\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{fig3EC}(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.
-
-\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)}
-  \end{tabular}
-  \caption{Energy consumption per period for (a)~$Lifetime_{95}$ and (b)~$Lifetime_{50}$.}
-  \label{fig3EC}
-\end{figure} 
-
-%The optimization algorithm, which used by PeCO 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 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{\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{fig3LT}(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{fig3LT}(b) that the lifetime
-is about twice longer with  PeCO compared to DESK protocol.  The performance
-difference    is    more    obvious   in    Figure~\ref{fig3LT}(b)    than    in
-Figure~\ref{fig3LT}(a) because the gain induced  by our protocols increases with
- time, and the lifetime with a coverage  of 50\% is far  longer than with
-95\%.
-
-\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)}
-  \end{tabular}
-  \caption{Network Lifetime for (a)~$Lifetime_{95}$ \\
-    and (b)~$Lifetime_{50}$.}
-  \label{fig3LT}
-\end{figure} 
-
-%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, PeCO 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}  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.
-
-\begin{figure}[h!]
-\centering \includegraphics[scale=0.5]{figure9.eps}
-\caption{Network lifetime for different coverage ratios.}
-\label{figLTALL}
-\end{figure} 
-
-%Comparison shows that PeCO 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. PeCO 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{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.
-
-%To cope with this problem, the area of interest is divided into a smaller subregions using  divide-and-conquer method, and then a PeCO protocol for optimizing the lifetime coverage in each subregion. PeCO 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  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.
-%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 so that the schedules are planned for multiple
-sensing periods.
-%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 heterogeneous
-sensors  from both  energy  and node  characteristics point of views.
-%the third, we are investigating new optimization model based on the sensing range so as to maximize the lifetime coverage in WSN;
-Finally,  it   would  be   interesting  to  implement   our  protocol   using  a
-sensor-testbed to evaluate it in real world applications.
-
-\bibliographystyle{gENO}
-\bibliography{biblio}
-
-
-\end{document}
+% gENOguide.tex
+% v4.0 released April 2013
+
+\documentclass{gENO2e}
+
+\usepackage{indentfirst}
+\usepackage{color}
+\usepackage[algo2e,ruled,vlined]{algorithm2e}
+\begin{document}
+
+\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}
+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
+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
+coverage for WSNs compared to other protocols.
+
+\begin{keywords}
+  Wireless Sensor Networks, Area Coverage, Energy efficiency, Optimization, Scheduling.
+\end{keywords}
+
+\end{abstract}
+
+\section{Introduction}
+\label{sec:introduction}
+
+The continuous progress in Micro  Electro-Mechanical Systems (MEMS) and wireless
+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
+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
+communication unit for data transmission and reception.
+
+The energy needed  by an active sensor node to  perform sensing, processing, and
+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
+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}.
+
+This paper makes the following contributions :
+\begin{enumerate}
+\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.
+\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.
+\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
+work in the  field is reviewed. Section~\ref{sec:The  PeCO Protocol Description}
+is devoted to the PeCO 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   are  given  for  future  works  in
+Section~\ref{sec:Conclusion and Future Works}.
+
+\section{Related Literature}
+\label{sec:Literature Review}
+
+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
+objective.  Hence, area  coverage \citep{Misra} means that every  point inside a
+fixed area must be monitored, while target coverage~\citep{yang2014novel} refers
+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
+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
+  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
+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
+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
+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
+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
+\citep{berman04,zorbas2010solving}.  Other approaches  are based on mathematical
+programming
+formulations~\citep{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~\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: 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
+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
+  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}
+
+\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.  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
+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
+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.
+
+The    PeCO   protocol    uses    the   same    perimeter-coverage   model    as
+\citet{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.  Authors \citet{huang2005coverage}  proved that a
+network area  is $k$-covered  (every point in  the area is  covered by  at least
+$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,
+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!]
+  \centering
+  \begin{tabular}{@{}cr@{}}
+    \includegraphics[width=75mm]{figure1a.eps} & \raisebox{3.25cm}{(a)} \\
+    \includegraphics[width=75mm]{figure1b.eps} & \raisebox{2.75cm}{(b)}
+  \end{tabular}
+  \caption{(a) Perimeter  coverage of sensor node  0 and (b) finding  the arc of
+    $u$'s perimeter covered by $v$.}
+  \label{figure1}
+\end{figure} 
+
+Figure~\ref{figure1}(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~$v$ 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 the
+euclidean distance between nodes~$u$ and $v$ is computed as follows:
+$$
+  Dist(u,v)=\sqrt{(u_x - v_x)^2 + (u_y-v_y)^2},
+$$
+while the angle~$\alpha$ is obtained through the formula:
+ \[
+\alpha = \arccos \left(\frac{Dist(u,v)}{2R_s} \right).
