Update by Ali

diff --git a/LiCO_Journal.tex b/LiCO_Journal.tex
index 38f9e9e9c1738621723239a34397ae2fc5aeea9e..f18c11d59f815b8bcdeed5ff28ed673b841899f4 100644 (file)
--- a/LiCO_Journal.tex
+++ b/LiCO_Journal.tex
@@ -59,20 +59,20 @@ The most important problem in a Wireless Sensor Network (WSN) is to optimize the
 use of its limited energy provision, so  that it can fulfill its monitoring task
 as  long as  possible. Among  known  available approaches  that can  be used  to
 improve  power  management,  lifetime coverage  optimization  provides  activity
 use of its limited energy provision, so  that it can fulfill its monitoring task
 as  long as  possible. Among  known  available approaches  that can  be used  to
 improve  power  management,  lifetime coverage  optimization  provides  activity

-scheduling which ensures  sensing coverage while minimizing the  energy cost. 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
+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.
 % 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.
 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
+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
 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
+OMNeT++, the  discrete event simulator, to  demonstrate that PeCO  is capable to

 offer longer lifetime coverage for WSNs in comparison with some other protocols.
 \end{abstract} 
 
 offer longer lifetime coverage for WSNs in comparison with some other protocols.
 \end{abstract} 
 


@@ -139,18 +139,18 @@ This paper makes the following contributions.
   deviations.
 \item We have conducted extensive simulation  experiments, using the  discrete event
   simulator OMNeT++, to demonstrate the  efficiency of our protocol. We have compared
   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   LiCO   protocol   to   two   approaches   found   in   the   literature:
+  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}
 
   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;
+%Two combined integrated energy-efficient techniques have been used by PeCO 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
 % 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
+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
 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


@@ -162,7 +162,7 @@ Section~\ref{sec:Conclusion and Future Works}.
 \label{sec:Literature Review}
 
 \noindent  In  this section,  we  summarize  some  related works  regarding  the
 \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
+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
 the literature.
 
 The most  discussed coverage problems in  literature can be classified  in three


@@ -180,7 +180,7 @@ 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
 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 LiCO protocol, instead  of determining the level of coverage of
+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.}
 
   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.}
 


@@ -199,7 +199,7 @@ 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
 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,
+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.}
   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.}


@@ -221,7 +221,7 @@ 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  is, the
 higher the  communication and  thus the energy  cost are.   {\it In order  to be
 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  is, the
 higher the  communication and  thus the energy  cost are.   {\it In order  to be

-  suitable for large-scale  networks, in the LiCO protocol,  the area of interest
+  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,
   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,


@@ -240,7 +240,7 @@ optimization  solver).  The  problem is  formulated as  an optimization  problem
 energy  constraints.   Column  generation   techniques,  well-known  and  widely
 practiced techniques for  solving linear programs with too  many variables, have
 also                                                                        been
 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 LiCO
+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.}
   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.}


@@ -281,10 +281,10 @@ used~\cite{castano2013column,rossi2012exact,deschinkel2012column}. {\it  In the
 
 %\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{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.
+%\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 LiCO Protocol Description}
-\label{sec:The LiCO Protocol Description}
+\section{ The PeCO Protocol Description}
+\label{sec:The PeCO 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
 
 \noindent  In  this  section,  we  describe in  details  our  Lifetime  Coverage
 Optimization protocol.  First we present the  assumptions we made and the models


@@ -318,7 +318,7 @@ $R_c$ 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.
 
 proved  that if  the  transmission  range fulfills  the  previous hypothesis,  a
 complete coverage of a convex area implies connectivity among active nodes.
 

-The LiCO protocol  uses the  same perimeter-coverage  model as  Huang and
+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
 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


@@ -425,9 +425,9 @@ above is thus given by the sixth line of the table.
 \end{table}
 
 
 \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.
+%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 LiCO  protocol, scheduling of sensor  nodes' activities is formulated  with an
+In the PeCO  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
 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


@@ -461,7 +461,7 @@ 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
 (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
+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
 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


@@ -477,11 +477,11 @@ the area.
 \begin{figure}[t!]
 \centering
 \includegraphics[width=80mm]{Model.pdf}  
 \begin{figure}[t!]
 \centering
 \includegraphics[width=80mm]{Model.pdf}  

-\caption{LiCO protocol.}
+\caption{PeCO protocol.}

 \label{fig2}
 \end{figure} 
 
 \label{fig2}
 \end{figure} 
 

-We define two types of packets to be used by LiCO protocol:
+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
 %\begin{enumerate}[(a)]
 \begin{itemize} 
 \item INFO  packet: sent  by each  sensor node to  all the  nodes inside  a same


@@ -505,10 +505,10 @@ Five status are possible for a sensor node in the network:
 %\end{enumerate}
 %Below, we describe each phase in more details.
 
