Update by Ali

diff --git a/CHAPITRE_04.tex b/CHAPITRE_04.tex
index 9770cc7244b705aa377fa36ea182978e3307cec3..ce90fb093c2ba9895a87d68bf5b1984a58e6f3cd 100644 (file)
--- a/CHAPITRE_04.tex
+++ b/CHAPITRE_04.tex
@@ -117,7 +117,7 @@ $X_{25}=( p_x + R_s * (\frac{1}{2}), p_y + R_s * (\frac{-\sqrt{3}}{2})) $.
 
 \begin{figure}[ht!]
 \centering
 
 \begin{figure}[ht!]
 \centering

-\includegraphics[scale=0.80]{Figures/ch4/FirstModel.pdf} % 70mm
+\includegraphics[scale=0.90]{Figures/ch4/OneSensingRound.jpg} % 70mm

 \caption{DiLCO protocol}
 \label{FirstModel}
 \end{figure} 
 \caption{DiLCO protocol}
 \label{FirstModel}
 \end{figure} 


@@ -148,18 +148,16 @@ This  step includes choosing  the Wireless  Sensor Node  Leader (WSNL), which  w
 \label{ch4:sec:02:03:03}
 The  WSNL will  solve an  integer  program (see  section~\ref{ch4:sec:03}) to select which sensors will be  activated in the following sensing phase to cover  the subregion.  WSNL will send  Active-Sleep packet  to each sensor in the subregion based on the algorithm's results.
 
 \label{ch4:sec:02:03:03}
 The  WSNL will  solve an  integer  program (see  section~\ref{ch4:sec:03}) to select which sensors will be  activated in the following sensing phase to cover  the subregion.  WSNL will send  Active-Sleep packet  to each sensor in the subregion based on the algorithm's results.
 

+($RE_j$)  corresponds to its remaining energy) to be
+alive during  the selected rounds knowing  that $E_{th}$ is the  amount of energy
+required to be alive during one round.

 
 \subsubsection{Sensing phase}
 \label{ch4:sec:02:03:04}
 
 \subsubsection{Sensing phase}
 \label{ch4:sec:02:03:04}

-Active  sensors  in the  round  will  execute  their sensing  task  to
-preserve maximal  coverage in the  region of interest. We  will assume
-that the cost  of keeping a node awake (or asleep)  for sensing task is
-the same  for all wireless sensor  nodes in the  network.  Each sensor
-will receive  an Active-Sleep  packet from WSNL  informing it  to stay
-awake or to go to sleep  for a time  equal to  the period of  sensing until
-starting a new round.
-
-An outline of the  protocol implementation is given by Algorithm~\ref{alg:DiLCO} which describes the execution of a period  by a node (denoted by $s_j$  for a sensor  node indexed by  $j$). In  the beginning,  a node  checks whether  it has enough energy to stay active during the next sensing phase. If yes, it exchanges information  with  all the  other  nodes belonging  to  the  same subregion:  it collects from each node its position coordinates, remaining energy ($RE_j$), ID,
+Active  sensors  in the  round  will  execute  their sensing  task  to preserve maximal  coverage in the  region of interest. We  will assume that the cost  of keeping a node awake (or asleep)  for sensing task is the same  for all wireless sensor  nodes in the  network.  Each sensor will receive  an Active-Sleep  packet from WSNL  informing it  to stay awake or to go to sleep  for a time  equal to  the period of  sensing until starting a new round.
+
+An outline of the  protocol implementation is given by Algorithm~\ref{alg:DiLCO} which describes the execution of a period  by a node (denoted by $s_j$  for a sensor  node indexed by  $j$). In  the beginning,  a node  checks whether  it has enough energy to stay active during the next sensing phase (i.e., the remaining energy ($RE_j$) $\geq$ the  amount of energy
+required to be alive during one round($E_{th}$)). If yes, it exchanges information  with  all the  other  nodes belonging  to  the  same subregion:  it collects from each node its position coordinates, remaining energy ($RE_j$), ID,

 and  the number  of  one-hop neighbors  still  alive. Once  the  first phase  is completed, the nodes  of a subregion choose a leader to  take the decision based on  the  following  criteria   with  decreasing  importance:  larger  number  of neighbors, larger remaining energy, and  then in case of equality, larger index. After that,  if the sensor node is  leader, it will execute  the integer program algorithm (see Section~\ref{ch4:sec:03})  which provides a set of  sensors planned to be active in the next sensing phase. As leader, it will send an Active-Sleep packet to each sensor  in the same subregion to  indicate it if it has to  be active or not.  Alternately, if  the  sensor  is not  the  leader, it  will  wait for  the Active-Sleep packet to know its state for the coming sensing phase.
 