+\] 
+The  arc  on the  perimeter  of~$u$  defined by  the  angular  interval $[\pi  -
+  \alpha,\pi + \alpha]$ is then said to be perimeter-covered by sensor~$v$.
+
+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{figure1}(a)  illustrates  the arcs  for  the  nine neighbors  of
+sensor $0$ and  Table~\ref{my-label} gives the position of  the corresponding arcs
+in  the interval  $[0,2\pi)$. More  precisely, 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 the 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 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
+above is thus given by the sixth line of the table.
+
+\begin{figure*}[t!]
+\centering
+\includegraphics[width=0.95\linewidth]{figure2.eps}  
+\caption{Maximum coverage levels for perimeter of sensor node $0$.}
+\label{figure2}
+\end{figure*} 
+
+\begin{table}
+\tbl{Coverage intervals and contributing sensors for node 0 \label{my-label}}
+{\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
+\hline
+\begin{tabular}[c]{@{}c@{}}Left \\ point \\ angle~$\alpha$ \end{tabular} & \begin{tabular}[c]{@{}c@{}}Interval \\ left \\ point\end{tabular} & \begin{tabular}[c]{@{}c@{}}Interval \\ right \\ point\end{tabular} & \begin{tabular}[c]{@{}c@{}}Maximum \\ coverage\\  level\end{tabular} & \multicolumn{5}{c|}{\begin{tabular}[c]{@{}c@{}}Set of sensors\\ involved \\ in coverage interval\end{tabular}} \\ \hline
+0.0291    & 1L                                                                        & 2L                                                        & 4                                                                     & 0                     & 1                     & 3                    & 4                    &                      \\ \hline
+0.104     & 2L                                                                        & 3R                                                        & 5                                                                     & 0                     & 1                     & 3                    & 4                    & 2                    \\ \hline
+0.3168    & 3R                                                                        & 4R                                                        & 4                                                                     & 0                     & 1                     & 4                    & 2                    &                      \\ \hline
+0.6752    & 4R                                                                        & 1R                                                        & 3                                                                     & 0                     & 1                     & 2                    &                      &                      \\ \hline
+1.8127    & 1R                                                                        & 5L                                                        & 2                                                                     & 0                     & 2                     &                      &                      &                      \\ \hline
+1.9228    & 5L                                                                        & 6L                                                        & 3                                                                     & 0                     & 2                     & 5                    &                      &                      \\ \hline
+2.3959    & 6L                                                                        & 2R                                                        & 4                                                                     & 0                     & 2                     & 5                    & 6                    &                      \\ \hline
+2.4258    & 2R                                                                        & 7L                                                        & 3                                                                     & 0                     & 5                     & 6                    &                      &                      \\ \hline
+2.7868    & 7L                                                                        & 8L                                                        & 4                                                                     & 0                     & 5                     & 6                    & 7                    &                      \\ \hline
+2.8358    & 8L                                                                        & 5R                                                        & 5                                                                     & 0                     & 5                     & 6                    & 7                    & 8                    \\ \hline
+2.9184    & 5R                                                                        & 7R                                                        & 4                                                                     & 0                     & 6                     & 7                    & 8                    &                      \\ \hline
+3.3301    & 7R                                                                        & 9R                                                        & 3                                                                     & 0                     & 6                     & 8                    &                      &                      \\ \hline
+3.9464    & 9R                                                                        & 6R                                                        & 4                                                                     & 0                     & 6                     & 8                    & 9                    &                      \\ \hline
+4.767     & 6R                                                                        & 3L                                                        & 3                                                                     & 0                     & 8                     & 9                    &                      &                      \\ \hline
+4.8425    & 3L                                                                        & 8R                                                        & 4                                                                     & 0                     & 3                     & 8                    & 9                    &                      \\ \hline
+4.9072    & 8R                                                                        & 4L                                                        & 3                                                                     & 0                     & 3                     & 9                    &                      &                      \\ \hline
+5.3804    & 4L                                                                        & 9R                                                        & 4                                                                     & 0                     & 3                     & 4                    & 9                    &                      \\ \hline
+5.9157    & 9R                                                                        & 1L                                                        & 3                                                                     & 0                     & 3                     & 4                    &                      &                      \\ \hline
+\end{tabular}}
+
+
+\end{table}
+
+In  the  PeCO protocol,  the  scheduling  of  the  sensor nodes'  activities  is
+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
+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.
