Update on fig. 2

[LiCO.git] / LiCO_Journal.tex

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\begin{document}

%\title{Lifetime Coverage Optimization Protocol \\
%  in Wireless Sensor Networks}
\title{Perimeter-based Coverage Optimization Protocol \\
  to Improve Lifetime in Wireless Sensor Networks}

\author{Ali Kadhum Idrees,~\IEEEmembership{}
        Karine Deschinkel,~\IEEEmembership{}
        Michel Salomon,~\IEEEmembership{}
        and~Rapha\"el Couturier ~\IEEEmembership{} 
        \thanks{The authors are with FEMTO-ST Institute, UMR 6174 CNRS, University of Franche-Comt\'e,
          Belfort, France. Email: ali.idness@edu.univ-fcomte.fr, $\lbrace$karine.deschinkel,
          michel.salomon, raphael.couturier$\rbrace$@univ-fcomte.fr}}

\markboth{IEEE Communications Letters,~Vol.~XX, No.~Y, January 2015}%
{Shell \MakeLowercase{\textit{et al.}}: Bare Demo of IEEEtran.cls for Journals}

\maketitle

\begin{abstract}
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 Lifetime  Coverage Optimization
protocol (LiCO).   It is a  hybrid of  centralized and distributed  methods: the
region of interest is first subdivided  into subregions and our protocol is then
distributed among sensor nodes in each  subregion. A sensor node which runs LiCO
protocol  repeats   periodically  four  stages:  information   exchange,  leader
election, optimization decision, and sensing.  More precisely, the scheduling of
nodes' activities (sleep/wake up duty cycles) is achieved in each subregion by a
leader selected after  cooperation between nodes within the  same subregion. The
novelty  of  our  approach  lies  essentially   in  the  formulation  of  a  new
mathematical optimization  model based on  perimeter coverage level  to schedule
sensors' activities.  Extensive simulation experiments have been performed using
OMNeT++, the  discrete event simulator, to  demonstrate that LiCO is  capable to
offer longer lifetime coverage for WSNs in comparison with some other protocols.
\end{abstract} 

% Note that keywords are not normally used for peerreview papers.
\begin{IEEEkeywords}
Wireless Sensor Networks, Area Coverage, Network lifetime, Optimization, Scheduling.
\end{IEEEkeywords}

\IEEEpeerreviewmaketitle

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

\noindent The continuous progress in Micro Electro-Mechanical Systems (MEMS) and
wireless communication hardware  has given rise to the opportunity  to use large
networks    of     tiny    sensors,    called    Wireless     Sensor    Networks
(WSN)~\cite{akyildiz2002wireless,puccinelli2005wireless}, to  fulfill monitoring
tasks.   A  WSN  consists  of  small low-powered  sensors  working  together  by
communicating with one another through 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 the reliability and to exploit node redundancy
thanks to energy-efficient activity  scheduling approaches.  Indeed, the overlap
of sensing  areas can be exploited  to schedule alternatively some  sensors in a
low power sleep mode and thus save  energy. Overall, the main question that must
be answered is: how to extend the lifetime coverage of a WSN as long as possible
while  ensuring   a  high  level  of   coverage?   So,  this  last   years  many
energy-efficient mechanisms have been suggested  to retain energy and extend the
lifetime of the WSNs~\cite{rault2014energy}.

%The sensor system ought to have a lifetime long enough to satisfy the application necessities. The lifetime coverage maximization is one of the fundamental requirements of any area coverage protocol in WSN implementation~\cite{nayak2010wireless}. In order to increase the reliability and prevent the possession of coverage holes (some parts are not covered in the area of interest) in the WSN, it is necessary to deploy the WSN with high density so as to increase the reliability and to exploit redundancy by using energy-efficient activity scheduling approaches.

%From a certain standpoint, the high coverage ratio is required by many applications such as military and health-care. Therefore, a suitable number of sensors are being chosen so as to cover the area of interest, is the first challenge. Meanwhile, the sensor nodes have a limited capabilities in terms of memory, processing, communication, and battery power being the most important and critical one.  So, the main question is: how to extend the lifetime coverage of WSN as long time as possible?. There are many energy-efficient mechanisms have been suggested to retain energy and extend the lifetime of the WSNs~\cite{rault2014energy}.