 \begin{algorithm}[h!]                
 and  the number  of  one-hop neighbors  still  alive. Once  the  first phase  is completed, the nodes  of a subregion choose a leader to  take the decision based on  the  following  criteria   with  decreasing  importance:  larger  number  of neighbors, larger remaining energy, and  then in case of equality, larger index. After that,  if the sensor node is  leader, it will execute  the integer program algorithm (see Section~\ref{ch4:sec:03})  which provides a set of  sensors planned to be active in the next sensing phase. As leader, it will send an Active-Sleep packet to each sensor  in the same subregion to  indicate it if it has to  be active or not.  Alternately, if  the  sensor  is not  the  leader, it  will  wait for  the Active-Sleep packet to know its state for the coming sensing phase.
 
 \begin{algorithm}[h!]                


@@ -438,23 +436,23 @@ in  order to  minimize  the communication  overhead  and maximize  the
 network lifetime. The Active Sensors Ratio is defined as follows:
 \begin{equation*}
 \scriptsize
 network lifetime. The Active Sensors Ratio is defined as follows:
 \begin{equation*}
 \scriptsize

-\mbox{ASR}(\%) =  \frac{\sum\limits_{r=1}^R \mbox{$A_r$}}{\mbox{$S$}} \times 100 .
+\mbox{ASR}(\%) =  \frac{\sum\limits_{r=1}^R \mbox{$A_r$}}{\mbox{$J$}} \times 100 .

 \end{equation*}
 \end{equation*}

-Where: $A_r$ is the number of active sensors in the subregion $r$ during current period, $S$ is the total number of sensors in the network, and $R$ is the total number of the subregions in the network.
+Where: $A_r$ is the number of active sensors in the subregion $r$ during current period, $J$ is the total number of sensors in the network, and $R$ is the total number of the subregions in the network.

 
 \item {{\bf Execution Time}:} a  sensor  node has  limited  energy  resources  and computing  power, therefore it is important that the proposed algorithm has the shortest possible execution  time. The energy of  a sensor node  must be mainly used   for  the  sensing   phase,  not   for  the   pre-sensing  ones. In this dissertation, the original execution time  is computed on a laptop  DELL with Intel Core~i3~2370~M (2.4 GHz)  processor (2  cores) and the  MIPS (Million Instructions  Per Second) rate equal to 35330. To be consistent  with the use of a sensor node with Atmel's AVR ATmega103L  microcontroller (6 MHz) and  a MIPS rate  equal to 6 to  run the optimization   resolution,   this  time   is   multiplied   by  2944.2   $\left( \frac{35330}{2} \times  \frac{1}{6} \right)$.  
   
 
 \item {{\bf Execution Time}:} a  sensor  node has  limited  energy  resources  and computing  power, therefore it is important that the proposed algorithm has the shortest possible execution  time. The energy of  a sensor node  must be mainly used   for  the  sensing   phase,  not   for  the   pre-sensing  ones. In this dissertation, the original execution time  is computed on a laptop  DELL with Intel Core~i3~2370~M (2.4 GHz)  processor (2  cores) and the  MIPS (Million Instructions  Per Second) rate equal to 35330. To be consistent  with the use of a sensor node with Atmel's AVR ATmega103L  microcontroller (6 MHz) and  a MIPS rate  equal to 6 to  run the optimization   resolution,   this  time   is   multiplied   by  2944.2   $\left( \frac{35330}{2} \times  \frac{1}{6} \right)$.  
   

-\item {{\bf Stopped simulation runs}:} A simulation ends  when the  sensor network becomes disconnected (some nodes are dead and are not able to send information to the base station). We report the number of simulations that are stopped due to network disconnections and for which round it occurs ( in chapter 3, period consists of one round).
+\item {{\bf Stopped simulation runs}:} A simulation ends  when the  sensor network becomes disconnected (some nodes are dead and are not able to send information to the base station). We report the number of simulations that are stopped due to network disconnections and for which round it occurs.% ( in chapter 4, period consists of one round).