+
+%\newpage
+\begin{figure}[h!]
+\centering
+\includegraphics[width=57.5mm]{figure3.eps}  
+\caption{Sensing range outside the WSN's area of interest.}
+\label{figure3}
+\end{figure}
+
+\vspace{-0.25cm}
+
+\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 the protocol
+will  be executed  in  a distributed  way in  each  subregion simultaneously  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.
+
+As shown  in Figure~\ref{figure4}, node  activity scheduling is produced  by the
+proposed protocol  in a periodic manner.  Each period is divided  into 4 stages:
+Information  (INFO)  Exchange,  Leader  Election, Decision  (the  result  of  an
+optimization problem),  and Sensing.  For each  period there is exactly  one set
+cover responsible for  the sensing task.  Protocols based on  a periodic scheme,
+like PeCO, are more robust against an  unexpected node failure. On the one hand,
+if a  node failure is discovered  before taking the decision,  the corresponding
+sensor node will  not be considered by the optimization  algorithm. On the other
+hand, if the sensor failure happens after  the decision, the sensing task of the
+network will be temporarily affected: only  during the period of sensing until a
+new period starts, since a new set cover will take charge of the sensing task in
+the next period. The energy consumption and some other constraints can easily be
+taken  into  account since  the  sensors  can  update  and then  exchange  their
+information (including their  residual energy) at the beginning  of each period.
+However, the pre-sensing  phases (INFO Exchange, Leader  Election, and Decision)
+are energy consuming, even for nodes that will not join the set cover to monitor
+the area. Sensing  period duration is adapted according to  the QoS requirements
+of the application.
+
+\begin{figure}[t!]
+\centering
+\includegraphics[width=80mm]{figure4.eps}  
+\caption{PeCO protocol.}
+\label{figure4}
+\end{figure} 
+
+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
+  the sensing phase.
+\end{itemize}
+
+Five statuses are possible for a sensor node in the network:
+\begin{itemize} 
+\item LISTENING: waits for a decision (to be active or not);
+\item COMPUTATION: executes the optimization algorithm as leader to
+  determine the activities scheduling;
+\item ACTIVE: node is sensing;
+\item SLEEP: node is turned off;
+\item COMMUNICATION: transmits or receives packets.
+\end{itemize}
+
+\subsection{PeCO Protocol Algorithm}
+
+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.
+
+\begin{algorithm2e}      
+  \label{alg:PeCO}
+  \caption{PeCO pseudocode}
+  \eIf{$RE_k \geq E_{th}$}{
+    $s_k.status$ = COMMUNICATION\;
+    Send $INFO()$ packet to other nodes in subregion\;
+    Wait $INFO()$ packet from other nodes in subregion\;
+    Update K.CurrentSize\;
+    LeaderID = Leader election\;
+    \eIf{$s_k.ID = LeaderID$}{
+      $s_k.status$ = COMPUTATION\;
+      \If{$ s_k.ID $ is Not previously selected as a Leader}{
+        Execute the perimeter coverage model\;
+      }
+      \eIf{($s_k.ID $ is the same Previous Leader) {\bf and} \\
+        \indent (K.CurrentSize = K.PreviousSize)}{
+        Use the same previous cover set for current sensing stage\;
+      }{
+        Update $a^j_{ik}$; prepare data for IP~Algorithm\;
+        $\left\{\left(X_{1},\dots,X_{l},\dots,X_{K}\right)\right\}$ = Execute Integer Program Algorithm($K$)\;
+        K.PreviousSize = K.CurrentSize\;
+      }
+      $s_k.status$ = COMMUNICATION\;
+      Send $ActiveSleep()$ to each node $l$ in subregion\;
+      Update $RE_k $\;
+    }{
+      $s_k.status$ = LISTENING\;
+      Wait $ActiveSleep()$ packet from the Leader\;
+      Update $RE_k $\;
+    }
+  }{
+    Exclude $s_k$ from entering in the current sensing stage\;
+  }
+\end{algorithm2e}
+
+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}
+
+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.