%\uppercase{\textbf{Our contributions.}}

This paper makes the following contributions.
\begin{enumerate}
\item We devise a framework to schedule nodes to be activated alternatively such
  that the network lifetime is prolonged  while ensuring that a certain level of
  coverage is preserved.  A key idea in  our framework is to exploit spatial and
  temporal subdivision.   On the one hand  the area of interest  if divided into
  several smaller subregions and on the other hand the time line is divided into
  periods of equal length. In each subregion the sensor nodes will cooperatively
  choose a  leader which will schedule  nodes' activities, and this  grouping of
  sensors is similar to typical cluster architecture.
\item We  propose a new mathematical  optimization model.  Instead of  trying to
  cover a set of specified points/targets as  in most of the methods proposed in
  the literature, we formulate an integer program based on perimeter coverage of
  each sensor.  The  model involves integer variables to  capture the deviations
  between  the actual  level of  coverage and  the required  level.  So  that an
  optimal scheduling  will be  obtained by  minimizing a  weighted sum  of these
  deviations.
\item We  conducted extensive simulation  experiments, using the  discrete event
  simulator OMNeT++, to demonstrate the  efficiency of our protocol. We compared
  our   LiCO   protocol   to   two   approaches   found   in   the   literature:
  DESK~\cite{ChinhVu} and  GAF~\cite{xu2001geography}, and also to  our previous
  work published in~\cite{Idrees2} which is  based on another optimization model
  for sensor scheduling.
\end{enumerate}

%Two combined integrated energy-efficient techniques have been used by LiCO protocol in order to maximize the lifetime coverage in WSN: the first, by dividing the area of interest into several smaller subregions based on divide-and-conquer method and then one leader elected for each subregion in an independent, distributed, and simultaneous way by the cooperation among the sensor nodes within each subregion, and this similar to cluster architecture;
% the second, activity scheduling based new optimization model has been used to provide the optimal cover set that will take the mission of sensing during current period. This optimization algorithm is based on a perimeter-coverage model so as to optimize the shared perimeter among the sensors in each subregion, and this represents as a energu-efficient control topology mechanism in WSN.

The rest  of the paper is  organized as follows.  In the next section  we review
some related work in the  field. Section~\ref{sec:The LiCO Protocol Description}
is devoted to the LiCO protocol  description and Section~\ref{cp} focuses on the
coverage model  formulation which is used  to schedule the activation  of sensor
nodes.  Section~\ref{sec:Simulation  Results and Analysis}  presents simulations
results and discusses the comparison  with other approaches. Finally, concluding
remarks   are  drawn   and  some   suggestions   given  for   future  works   in
Section~\ref{sec:Conclusion and Future Works}.

% that show that our protocol outperforms others protocols.
\section{Related Literature}
\label{sec:Literature Review}

\noindent  In  this section,  we  summarize  some  related works  regarding  the
coverage problem and  distinguish our LiCO protocol from the  works presented in
the literature.

The most  discussed coverage problems in  literature can be classified  in three
categories~\cite{li2013survey}   according   to  their   respective   monitoring
objective.  Hence,  area coverage \cite{Misra}  means that every point  inside a
fixed area  must be monitored, while  target coverage~\cite{yang2014novel} refer
to  the objective  of coverage  for a  finite number  of discrete  points called
targets,  and  barrier coverage~\cite{HeShibo}\cite{kim2013maximum}  focuses  on
preventing  intruders   from  entering   into  the   region  of   interest.   In
\cite{Deng2012}  authors  transform the  area  coverage  problem to  the  target
coverage one taking into account the  intersection points among disks of sensors
nodes    or   between    disk   of    sensor   nodes    and   boundaries.     In
\cite{Huang:2003:CPW:941350.941367}  authors prove  that  if  the perimeters  of
sensors are sufficiently  covered it will be  the case for the  whole area. They
provide an algorithm in $O(nd~log~d)$  time to compute the perimeter-coverage of
each  sensor,  where  $d$  denotes  the  maximum  number  of  sensors  that  are
neighboring  to  a  sensor and  $n$  is  the  total  number of  sensors  in  the
network. {\it In LiCO protocol, instead  of determining the level of coverage of
  a set  of discrete  points, our  optimization model is  based on  checking the
  perimeter-coverage of each sensor to activate a minimal number of sensors.}

