Modif by Karine

diff --git a/article.tex b/article.tex
index a3fb2092f1426f674c1981f2d9b5e82f8f969212..7ae9e53bf699fef06fffb4215eaee9c564d7d966 100644 (file)
--- a/article.tex
+++ b/article.tex
@@ -187,8 +187,8 @@ many cover sets) can be added to the above list.
 \subsection{Centralized approaches}
 
 The major approach  is to divide/organize the sensors into  a suitable number of
 \subsection{Centralized approaches}
 
 The major approach  is to divide/organize the sensors into  a suitable number of

-set covers where  each set completely covers an interest  region and to activate
-these set covers successively.  The centralized algorithms always provide nearly
+cover sets where  each set completely covers an interest  region and to activate
+these cover sets successively.  The centralized algorithms always provide nearly

 or close  to optimal solution since the  algorithm has global view  of the whole
 network. Note that  centralized algorithms have the advantage  of requiring very
 low  processing  power  from  the  sensor  nodes,  which  usually  have  limited
 or close  to optimal solution since the  algorithm has global view  of the whole
 network. Note that  centralized algorithms have the advantage  of requiring very
 low  processing  power  from  the  sensor  nodes,  which  usually  have  limited


@@ -258,9 +258,9 @@ perimeter coverage model from~\cite{Huang:2003:CPW:941350.941367}.
 %heterogeneous energy wireless sensor networks. 
 %In this work, the coverage protocol distributed in each sensor node in the subregion but the optimization take place over the the whole subregion. We consider only distributing the coverage protocol over two subregions. 
 
 %heterogeneous energy wireless sensor networks. 
 %In this work, the coverage protocol distributed in each sensor node in the subregion but the optimization take place over the the whole subregion. We consider only distributing the coverage protocol over two subregions. 
 

-The  works presented in  \cite{Bang, Zhixin,  Zhang} focuses  on coverage-aware,
+The  works presented in  \cite{Bang, Zhixin,  Zhang} focuse  on coverage-aware,

 distributed energy-efficient,  and distributed clustering  methods respectively,
 distributed energy-efficient,  and distributed clustering  methods respectively,

-which aims to extend the network  lifetime, while the coverage is ensured.  More
+which aim to extend the network  lifetime, while the coverage is ensured.  More

 recently, Shibo  et al.  \cite{Shibo} have  expressed the coverage  problem as a
 minimum weight submodular set cover problem and proposed a Distributed Truncated
 Greedy Algorithm (DTGA) to solve it.  They take advantage from both temporal and
 recently, Shibo  et al.  \cite{Shibo} have  expressed the coverage  problem as a
 minimum weight submodular set cover problem and proposed a Distributed Truncated
 Greedy Algorithm (DTGA) to solve it.  They take advantage from both temporal and


@@ -419,9 +419,9 @@ proposed in \cite{Huang:2003:CPW:941350.941367}.
 %heterogeneous energy wireless sensor networks. 
 %In this work, the coverage protocol distributed in each sensor node in the subregion but the optimization take place over the the whole subregion. We consider only distributing the coverage protocol over two subregions. 
 
 %heterogeneous energy wireless sensor networks. 
 %In this work, the coverage protocol distributed in each sensor node in the subregion but the optimization take place over the the whole subregion. We consider only distributing the coverage protocol over two subregions. 
 

-The  works presented in  \cite{Bang, Zhixin,  Zhang} focuses  on coverage-aware,
+The  works presented in  \cite{Bang, Zhixin,  Zhang} focuse  on coverage-aware,

 distributed energy-efficient,  and distributed clustering  methods respectively,
 distributed energy-efficient,  and distributed clustering  methods respectively,

-which aims  to extend the network  lifetime, while the coverage  is ensured.  S.
+which aim  to extend the network  lifetime, while the coverage  is ensured.  S.

 Misra et al.   \cite{Misra} have proposed a localized  algorithm for coverage in
 sensor networks.  The  algorithm conserve the energy while  ensuring the network
 coverage by activating the subset of  sensors with the minimum overlap area. The
 Misra et al.   \cite{Misra} have proposed a localized  algorithm for coverage in
 sensor networks.  The  algorithm conserve the energy while  ensuring the network
 coverage by activating the subset of  sensors with the minimum overlap area. The


@@ -596,7 +596,7 @@ There are five status for each sensor node in the network:
 \item LISTENING: sensor node is waiting for a decision (to be active or not);
 \item  COMPUTATION: sensor  node  has been  elected  as leader  and applies  the
   optimization process;
 \item LISTENING: sensor node is waiting for a decision (to be active or not);
 \item  COMPUTATION: sensor  node  has been  elected  as leader  and applies  the
   optimization process;

-\item ACTIVE: sensor node participate to the monitoring of the area;
+\item ACTIVE: sensor node is participating to the monitoring of the area;

 \item SLEEP: sensor node is turned off to save energy;
 \item COMMUNICATION: sensor node is transmitting or receiving packet.
 \end{enumerate}
 \item SLEEP: sensor node is turned off to save energy;
 \item COMMUNICATION: sensor node is transmitting or receiving packet.
 \end{enumerate}


@@ -757,8 +757,8 @@ absence  of  monitoring  on  some  parts  of the  subregion  by  minimizing  the
 undercoverage.  The weights  $W_\theta$ and $W_U$ must be  properly chosen so as
 to guarantee that the maximum number of points are covered during each round. 
 %% MS W_theta is smaller than W_u => problem with the following sentence
 undercoverage.  The weights  $W_\theta$ and $W_U$ must be  properly chosen so as
 to guarantee that the maximum number of points are covered during each round. 
 %% MS W_theta is smaller than W_u => problem with the following sentence

-In our simulations priority is given  to the coverage by choosing $W_{\theta}$ very
-large compared to $W_U$.
+In our simulations priority is given  to the coverage by choosing $W_{U}$ very
+large compared to $W_{\theta}$.