 %\end{enumerate}
 %Below, we describe each phase in more details.
 

-\subsection{LiCO Protocol Algorithm}
+\subsection{PeCO Protocol Algorithm}

 
 \noindent The  pseudocode implementing the  protocol on  a node is  given below.
 
 \noindent The  pseudocode implementing the  protocol on  a node is  given below.

-More  precisely,  Algorithm~\ref{alg:LiCO}  gives  a brief  description  of  the
+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{algorithm}[h!]                
 protocol applied by a sensor node $s_k$ where $k$ is the node index in the WSN.
 
 \begin{algorithm}[h!]                


@@ -552,8 +552,8 @@ protocol applied by a sensor node $s_k$ where $k$ is the node index in the WSN.
       }  
   }
   \Else { Exclude $s_k$ from entering in the current sensing stage}
       }  
   }
   \Else { Exclude $s_k$ from entering in the current sensing stage}

-\caption{LiCO($s_k$)}
-\label{alg:LiCO}
+\caption{PeCO($s_k$)}
+\label{alg:PeCO}

 \end{algorithm}
 
 In this  algorithm, K.CurrentSize and K.PreviousSize  respectively represent the
 \end{algorithm}
 
 In this  algorithm, K.CurrentSize and K.PreviousSize  respectively represent the


@@ -570,7 +570,7 @@ 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.
 
 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.
+%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.
 
 
 % 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.
 


@@ -803,7 +803,7 @@ approach.
 \subsection{Simulation Results}
 
 In  order  to  assess and  analyze  the  performance  of  our protocol  we  have
 \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
+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 laptop DELL  with an Intel
 Core~i3~2370~M (2.4~GHz)  processor (2  cores) whose MIPS  (Million Instructions
 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


@@ -816,20 +816,20 @@ 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.
 
 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
+As said previously, the PeCO 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
 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
+PeCO 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,
 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
+whereas 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 ($l=1$).
 
 sensor perimeter. In our experimentations, we chose a level of coverage equal to
 one ($l=1$).
 


@@ -838,13 +838,13 @@ one ($l=1$).
 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\%
 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
+produced by  PeCO 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
 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
+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
 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
+compared to DESK). The energy saved by  PeCO in the early periods allows later a

 substantial increase of the coverage performance.
 
 \parskip 0pt    
 substantial increase of the coverage performance.
 
 \parskip 0pt    


@@ -855,9 +855,9 @@ substantial increase of the coverage performance.
 \label{fig333}
 \end{figure} 
 
 \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.
+%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 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. 
+%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}
 
 
 \subsubsection{\bf Active Sensors Ratio}


@@ -866,9 +866,9 @@ 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
 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
+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
 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
+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}.
 
 the three other approaches, while keeping a greater coverage ratio as shown in
 Figure \ref{fig333}.
 


@@ -885,9 +885,9 @@ 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
 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
+$Lifetime95$ and  $Lifetime50$. The results show  that our PeCO protocol  is the

 most competitive  from the energy  consumption point of  view. As shown  in both
 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
+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
 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


@@ -904,7 +904,7 @@ while keeping a good coverage level.
   \label{fig3EC}
 \end{figure} 
 
   \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 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. 
 
 
  %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. 
 


@@ -913,14 +913,14 @@ while keeping a good coverage level.
 
 \subsubsection{\bf Network Lifetime}
 
 
 \subsubsection{\bf Network Lifetime}
 

-We observe the superiority of LiCO and DiLCO protocols in comparison against the
+We observe the superiority of PeCO 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
 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
+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
 ratio greater than 50\%, we can  see on Figure~\ref{fig3LT}(b) that the lifetime

-is about twice longer with  LiCO compared to DESK protocol.  The performance
+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
 the time, and the lifetime with a coverage  of 50\% is far more longer than with
 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


@@ -937,18 +937,18 @@ the time, and the lifetime with a coverage  of 50\% is far more longer than with
   \label{fig3LT}
 \end{figure} 
 
   \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 $95 \%$.
+%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 $95\%$
 
 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
+respectively, where  Protocol is 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
 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
+coverage ratio and network lifetime. PeCO always outperforms DiLCO for the three

 lower  coverage  ratios,  moreover  the   improvements  grow  with  the  network
 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
+size. DiLCO is better  for coverage ratios near 100\%, but in  that case PeCO is

 not so bad for the smallest network sizes.
 
 \begin{figure}[h!]
 not so bad for the smallest network sizes.
 
 \begin{figure}[h!]


@@ -957,7 +957,7 @@ not so bad for the smallest network sizes.
 \label{figLTALL}
 \end{figure} 
 
 \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.
+%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}
 
 
 \section{Conclusion and Future Works}


@@ -975,7 +975,7 @@ 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.
 