The major  approach to extend network  lifetime while preserving coverage  is to
divide/organize the  sensors into a suitable  number of set covers  (disjoint or
non-disjoint), where  each set completely  covers a  region of interest,  and to
activate these set  covers successively. The network activity can  be planned in
advance and scheduled  for the entire network lifetime or  organized in periods,
and the set  of active sensor nodes  is decided at the beginning  of each period
\cite{ling2009energy}.  Active node selection is determined based on the problem
requirements (e.g.   area monitoring,  connectivity, or power  efficiency).  For
instance, Jaggi {\em et al.}~\cite{jaggi2006}  address the problem of maximizing
the lifetime  by dividing sensors  into the  maximum number of  disjoint subsets
such  that each  subset  can ensure  both coverage  and  connectivity. A  greedy
algorithm  is applied  once to  solve  this problem  and the  computed sets  are
activated  in   succession  to  achieve   the  desired  network   lifetime.   Vu
\cite{chin2007},  Padmatvathy  {\em   et  al.}~\cite{pc10},  propose  algorithms
working in a periodic fashion where a  cover set is computed at the beginning of
each period.   {\it Motivated by  these works,  LiCO protocol works  in periods,
  where each  period contains a  preliminary phase for information  exchange and
  decisions, followed by a sensing phase where one cover set is in charge of the
  sensing task.}

Various centralized  and distributed approaches, or  even a mixing of  these two
concepts, have  been proposed  to extend the  network lifetime.   In distributed
algorithms~\cite{yangnovel,ChinhVu,qu2013distributed} each sensor decides of its
own activity scheduling  after an information exchange with  its neighbors.  The
main interest of a such approach is  to avoid long range communications and thus
to reduce the energy dedicated to the communications.  Unfortunately, since each
node has only information on its  immediate neighbors (usually the one-hop ones)
it may take a bad decision leading to a global suboptimal solution.  Conversely,
centralized
algorithms~\cite{cardei2005improving,zorbas2010solving,pujari2011high}    always
provide nearly  or close to  optimal solution since  the algorithm has  a global
view of the whole network. The disadvantage of a centralized method is obviously
its high cost  in communications needed to  transmit to a single  node, the base
station which will globally schedule nodes'  activities, data from all the other
sensor nodes in  the area.  The price  in communications can be  very huge since
long range communications will be needed. In fact the larger the WNS, the higher
the  communication and  thus energy  cost.   {\it In  order to  be suitable  for
  large-scale networks,  in the LiCO  protocol the  area of interest  is divided
  into several smaller subregions, and in each  one, a node called the leader is
  in charge  for selecting the active  sensors for the current  period. Thus our
  protocol  is  scalable  and  a  globally distributed  method,  whereas  it  is
  centralized in each subregion.}

Various  coverage scheduling  algorithms have  been developed  this last  years.
Many of  them, dealing with  the maximization of the  number of cover  sets, are
heuristics.   These  heuristics involve  the  construction  of  a cover  set  by
including in priority the sensor nodes  which cover critical targets, that is to
say   targets   that  are   covered   by   the   smallest  number   of   sensors
\cite{berman04,zorbas2010solving}.  Other  approaches are based  on mathematical
programming formulations~\cite{cardei2005energy,5714480,pujari2011high,Yang2014}
and dedicated techniques (solving with a branch-and-bound algorithm available in
optimization  solver).  The  problem is  formulated as  an optimization  problem
(maximization of the lifetime or number of cover sets) under target coverage and
energy  constraints.   Column  generation   techniques,  well-known  and  widely
practiced techniques for  solving linear programs with too  many variables, have
also                                                                        been
used~\cite{castano2013column,rossi2012exact,deschinkel2012column}. {\it  In LiCO
  protocol, each  leader, in charge  of a  subregion, solves an  integer program
  which has a twofold objective: minimize the overcoverage and the undercoverage
  of the perimeter of each sensor.}

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

%The problem of k-coverage in WSNs was addressed~\cite{ammari2012centralized}. It mathematically formulated and the spacial sensor density for full k-coverage determined, where the relation between the communication range and the sensing range constructed by this work to retain the k-coverage and connectivity in WSN. After that, a four configuration protocols have proposed for treating the k-coverage in WSNs.  