 %The Active-Sleep packet includes the schedule vector with the number of rounds that should be applied by the receiving sensor node during the sensing phase.
 
 \subsection{Sensing phase}
 %The Active-Sleep packet includes the schedule vector with the number of rounds that should be applied by the receiving sensor node during the sensing phase.
 
 \subsection{Sensing phase}


@@ -823,8 +823,7 @@ random topologies and  the results presented hereafter are  the average of these
 25 runs.
 %Based on the results of our proposed work in~\cite{idrees2014coverage}, we found as the region of interest are divided into larger subregions as the network lifetime increased. In this simulation, the network are divided into 16 subregions. 
 We  performed  simulations for  five  different  densities  varying from  50  to
 25 runs.
 %Based on the results of our proposed work in~\cite{idrees2014coverage}, we found as the region of interest are divided into larger subregions as the network lifetime increased. In this simulation, the network are divided into 16 subregions. 
 We  performed  simulations for  five  different  densities  varying from  50  to

-250~nodes. Experimental results are obtained from randomly generated networks in
-which  nodes  are deployed  over  a  $50 \times  25~m^2  $  sensing field.  More
+250~nodes deployed  over  a  $50 \times  25~m^2  $  sensing field.  More

 precisely, the  deployment is controlled  at a coarse  scale in order  to ensure
 that  the deployed  nodes can  cover the  sensing field  with the  given sensing
 range.
 precisely, the  deployment is controlled  at a coarse  scale in order  to ensure
 that  the deployed  nodes can  cover the  sensing field  with the  given sensing
 range.


@@ -907,8 +906,7 @@ collects  data, and  the  power supply  which  powers the  complete sensor  node
 \cite{raghunathan2002energy}. Each  of the first three subsystems  can be turned
 on or  off depending on  the current status  of the sensor.   Energy consumption
 (expressed in  milliWatt per second) for  the different status of  the sensor is
 \cite{raghunathan2002energy}. Each  of the first three subsystems  can be turned
 on or  off depending on  the current status  of the sensor.   Energy consumption
 (expressed in  milliWatt per second) for  the different status of  the sensor is

-summarized in Table~\ref{table4}.  The energy  needed to send or receive a 1-bit
-packet is equal to $0.2575~mW$.
+summarized in Table~\ref{table4}.  

 
 \begin{table}[ht]
 \caption{The Energy Consumption Model}
 
 \begin{table}[ht]
 \caption{The Energy Consumption Model}


@@ -948,7 +946,8 @@ communication. The size of the  INFO packet and Active-Sleep packet are 112~bits
 and 24~bits  respectively.  The  value of energy  spent to send  a 1-bit-content
 message is  obtained by using  the equation in  ~\cite{raghunathan2002energy} to
 calculate  the energy cost  for transmitting  messages and  we propose  the same
 and 24~bits  respectively.  The  value of energy  spent to send  a 1-bit-content
 message is  obtained by using  the equation in  ~\cite{raghunathan2002energy} to
 calculate  the energy cost  for transmitting  messages and  we propose  the same

-value for receiving the packets.
+value for receiving the packets. The energy  needed to send or receive a 1-bit
+packet is equal to $0.2575~mW$.

 
 The initial energy of each node  is randomly set in the interval $[500;700]$.  A
 sensor node  will not participate in the  next round if its  remaining energy is
 
 The initial energy of each node  is randomly set in the interval $[500;700]$.  A
 sensor node  will not participate in the  next round if its  remaining energy is


@@ -1188,7 +1187,7 @@ into account for scheduling of the sensing phase. The times obtained for $T=1,3$
 or $5$ seems bearable, but for $T=7$ they become quickly unsuitable for a sensor
 node, especially when  the sensor network size increases.   Again, we can notice
 that if we want  to schedule the nodes activities for a  large number of rounds,
 or $5$ seems bearable, but for $T=7$ they become quickly unsuitable for a sensor
 node, especially when  the sensor network size increases.   Again, we can notice
 that if we want  to schedule the nodes activities for a  large number of rounds,

-we need to choose a relevant number of subregion in order to avoid a complicated
+we need to choose a relevant number of subregions in order to avoid a complicated

 and cumbersome optimization.  On the one hand, a large value  for $T$ permits to
 reduce the  energy-overhead due  to the three  pre-sensing phases, on  the other
 hand  a leader  node may  waste a  considerable amount  of energy  to  solve the
 and cumbersome optimization.  On the one hand, a large value  for $T$ permits to
 reduce the  energy-overhead due  to the three  pre-sensing phases, on  the other
 hand  a leader  node may  waste a  considerable amount  of energy  to  solve the