 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
+%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
 %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


@@ -983,7 +983,7 @@ targets/points to be covered.
 %(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
 %(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  LiCO  is  more   energy-efficient  than  other
+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.
 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.

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diff --git a/R/LT95.pdf b/R/LT95.pdf
index 2c264b3560551bf57300e3a6b7fdb5607ffb1794..ec103de861b600ae4249ccd0c8ac3e7e7c931ca2 100644 (file)
Binary files a/R/LT95.pdf and b/R/LT95.pdf differ

diff --git a/R/LTa.eps b/R/LTa.eps
index 876a77dc7b62dd34215e76b375eb0231be11c066..67ca46d48d1411fd6ef20e6a6d405841913cf6f9 100644 (file)
--- a/R/LTa.eps
+++ b/R/LTa.eps
@@ -2,7 +2,7 @@
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 %%HiResBoundingBox: 54 53.5 535 401.5
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+%%CreationDate: Mon Feb  2 16:16:10 2015

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@@ -513,7 +513,7 @@ SDict begin [
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   /Author (ali)
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-  /CreationDate (Wed Dec 17 01:57:52 2014)
+  /CreationDate (Mon Feb  2 16:16:10 2015)

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@@ -844,7 +844,7 @@ LT0
 LT1
 0.10 0.10 0.44 C LCb setrgbcolor
 1156 3220 M
 LT1
 0.10 0.10 0.44 C LCb setrgbcolor
 1156 3220 M

-[ [(Helvetica) 110.0 0.0 true true 0 (LiCO/50)]
+[ [(Helvetica) 110.0 0.0 true true 0 (PeCO/50)]

 ] -36.7 MRshow
 LT1
 0.10 0.10 0.44 C 1.000 1222 3193 327 55 BoxColFill
 ] -36.7 MRshow
 LT1
 0.10 0.10 0.44 C 1.000 1222 3193 327 55 BoxColFill


@@ -874,7 +874,7 @@ LT2
 LT3
 1.00 0.00 0.00 C LCb setrgbcolor
 1156 3000 M
 LT3
 1.00 0.00 0.00 C LCb setrgbcolor
 1156 3000 M

-[ [(Helvetica) 110.0 0.0 true true 0 (LiCO/80)]
+[ [(Helvetica) 110.0 0.0 true true 0 (PeCO/80)]

 ] -36.7 MRshow
 LT3
 1.00 0.00 0.00 C 1.000 1222 2973 327 55 BoxColFill
 ] -36.7 MRshow
 LT3
 1.00 0.00 0.00 C 1.000 1222 2973 327 55 BoxColFill


@@ -904,7 +904,7 @@ LT4
 LT5
 0.18 0.55 0.34 C LCb setrgbcolor
 1156 2780 M
 LT5
 0.18 0.55 0.34 C LCb setrgbcolor
 1156 2780 M

-[ [(Helvetica) 110.0 0.0 true true 0 (LiCO/85)]
+[ [(Helvetica) 110.0 0.0 true true 0 (PeCO/85)]

 ] -36.7 MRshow
 LT5
 0.18 0.55 0.34 C 1.000 1222 2753 327 55 BoxColFill
 ] -36.7 MRshow
 LT5
 0.18 0.55 0.34 C 1.000 1222 2753 327 55 BoxColFill


@@ -934,7 +934,7 @@ LT6
 LT7
 0.00 0.55 0.55 C LCb setrgbcolor
 1156 2560 M
 LT7
 0.00 0.55 0.55 C LCb setrgbcolor
 1156 2560 M

-[ [(Helvetica) 110.0 0.0 true true 0 (LiCO/90)]
+[ [(Helvetica) 110.0 0.0 true true 0 (PeCO/90)]

 ] -36.7 MRshow
 LT7
 0.00 0.55 0.55 C 1.000 1222 2533 327 55 BoxColFill
 ] -36.7 MRshow
 LT7
 0.00 0.55 0.55 C 1.000 1222 2533 327 55 BoxColFill


@@ -964,7 +964,7 @@ LT8
 LT0
 0.50 0.00 0.00 C LCb setrgbcolor
 1156 2340 M
 LT0
 0.50 0.00 0.00 C LCb setrgbcolor
 1156 2340 M

-[ [(Helvetica) 110.0 0.0 true true 0 (LiCO/95)]
+[ [(Helvetica) 110.0 0.0 true true 0 (PeCO/95)]

 ] -36.7 MRshow
 LT0
 0.50 0.00 0.00 C 1.000 1222 2313 327 55 BoxColFill
 ] -36.7 MRshow
 LT0
 0.50 0.00 0.00 C 1.000 1222 2313 327 55 BoxColFill