%In~\cite{rebai2014branch}, the problem of full grid coverage is formulated using two integer linear programming models: the first, a model that takes into account only the overall coverage constraint; the second, both the connectivity and the full grid coverage constraints have taken into consideration. This work did not take into account the energy constraint.

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

%The authors in~\cite{liu2014generalized} explained that in some applications of WSNs such as structural health monitoring (SHM) and volcano monitoring, the traditional coverage model which is a geographic area defined for individual sensors is not always valid. For this reason, they define a generalized coverage model, which is not need to have the coverage area of individual nodes, but only based on a function to determine whether a set of
%sensor nodes is capable of satisfy the requested monitoring task for a certain area. They have proposed two approaches to divide the deployed nodes into suitable cover sets, which can be used to prolong the network lifetime. 
 
%The work in~\cite{wang2010preserving} addressed the target area coverage problem by proposing a geometric-based activity scheduling scheme, named GAS, to fully cover the target area in WSNs. The authors deals with small area (target area coverage), which can be monitored by a single sensor instead of area coverage, which focuses on a large area that should be monitored by many sensors cooperatively. They explained that GAS is capable to monitor the target area by using a few sensors as possible and it can produce as many cover sets as possible.

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

%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 (LiCO) in WSNs is suggested. The sensing field is divided into smaller subregions by means of divide-and-conquer method, and a LiCO protocol is distributed in each sensor in the subregion. The network lifetime in each subregion is divided into periods, each period includes 4 stages: Information Exchange, Leader election, decision based activity scheduling optimization, and sensing. The leaders are elected in an independent, asynchronous, and distributed way in all the subregions of the WSN. After that, energy-efficient activity scheduling mechanism based new optimization model is performed by each leader in the subregions. This optimization model is based on the perimeter coverage model in order to producing the optimal cover set of active sensors, which are taken the responsibility of sensing during the current period. LiCO protocol merges between two energy efficient mechanisms, which are used the main advantages of the centralized and distributed approaches and avoids the most of their disadvantages.

\section{ The LiCO Protocol Description}
\label{sec:The LiCO Protocol Description}

\noindent  In  this  section,  we  describe in  details  our  Lifetime  Coverage
Optimization protocol.  First we present the  assumptions we made and the models
we considered (in particular the perimeter coverage one), second we describe the
background idea of our protocol, and third  we give the outline of the algorithm
executed by each node.

% 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
energy provision  point of  view.  The  location information  is available  to a
sensor node either  through hardware such as embedded GPS  or location discovery
algorithms.   We  assume  that  each  sensor  node  can  directly  transmit  its
measurements to  a mobile  sink node.  For  example, a sink  can be  an unmanned
aerial  vehicle  (UAV)  flying  regularly  over  the  sensor  field  to  collect
measurements from sensor nodes. A mobile sink node collects the measurements and
transmits them to the base station.   We consider a Boolean disk coverage model,
which is the most  widely used sensor coverage model in  the literature, and all
sensor nodes  have a constant sensing  range $R_s$.  Thus, all  the space points
within a disk centered at a sensor with  a radius equal to the sensing range are
said to be covered  by this sensor. We also assume  that the communication range
$R_c$ satisfies $R_c  \geq 2 \cdot R_s$. In fact,  Zhang and Zhou~\cite{Zhang05}
proved  that if  the  transmission  range fulfills  the  previous hypothesis,  a
complete coverage of a convex area implies connectivity among active nodes.

\indent  LiCO protocol  uses the  same perimeter-coverage  model than  Huang and
Tseng in~\cite{huang2005coverage}. It  can be expressed as follows:  a sensor is
said to be perimeter  covered if all the points on its  perimeter are covered by
at least  one sensor  other than  itself.  They  proved that  a network  area is
$k$-covered if and only if each sensor in the network is $k$-perimeter-covered (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  intersection  of  the  two  sensing
areas. These points are denoted for  neighbor~$i$ by $iL$ and $iR$, respectively
for  left and  right from  neighbor  point of  view.  The  resulting couples  of
intersection points subdivide  the perimeter of sensor~$0$  into portions called
arcs.

\begin{figure}[ht!]
  \centering
  \begin{tabular}{@{}cr@{}}
    \includegraphics[width=75mm]{pcm.jpg} & \raisebox{3.25cm}{(a)}
    \\ \includegraphics[width=75mm]{twosensors.jpg} & \raisebox{2.75cm}{(b)}
  \end{tabular}
  \caption{(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  $\alpha  = arccos  \left(\dfrac{Dist(u,v)}{2R_s}
\right)$.   So, the  arc on  the perimeter  of node~$u$  defined by  the angular
interval $[\pi - \alpha,\pi + \alpha]$ is said to be perimeter-covered by sensor
node $v$.