 
 \end{enumerate}
 
 
 
 
 \end{enumerate}
 
 
 

-\subsection{Performance Analysis for Different Subregions}
+\subsection{Performance Analysis for Different Number of Subregions}

 \label{ch4:sec:04:05}
   
 \label{ch4:sec:04:05}
   

-In this subsection, we are studied the performance of our DiLCO protocol for a different number of subregions (Leaders).
-The DiLCO-1 protocol is a centralized approach to all the area of the interest, while  DiLCO-2, DiLCO-4, DiLCO-8, DiLCO-16 and DiLCO-32 are distributed on two, four, eight, sixteen, and thirty-two subregions respectively. We did not take the DiLCO-1 protocol in our simulation results because it needs a high execution time to give the decision leading to consume all its energy before producing the solution for the optimization problem.
+In this subsection, we are study the performance of our DiLCO protocol for a different number of subregions (Leaders).
+The DiLCO-1 protocol is a centralized approach to all the area of the interest, while  DiLCO-2, DiLCO-4, DiLCO-8, DiLCO-16 and DiLCO-32 are distributed on two, four, eight, sixteen, and thirty-two subregions respectively. We do not take the DiLCO-1 protocol in our simulation results because it needs a high execution time to give the decision leading to consume all its energy before producing the solution for the optimization problem.

 
 \begin{enumerate}[i)]
 \item {{\bf Coverage Ratio}}
 
 \begin{enumerate}[i)]
 \item {{\bf Coverage Ratio}}


@@ -469,7 +467,7 @@ In this experiment, Figure~\ref{Figures/ch4/R1/CR} shows the average coverage ra
 \label{Figures/ch4/R1/CR}
 \end{figure} 
 It can be seen that DiLCO protocol (with 4, 8, 16 and 32 subregions) gives nearly similar coverage ratios during the first thirty rounds.  
 \label{Figures/ch4/R1/CR}
 \end{figure} 
 It can be seen that DiLCO protocol (with 4, 8, 16 and 32 subregions) gives nearly similar coverage ratios during the first thirty rounds.  

-DiLCO-2 protocol gives near similar coverage ratio with other ones for first 10 rounds and then decreased until the died of the network in the round $18^{th}$ because it consumes more energy with the effect of the network disconnection. 
+DiLCO-2 protocol gives near similar coverage ratio with other ones for first 10 rounds and then decreased until the died of the network in the round $18^{th}$. In case of only 2 subregions, the energy consumption is high and the network is rapidly disconnected. 

 As shown in the figure ~\ref{Figures/ch4/R1/CR}, as the number of subregions increases,  the coverage preservation for the area of interest increases for a larger number of rounds. Coverage ratio decreases when the number of rounds increases due to dead nodes. Although some nodes are dead, thanks to  DiLCO-8,  DiLCO-16, and  DiLCO-32 protocols,  other nodes are  preserved to ensure the coverage. Moreover, when we have a dense sensor network, it leads to maintain the  coverage for a larger number of rounds. DiLCO-8,  DiLCO-16, and  DiLCO-32 protocols are slightly more efficient than other protocols, because they subdivide the area of interest into 8, 16 and 32~subregions; if one of the subregions becomes disconnected, the coverage may be still ensured in the remaining subregions.
 
 \item {{\bf Active Sensors Ratio}}
 As shown in the figure ~\ref{Figures/ch4/R1/CR}, as the number of subregions increases,  the coverage preservation for the area of interest increases for a larger number of rounds. Coverage ratio decreases when the number of rounds increases due to dead nodes. Although some nodes are dead, thanks to  DiLCO-8,  DiLCO-16, and  DiLCO-32 protocols,  other nodes are  preserved to ensure the coverage. Moreover, when we have a dense sensor network, it leads to maintain the  coverage for a larger number of rounds. DiLCO-8,  DiLCO-16, and  DiLCO-32 protocols are slightly more efficient than other protocols, because they subdivide the area of interest into 8, 16 and 32~subregions; if one of the subregions becomes disconnected, the coverage may be still ensured in the remaining subregions.
 