+
+First, the following sets:
+\begin{itemize}
+\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}
+$I_j$ 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 \{ 
+\begin{array}{lll}
+  1 & \mbox{if sensor $k$ is involved in the } \\
+  	&	\mbox{coverage interval $i$ of sensor $j$}, \\
+  0 & \mbox{otherwise.}\\
+\end{array} \right.
+\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 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}
+
+
+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,
+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}
+
+\subsection{Simulation Settings}
+
+The WSN  area of interest is  supposed to be divided  into 16~regular subregions
+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}}{
+\centering
+\begin{tabular}{c|c}
+\hline
+Parameter & Value  \\ [0.5ex]
+\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 period & duration of 60 minutes \\
+$E_{th}$ & 36~Joules \\
+$R_s$ & 5~m \\     
+$R_c$ & 10~m \\   
+$\alpha^j_i$ & 0.6 \\
+$\beta^j_i$ & 0.4
+\end{tabular}}
+\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
+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
+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,
+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
+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
+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.
+\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
+  $Lifetime_{50}$  denote, respectively,  the  amount of  time  during which  is
+  guaranteed a  level of coverage  greater than $95\%$  and $50\%$. The  WSN can
+  fulfill the expected  monitoring task until all its nodes  have depleted their
+  energy or if the network is no  more connected. This last condition is crucial
+  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. Here the sensor field is discretized as
+  a regular grid, which yields the following equation:
+  \begin{equation*}
+    \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
+  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
+  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:
+  \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},
+  \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
+  energy consumption spent  by all the nodes for  wireless communications during
+  period $p$.  $E^{\scriptsize \mbox{list}}_p$,  the next factor, corresponds to
+  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   (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}
+
+\subsection{Simulation Results}
+
+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!]
+\centering
+ \includegraphics[scale=0.5] {figure5.eps} 
+\caption{Coverage ratio for 200 deployed nodes.}
+\label{figure5}
+\end{figure} 
+
+\subsubsection{Active Sensors Ratio}
+
+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
+\includegraphics[scale=0.5]{figure6.eps}  
+\caption{Active sensors ratio for 200 deployed nodes.}
+\label{figure6}
+\end{figure} 
+
+\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@{}}
+    \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)~$Lifetime_{50}$.}
+  \label{figure8}
+\end{figure} 
+
+\subsubsection{Network Lifetime}
+
+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@{}}
+    \includegraphics[scale=0.5]{figure9a.eps} & \raisebox{2.75cm}{(a)} \\  
+    \includegraphics[scale=0.5]{figure9b.eps} & \raisebox{2.75cm}{(b)}
+  \end{tabular}
+  \caption{Network Lifetime for (a)~$Lifetime_{95}$ and (b)~$Lifetime_{50}$.}
+  \label{figure9}
+\end{figure} 
+
+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!]
+\centering \includegraphics[scale=0.55]{figure10.eps}
+\caption{Network lifetime for different coverage ratios.}
+\label{figure10}
+\end{figure} 
+
+\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
+\caption{The impact of $\alpha$ and $\beta$ on PeCO's performance}
+\label{my-labelx}
+\begin{tabular}{|c|c|c|c|}
+\hline
+$\alpha$ & $\beta$ & $Lifetime_{50}$ & $Lifetime_{95}$ \\ \hline
+0.0 & 1.0 & 151 & 0 \\ \hline
+0.1 & 0.9 & 145 & 0 \\ \hline
+0.2 & 0.8 & 140 & 0 \\ \hline
+0.3 & 0.7 & 134 & 0 \\ \hline
+0.4 & 0.6 & 125 & 0 \\ \hline
+0.5 & 0.5 & 118 & 30 \\ \hline
+{\bf 0.6} & {\bf 0.4} & {\bf 94} & {\bf 57} \\ \hline
+0.7 & 0.3 & 97 & 49 \\ \hline
+0.8 & 0.2 & 90 & 52 \\ \hline
+0.9 & 0.1 & 77 & 50 \\ \hline
+1.0 & 0.0 & 60 & 44 \\ \hline
+\end{tabular}
+\end{table}
+
+
+\section{Conclusion and Future Works}
+\label{sec:Conclusion and Future Works}
+
+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).  
+ 
+\bibliographystyle{gENO}
+\bibliography{biblio} 
+
+\end{document}