Every couple of intersection points is placed on the angular interval $[0,2\pi]$
in  a  counterclockwise manner,  leading  to  a  partitioning of  the  interval.
Figure~\ref{pcm2sensors}(a)  illustrates  the arcs  for  the  nine neighbors  of
sensor $0$ and  figure~\ref{expcm} gives the position of  the corresponding arcs
in  the interval  $[0,2\pi]$. More  precisely, we  can see  that the  points are
ordered according  to the  measures of  the angles  defined by  their respective
positions. The intersection points are  then visited one after another, starting
from  first  intersection point  after  point~zero,  and  the maximum  level  of
coverage is determined  for each interval defined by two  successive points. The
maximum  level of  coverage is  equal to  the number  of overlapping  arcs.  For
example, 
between~$5L$  and~$6L$ the maximum  level of  coverage is equal  to $3$
(the value is highlighted in yellow  at the bottom of figure~\ref{expcm}), which
means that at most 2~neighbors can cover  the perimeter in addition to node $0$.
Table~\ref{my-label} summarizes for each coverage  interval the maximum level of
coverage and  the sensor  nodes covering the  perimeter.  The  example discussed
above is thus given by the sixth line of the table.

%The points reported on the line segment $[0,2\pi]$ separates it in intervals as shown in figure~\ref{expcm}. For example, for each neighboring sensor of sensor 0, place the points  $\alpha^ 1_L$, $\alpha^ 1_R$, $\alpha^ 2_L$, $\alpha^ 2_R$, $\alpha^ 3_L$, $\alpha^ 3_R$, $\alpha^ 4_L$, $\alpha^ 4_R$, $\alpha^ 5_L$, $\alpha^ 5_R$, $\alpha^ 6_L$, $\alpha^ 6_R$, $\alpha^ 7_L$, $\alpha^ 7_R$, $\alpha^ 8_L$, $\alpha^ 8_R$, $\alpha^ 9_L$, and $\alpha^ 9_R$; on the line segment $[0,2\pi]$, and then sort all these points in an ascending order into a list.  Traverse the line segment $[0,2\pi]$ by visiting each point in the sorted list from left to right and determine the coverage level of each interval of the sensor 0 (see figure \ref{expcm}). For each interval, we sum up the number of parts of segments, and we deduce a level of coverage for each interval. For instance, the interval delimited by the points $5L$ and $6L$ contains three parts of segments. That means that this part of the perimeter of the sensor $0$ may be covered by three sensors, sensor $0$ itself and sensors $2$ and $5$. The level of coverage of this interval may reach $3$ if all previously mentioned sensors are active. Let say that sensors $0$, $2$ and $5$ are involved in the coverage of this interval. Table~\ref{my-label} summarizes the level of coverage for each interval and the sensors involved in for sensor node 0 in figure~\ref{pcm2sensors}(a). 
% to determine the level of the perimeter coverage for each left and right point of a segment.

\begin{figure*}[ht!]
\centering
\includegraphics[width=137.5mm]{expcm2.jpg}  
\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.

\iffalse

\begin{figure}[ht!]
\centering
\includegraphics[width=90mm]{ex2pcm.jpg}  
\caption{Coverage intervals and contributing sensors for sensor node 0.}
\label{ex2pcm}
\end{figure} 

\fi

 \begin{table}[h]
 \caption{Coverage intervals and contributing sensors for sensor node 0.}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
\hline
\begin{tabular}[c]{@{}c@{}}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}

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


%The optimization algorithm that used by LiCO protocol based on the perimeter coverage levels of the left and right points of the segments and worked to minimize the number of sensor nodes for each left or right point of the segments within each sensor node. The algorithm minimize the perimeter coverage level of the left and right points of the segments, while, it assures that every perimeter coverage level of the left and right points of the segments greater than or equal to 1.