 \item {{\bf Active Sensors Ratio}}


@@ -482,7 +480,7 @@ As shown in the figure ~\ref{Figures/ch4/R1/CR}, as the number of subregions inc
 \label{Figures/ch4/R1/ASR}
 \end{figure} 
 
 \label{Figures/ch4/R1/ASR}
 \end{figure} 
 

-The results presented in figure~\ref{Figures/ch4/R1/ASR} show the increase in the number of subregions led to increasing in the number of active nodes. The DiLCO-16 and DiLCO-32 protocols have a larger number of active nodes, but it preserve the coverage for a larger number of rounds. The advantage of the DiLCO-16 and DiLCO-32 protocols are that even if a network is disconnected in one subregion, the other ones usually continues the optimization process, and this extends the lifetime of the network.
+The results presented in figure~\ref{Figures/ch4/R1/ASR} show the increase of the number of subregions lead to the increase of the number of active nodes. The DiLCO-16 and DiLCO-32 protocols have a larger number of active nodes, but it preserve the coverage for a larger number of rounds. The advantage of the DiLCO-16 and DiLCO-32 protocols are that even if a network is disconnected in one subregion, the other ones usually continues the optimization process, and this extends the lifetime of the network.

 
 \item {{\bf The percentage of stopped simulation runs}}
 %\subsubsection{The percentage of stopped simulation runs}
 
 \item {{\bf The percentage of stopped simulation runs}}
 %\subsubsection{The percentage of stopped simulation runs}


@@ -494,7 +492,7 @@ Figure~\ref{Figures/ch4/R1/SR} illustrates the percentage of stopped simulation
 \label{Figures/ch4/R1/SR}
 \end{figure} 
 
 \label{Figures/ch4/R1/SR}
 \end{figure} 
 

-It can be observed that the DiLCO-2  is the approach which stops first because it applied the optimization on only two subregions for the area of interest that is why it is first exhibits network disconnections.
+DiLCO-2 is the approach which stops first because it applies the optimization on only two subregions and the high energy consumption accelerate the network disconnection.

 Thus, as explained previously, in case of the DiLCO-16 and DiLCO-32 with several subregions, the optimization effectively continues as long as a network in a subregion is still connected. This longer partial coverage optimization participates in extending the network lifetime. 
 
 \item {{\bf The Energy Consumption}}
 Thus, as explained previously, in case of the DiLCO-16 and DiLCO-32 with several subregions, the optimization effectively continues as long as a network in a subregion is still connected. This longer partial coverage optimization participates in extending the network lifetime. 
 
 \item {{\bf The Energy Consumption}}


@@ -508,7 +506,7 @@ We measure the energy consumed by the sensors during the communication, listenin
 \label{Figures/ch4/R1/EC95}
 \end{figure} 
 
 \label{Figures/ch4/R1/EC95}
 \end{figure} 
 

-The results show that DiLCO-16 and DiLCO-32 are the most competitive from the energy consumption point of view but as the network size increase the energy consumption increase compared with DiLCO-2,  DiLCO-4, and DiLCO-8. The other approaches have a high energy consumption due to the energy consumed during the different modes of the sensor node.\\
+The results show that DiLCO-16 and DiLCO-32 are the most competitive from the energy consumption point of view. The other approaches have a high energy consumption due to the energy consumed during the different modes of the sensor node.\\

  
 As shown in Figures~\ref{Figures/ch4/R1/EC95} and ~\ref{Figures/ch4/R1/EC50}, DiLCO-2 consumes more energy than the other versions of DiLCO, especially for large sizes of network. This is easy to understand since the bigger the number of sensors involved in the integer program, the larger the time computation to solve the optimization problem, as well as the higher energy consumed during the communication.  
 \begin{figure}[h!]
  
 As shown in Figures~\ref{Figures/ch4/R1/EC95} and ~\ref{Figures/ch4/R1/EC50}, DiLCO-2 consumes more energy than the other versions of DiLCO, especially for large sizes of network. This is easy to understand since the bigger the number of sensors involved in the integer program, the larger the time computation to solve the optimization problem, as well as the higher energy consumed during the communication.  
 \begin{figure}[h!]


@@ -517,11 +515,11 @@ As shown in Figures~\ref{Figures/ch4/R1/EC95} and ~\ref{Figures/ch4/R1/EC50}, Di
 \caption{Energy Consumption for Lifetime50}
 \label{Figures/ch4/R1/EC50}
 \end{figure} 
 \caption{Energy Consumption for Lifetime50}
 \label{Figures/ch4/R1/EC50}
 \end{figure} 

-In fact,  a distributed method on the subregions greatly reduces the number of communications, the time of listening and computation so thanks to the partitioning of the initial network in several independent subnetworks. 
+In fact,  the distribution of the computation over many subregions greatly reduces the number of communications, the time of listening and computation so thanks to the partitioning of the initial network in several independent subnetworks. 

 
 \item {{\bf Execution Time}}
 %\subsubsection{Execution Time}
 
 \item {{\bf Execution Time}}
 %\subsubsection{Execution Time}

-In this experiment, the execution time of the our distributed optimization approach has been studied. Figure~\ref{Figures/ch4/R1/T} gives the average execution times in seconds for the decision phase (solving of the optimization problem) during one round. They are given for the different approaches and various numbers of sensors. The original execution time is computed as described in section \ref{ch4:sec:04:02}.
+In this experiment, the execution time of the our distributed optimization approach has been studied. Figure~\ref{Figures/ch4/R1/T} gives the average execution times in seconds for the decision phase (solving of the optimization problem) during one round. They are given for the different approaches and various numbers of sensors. The original execution time is computed as described in section \ref{ch4:sec:04:04}. 

 %The original execution time is computed on a laptop DELL with intel Core i3 2370 M (2.4 GHz) processor (2 cores) and the MIPS (Million Instructions Per Second) rate equal to 35330. To be consistent with the use of a sensor node with Atmels AVR ATmega103L microcontroller (6 MHz) and a MIPS rate equal to 6 to run the optimization resolution, this time is multiplied by 2944.2 $\left( \frac{35330}{2} \times 6\right)$ and reported on Figure~\ref{fig8} for different network sizes.
 
 \begin{figure}[h!]
 %The original execution time is computed on a laptop DELL with intel Core i3 2370 M (2.4 GHz) processor (2 cores) and the MIPS (Million Instructions Per Second) rate equal to 35330. To be consistent with the use of a sensor node with Atmels AVR ATmega103L microcontroller (6 MHz) and a MIPS rate equal to 6 to run the optimization resolution, this time is multiplied by 2944.2 $\left( \frac{35330}{2} \times 6\right)$ and reported on Figure~\ref{fig8} for different network sizes.
 
 \begin{figure}[h!]


@@ -531,9 +529,9 @@ In this experiment, the execution time of the our distributed optimization appro
 \label{Figures/ch4/R1/T}
 \end{figure} 
 
 \label{Figures/ch4/R1/T}
 \end{figure} 
 

-We can see from figure~\ref{Figures/ch4/R1/T}, that the DiLCO-32 has very low execution times in comparison with other DiLCO versions because it distributed on larger number of small subregions.  Conversely, DiLCO-2 requires to solve an optimization problem considering half the nodes in each subregion presents high execution times.
+We can see from figure~\ref{Figures/ch4/R1/T}, that the DiLCO-32 has very low execution times in comparison with other DiLCO versions because it is distributed on larger number of small subregions.  Conversely, DiLCO-2 requires to solve an optimization problem considering half the nodes in each subregion presents high execution times.

 
 

-The DiLCO-32 protocol has more suitable times at the same time it turns on redundant nodes more.  We think that in distributed fashion the solving of the  optimization problem in a subregion can be tackled by sensor nodes. Overall, to be able to deal with very large networks,  a distributed method is clearly required.
+We think that in distributed fashion the solving of the  optimization problem in a subregion can be tackled by sensor nodes. Overall, to be able to deal with very large networks,  a distributed method is clearly required.

 
 \item {{\bf The Network Lifetime}}
 %\subsubsection{The Network Lifetime}
 
 \item {{\bf The Network Lifetime}}
 %\subsubsection{The Network Lifetime}


@@ -545,12 +543,11 @@ In figure~\ref{Figures/ch4/R1/LT95} and \ref{Figures/ch4/R1/LT50}, network lifet
 \caption{Network Lifetime for $Lifetime95$}
 \label{Figures/ch4/R1/LT95}
 \end{figure} 
 \caption{Network Lifetime for $Lifetime95$}
 \label{Figures/ch4/R1/LT95}
 \end{figure} 

-We see that DiLCO-2 protocol results in execution times that quickly become unsuitable for a sensor network, as well as the energy consumed during the communication, seems to be huge because it is distributed over only two subregions.
+For DiLCO-2 protocol results, execution times quickly become unsuitable for a sensor network, and the energy consumed during the communication, seems to be huge because it is distributed over only two subregions.