In LiCO  protocol, scheduling of sensor  nodes' activities is formulated  with an
integer program  based on  coverage intervals. The  formulation of  the coverage
optimization problem is  detailed in~section~\ref{cp}.  Note that  when a sensor
node  has a  part of  its sensing  range outside  the WSN  sensing field,  as in
figure~\ref{ex4pcm}, the maximum coverage level for  this arc is set to $\infty$
and  the  corresponding  interval  will  not   be  taken  into  account  by  the
optimization algorithm.
 
\begin{figure}[t!]
\centering
\includegraphics[width=62.5mm]{ex4pcm.jpg}  
\caption{Sensing range outside the WSN's area of interest.}
\label{ex4pcm}
\end{figure} 
%Figure~\ref{ex5pcm} gives an example to compute the perimeter coverage levels for the left and right points of the segments for a sensor node $0$, which has a part of its sensing range exceeding the border of the sensing field of WSN, and it has a six neighbors. In figure~\ref{ex5pcm}, the sensor node $0$ has two segments outside the border of the network sensing field, so the left and right points of the two segments called $-1L$, $-1R$, $-2L$, and $-2R$.
%\begin{figure}[ht!]
%\centering
%\includegraphics[width=75mm]{ex5pcm.jpg}  
%\caption{Coverage intervals and contributing sensors for sensor node 0 having a  part of its sensing range outside the border.}
%\label{ex5pcm}
%\end{figure} 

\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
LiCO, are more  robust against an unexpected  node failure. On the  one hand, if
node failure is discovered before  taking the decision, the corresponding sensor
node will  not be considered  by the optimization  algorithm, and, 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]{Model.pdf}  
\caption{LiCO protocol.}
\label{fig2}
\end{figure} 

We define two types of packets to be used by LiCO 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{LiCO Protocol Algorithm}

\noindent The  pseudocode implementing the  protocol on  a node is  given below.
More  precisely,  Algorithm~\ref{alg:LiCO}  gives  a brief  description  of  the
protocol applied by a sensor node $s_k$ where $k$ is the node index in the WSN.

\begin{algorithm}[h!]                
 % \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{LiCO($s_k$)}
\label{alg:LiCO}
\end{algorithm}

In this  algorithm, K.CurrentSize and  K.PreviousSize refer to the  current size
and the  previous size of  the subnetwork  in the subregion  respectively.  That
means the number  of sensor nodes which are still  alive.  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  number 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 LiCO protocol. Every sensor node is selected as a leader, it is executed the perimeter coverage model only one time during it's life in the network.

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

\section{Lifetime 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 $a^j_{ik}$ 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.
%\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 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]
\caption{Relevant parameters for network initialization.}
% 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}
\label{table3}
% 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 it's 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 for
the  undercoverage  (by  setting  the  $\alpha^j_i$ with  a  larger  value  than
$\beta^j_i$)  so as  to prevent  the non-coverage  for the  interval~$i$ of  the
sensor~$j$.  On the  other hand,  we have  given a  little bit  lower value  for
$\beta^j_i$ so as to minimize the number of active sensor nodes 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 nodes  as few  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 LiCO protocol in  OMNeT++~\cite{varga} simulator.  Besides LiCO, two
other  protocols,  described  in  the  next paragraph,  will  be  evaluated  for
comparison purposes.   The simulations were run  on a laptop DELL  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 LiCO is  compared with 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
LiCO and  DiLCO protocols are  based on the  same framework. In  particular, the
choice for the simulations of a partitioning in 16~subregions was chosen because
it corresponds to the configuration producing  the better 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 LiCO 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 little better
coverage ratio with respectively 99.99\%,  99.91\%, and 99.02\%, against 98.76\%
produced by  LiCO for the  first periods. This  is due to  the fact that  at the
beginning  DiLCO protocol  puts in  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  LiCO provides a better  coverage ratio and keeps  a coverage ratio
greater  than 50\%  for  longer periods  (15  more compared  to  DiLCO, 40  more
compared to DESK). The energy saved by  LiCO in the early periods allows later a
substantial increase of the coverage performance.

\parskip 0pt    
\begin{figure}[h!]
\centering
 \includegraphics[scale=0.5] {R/CR.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 LiCO protocol. The coverage ratio of LiCO protocol is better than other approaches from the period 64.