 
 

-As highlighted by figures~\ref{Figures/ch4/R1/LT95} and \ref{Figures/ch4/R1/LT50}, the network lifetime obviously increases when the size of the network increases, with DiLCO-16 protocol that leads to the larger lifetime improvement. By choosing the best-suited nodes, for each round, to cover the area of interest and by
-letting the other ones sleep in order to be used later in next rounds, DiLCO-16 protocol efficiently extends the network lifetime because the benefit from the optimization with 16 subregions is better than DiLCO-32 protocol with 32 subregions. DilCO-32 protocol puts in active mode a larger number of sensor nodes especially near the borders of the subdivisions.
+As highlighted by figures~\ref{Figures/ch4/R1/LT95} and \ref{Figures/ch4/R1/LT50}, the network lifetime obviously increases when the size of the network increases. DiLCO-16 protocol leads to the larger lifetime improvement. DiLCO-16 protocol efficiently extends the network lifetime because the benefit from the optimization with 16 subregions is better than DiLCO-32 protocol with 32 subregions. in fact, DilCO-32 protocol puts in active mode a larger number of sensor nodes especially near the borders of the subdivisions.

 
 

-Comparison shows that DiLCO-16 protocol, which uses 16 leaders, is the best one because it is used less number of active nodes during the network lifetime compared with DiLCO-32 protocol. It also means that distributing the protocol 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 DiLCO-16 protocol, which uses 16 leaders, is the best one because it uses less number of active nodes during the network lifetime compared with DiLCO-32 protocol. It also means that distributing the protocol in each node and subdividing the sensing field into many subregions, which are managed independently and simultaneously, is a relevant way to maximize the lifetime of a network.

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


@@ -561,19 +558,19 @@ Comparison shows that DiLCO-16 protocol, which uses 16 leaders, is the best one
 
 \end{enumerate}
 
 
 \end{enumerate}
 

-\subsection{Performance Analysis for Primary Point Models}
+\subsection{Performance Analysis for Different Number of Primary Points}

 \label{ch4:sec:04:06}
 
 \label{ch4:sec:04:06}
 

-In this section, we are studied the performance of DiLCO~16 approach for a different primary point models. The objective of this comparison is to select the suitable primary point model to be used by DiLCO protocol. 
+In this section, we study the performance of DiLCO~16 approach for a different number of primary points. The objective of this comparison is to select the suitable primary point model to be used by DiLCO protocol. 

 
 

-In this comparisons, DiLCO-16 protocol are used with five models which are called Model~1( With 5 Primary Points), Model~2 ( With 9 Primary Points), Model~3 ( With 13 Primary Points), Model~4 ( With 17 Primary Points), and Model~5 ( With 21 Primary Points). 
+In this comparison, DiLCO-16 protocol is used with five models which are called Model-5( With 5 Primary Points), Model-9 ( With 9 Primary Points), Model-13 ( With 13 Primary Points), Model-17 ( With 17 Primary Points), and Model-21 ( With 21 Primary Points). 

 
 
 \begin{enumerate}[i)]
 
 \item {{\bf Coverage Ratio}}
 %\subsubsection{Coverage Ratio} 
 
 
 \begin{enumerate}[i)]
 
 \item {{\bf Coverage Ratio}}
 %\subsubsection{Coverage Ratio} 

-In this experiment, we Figure~\ref{Figures/ch4/R2/CR} shows the average coverage ratio for 150 deployed nodes.  
+Figure~\ref{Figures/ch4/R2/CR} shows the average coverage ratio for 150 deployed nodes.  

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


@@ -582,8 +579,8 @@ In this experiment, we Figure~\ref{Figures/ch4/R2/CR} shows the average coverage
 \label{Figures/ch4/R2/CR}
 \end{figure} 
 
 \label{Figures/ch4/R2/CR}
 \end{figure} 
 

-It is shown that all models provide a very near coverage ratios during the network lifetime, with very small superiority for the models with higher number of primary points. Moreover, when the number of rounds increases, coverage ratio produced by Model~3, Model~4, and Model~5 decreases in comparison with Model~1 and Model~2 due to the high energy consumption during the listening to take the decision after finishing optimization process for larger number of primary points. As shown in figure ~\ref{Figures/ch4/R2/CR}, Coverage ratio decreases when the number of rounds increases due to dead nodes. Although  some nodes are dead,
-thanks to  Model~2, which is slightly more efficient than other Models, because it is balanced between the number of rounds and the better coverage ratio in comparison with other Models.
+It is shown that all models provide a very near coverage ratios during the network lifetime, with a very small superiority for the models with higher number of primary points. Moreover, when the number of rounds increases, coverage ratio produced by Model-13, Model-17, and Model-21 decreases in comparison with Model-5 and Model-9 due to a larger time computation for the decision process for larger number of primary points. 
+As shown in figure ~\ref{Figures/ch4/R2/CR}, Coverage ratio decreases when the number of rounds increases due to dead nodes. Model-9 is slightly more efficient than other models, because it is balanced between the number of rounds and the better coverage ratio in comparison with other Models.

 
 \item {{\bf Active Sensors Ratio}}
 %\subsubsection{Active Sensors Ratio} 
 
 \item {{\bf Active Sensors Ratio}}
 %\subsubsection{Active Sensors Ratio} 


@@ -595,12 +592,12 @@ thanks to  Model~2, which is slightly more efficient than other Models, because
 \label{Figures/ch4/R2/ASR}
 \end{figure} 
 
 \label{Figures/ch4/R2/ASR}
 \end{figure} 
 

-The results presented in figure~\ref{Figures/ch4/R2/ASR} show the superiority of the proposed  Model 1, in comparison with the other Models. The model with fewer number of primary points uses fewer active nodes than the other models, which uses larger number of primary points to represent the area of the sensor. According to the results that presented in figure~\ref{Figures/ch4/R2/CR}, we observe that although the Model~1 continue to a larger number of rounds, but it has less coverage ratio compared with other models. The advantage of the Model~2 approach is to use fewer number of active nodes for each round compared with Model~3,  Model~4, and Model~5. This led to continuing for a larger number of rounds with extending the network lifetime. Model~2 has a better coverage ratio compared to Model~1 and acceptable number of rounds.
+The results presented in figure~\ref{Figures/ch4/R2/ASR} show the superiority of the proposed  Model-5, in comparison with the other models. The model with fewer number of primary points uses fewer active nodes than the other models. According to the results presented in figure~\ref{Figures/ch4/R2/CR}, we observe that although the Model-5 continue to a larger number of rounds, but it has less coverage ratio compared with other models. The advantage of the Model-9 approach is to use fewer number of active nodes for each round compared with Model-13,  Model-17, and Model-21. This led to continuing for a larger number of rounds with extending the network lifetime. Model-9 has a better coverage ratio compared to Model-5 and acceptable number of rounds.

 
 
 
 

-\item {{\bf he percentage of stopped simulation runs}}
+\item {{\bf The percentage of stopped simulation runs}}

 %\subsubsection{The percentage of stopped simulation runs}
 %\subsubsection{The percentage of stopped simulation runs}

-In this study, we want to show the effect of increasing the primary points on the number of stopped simulation runs for each round. Figure~\ref{Figures/ch4/R2/SR} illustrates the percentage of stopped simulation runs per round for 150 deployed nodes. 
+Figure~\ref{Figures/ch4/R2/SR} illustrates the percentage of stopped simulation runs per round for 150 deployed nodes. 

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


@@ -609,7 +606,7 @@ In this study, we want to show the effect of increasing the primary points on th
 \label{Figures/ch4/R2/SR}
 \end{figure} 
 
 \label{Figures/ch4/R2/SR}
 \end{figure} 
 

-As shown in Figure~\ref{Figures/ch4/R2/SR}, when the number of primary points is increased, the percentage of the stopped simulation runs per round is increased. The reason behind the increase is the increase in the sensors dead when the primary points increase. We are observed that the Model~1 is a better than other models because it conserve more energy by turn on less number of sensors during the sensing phase, but in the same time it preserve the coverage with a less coverage ratio in comparison with other models. Model~2 seems to be more suitable to be used in wireless sensor networks.
+When the number of primary points is increased, the percentage of the stopped simulation runs per round is increased. The reason behind the increase is the increase in the sensors dead when the primary points increase. Model-5 is better than other models because it conserve more energy by turn on less number of sensors during the sensing phase, but in the same time it preserve the coverage with a less coverage ratio in comparison with other models. Model~2 seems to be more suitable to be used in wireless sensor networks.