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


\subsubsection{\bf Active Sensors Ratio}

Having the less active sensor nodes in  each period is essential to minimize the
energy consumption  and so  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 LiCO  protocols compete perfectly  with only 17.92  \% and
20.16 \% active  nodes during the same  time interval. As the  number of periods
increases, LiCO protocol  has a lower number of active  nodes in comparison with
the three other approaches, while keeping a greater coverage ratio as shown in
figure \ref{fig333}.

\begin{figure}[h!]
\centering
\includegraphics[scale=0.5]{R/ASR.eps}  
\caption{Active sensors ratio for 200 deployed nodes.}
\label{fig444}
\end{figure} 

\subsubsection{\bf Energy Consumption}

We study the effect of the energy  consumed by the WSN during the communication,
computation, listening, active, and sleep status for different network densities
and  compare  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 LiCO protocol  is the
most competitive  from the energy  consumption point of  view. As shown  in both
figures, LiCO consumes much less energy than the three other methods.  One might
think that the  resolution of the integer  program is too costly  in energy, but
the  results show  that it  is very  beneficial to  lose a  bit of  time in  the
selection of  sensors to  activate.  Indeed 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]{R/EC95.eps} & \raisebox{2.75cm}{(a)} \\
    \includegraphics[scale=0.475]{R/EC50.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 LiCO protocol,  was improved the lifetime coverage efficiently based on the perimeter coverage model.

 %The other approaches have a high energy consumption due to activating a larger number of 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 LiCO and DiLCO protocols in comparison against 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  the larger for DiLCO
and LiCO  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 two times longer with  LiCO 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
the time, and the lifetime with a coverage  of 50\% is far more longer than with
95\%.

\begin{figure}[h!]
  \centering
  \begin{tabular}{@{}cr@{}}
    \includegraphics[scale=0.475]{R/LT95.eps} & \raisebox{2.75cm}{(a)} \\  
    \includegraphics[scale=0.475]{R/LT50.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, LiCO protocol efficiently prolonged the network lifetime especially for a coverage ratio greater than $50 \%$, whilst it stayed very near to  DiLCO-16 protocol for $95 \%$.

Figure~\ref{figLTALL}  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  Protocol is DiLCO  or LiCO.  Indeed there  are applications
that do not require a 100\% coverage of  the area to be monitored. LiCO might be
an interesting  method since  it achieves  a good balance  between a  high level
coverage ratio and network lifetime. LiCO 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 LiCO is
not so bad for the smallest network sizes.

\begin{figure}[h!]
\centering \includegraphics[scale=0.5]{R/LTa.eps}
\caption{Network lifetime for different coverage ratios.}
\label{figLTALL}
\end{figure} 

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


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

In this paper  we have studied the problem of  lifetime coverage optimization in
WSNs. We designed  a new protocol, called Lifetime  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 LiCO protocol for optimizing the lifetime coverage in each subregion. LiCO protocol combines two efficient techniques:  network
%leader election, which executes the perimeter coverage model (only one time), the optimization algorithm, and sending the schedule produced by the optimization algorithm to other nodes in the subregion ; the second, sensor activity scheduling based optimization in which a new lifetime coverage optimization model is proposed. The main challenges include how to select the  most efficient leader in each subregion and the best schedule of sensor nodes that will optimize the network lifetime coverage
%in the subregion. 
%The network lifetime coverage in each subregion is divided into
%periods, each period consists  of four stages: (i) Information Exchange,
%(ii) Leader Election, (iii) a Decision based new optimization model in order to
%select the  nodes remaining  active for the last stage,  and  (iv) Sensing.
We  carried out  several simulations  to  evaluate the  proposed protocol.   The
simulation  results  show   that  LiCO  is  more   energy-efficient  than  other
approaches, with respect to lifetime,  coverage ratio, active sensors ratio, and
energy consumption.
%Indeed, when dealing with large and dense WSNs, a distributed optimization approach on the subregions of WSN like the one we are proposed allows to reduce the difficulty of a single global optimization problem by partitioning it in many smaller problems, one per subregion, that can be solved more easily. We have  identified different  research directions  that arise  out of  the work presented here.
We plan to extend our framework such that the schedules are planned for multiple
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.

\section*{Acknowledgments}

\noindent  As a  Ph.D.   student, Ali  Kadhum IDREES  would  like to  gratefully
acknowledge the  University of Babylon -  IRAQ for financial support  and Campus
France for the  received support. This work has also been supported by the Labex
ACTION.

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