 
 
 \item {{\bf The Energy Consumption}}
 
 
 \item {{\bf The Energy Consumption}}


@@ -629,7 +626,7 @@ In this experiment, we study the effect of increasing the primary points to repr
 \label{Figures/ch4/R2/EC50}
 \end{figure} 
 
 \label{Figures/ch4/R2/EC50}
 \end{figure} 
 

- We see from the results presented in Figures~\ref{Figures/ch4/R2/EC95} and \ref{Figures/ch4/R2/EC50}, The energy consumed by the network for each round increases when the primary points increases, because the decision for the optimization process requires more time, which leads to consuming more energy during the listening mode. The results show that Model~1 is the most competitive from the energy consumption point of view, but the worst one from coverage ratio point of view. The other Models have a high energy consumption  due to the increase in the primary points, which are led to increase the energy consumption during the listening mode before producing the solution by solving the optimization process. In fact, we see that Model~2 is a good candidate to be used by wireless sensor network because it preserves a good coverage ratio with a suitable energy consumption in comparison with other models. 
+ We see from the results presented in Figures~\ref{Figures/ch4/R2/EC95} and \ref{Figures/ch4/R2/EC50}, The energy consumed by the network for each round increases when the primary points increases, because the decision for the optimization process requires more time, which leads to consuming more energy during the listening mode. The results show that Model-5 is the most competitive from the energy consumption point of view, but the worst one from coverage ratio point of view. The other models have a high energy consumption  due to the increase in the primary points, which are led to increase the energy consumption during the listening mode before producing the solution by solving the optimization process. In fact, Model-9 is a good candidate to be used by wireless sensor network because it preserves a good coverage ratio with a suitable energy consumption in comparison with other models. 

 
 \item {{\bf Execution Time}}
 %\subsubsection{Execution Time}
 
 \item {{\bf Execution Time}}
 %\subsubsection{Execution Time}


@@ -642,12 +639,12 @@ In this experiment, we have studied the impact of the increase in primary points
 \label{Figures/ch4/R2/T}
 \end{figure} 
 
 \label{Figures/ch4/R2/T}
 \end{figure} 
 

-They are given for the different primary point models and various numbers of sensors. We can see from Figure~\ref{Figures/ch4/R2/T}, that Model~1 has lower execution time in comparison with other Models because it used smaller number of primary points to represent the area of the sensor.  Conversely, the other primary point models  have been presented  a higher execution times.
-Moreover, Model~2 has more suitable times and coverage ratio that lead to continue for a larger number of rounds extending the network lifetime. We  think that a good primary point model, this one that balances between the coverage ratio and the number of rounds during the lifetime of the network.
+They are given for the different primary point models and various numbers of sensors. We can see from Figure~\ref{Figures/ch4/R2/T}, that Model-5 has lower execution time in comparison with other models because it used smaller number of primary points to represent the area of the sensor.  Conversely, the other primary point models  have been presented  a higher execution times.
+Moreover, Model-9 has more suitable times and coverage ratio that lead to continue for a larger number of rounds extending the network lifetime. We  think that a good primary point model, this one that balances between the coverage ratio and the number of rounds during the lifetime of the network.

 
 \item {{\bf The Network Lifetime}}
 %\subsubsection{The Network Lifetime}
 
 \item {{\bf The Network Lifetime}}
 %\subsubsection{The Network Lifetime}

-Finally, we will study the effect of increasing the primary points on the lifetime of the network. In Figure~\ref{Figures/ch4/R2/LT95} and in Figure~\ref{Figures/ch4/R2/LT50}, network lifetime, $Lifetime95$ and $Lifetime50$ respectively, are illustrated for different network sizes. 
+Finally, we study the effect of increasing the primary points on the lifetime of the network. In Figure~\ref{Figures/ch4/R2/LT95} and in Figure~\ref{Figures/ch4/R2/LT50}, network lifetime, $Lifetime95$ and $Lifetime50$ respectively, are illustrated for different network sizes. 

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


@@ -665,20 +662,20 @@ Finally, we will study the effect of increasing the primary points on the lifeti
 \end{figure} 
 
 
 \end{figure} 
 
 

-As highlighted by figures~\ref{Figures/ch4/R2/LT95} and \ref{Figures/ch4/R2/LT50}, the network lifetime obviously increases when the size of the network increases, with  Model~1 that leads to the larger lifetime improvement.
-Comparison shows that the Model~1, which uses less number of primary points, is the best one because it is less energy consumption during the network lifetime. It is also the worst one from the point of view of coverage ratio. Our proposed Model~2 efficiently prolongs the network lifetime with a good coverage ratio in comparison with other models.
+As highlighted by figures~\ref{Figures/ch4/R2/LT95} and \ref{Figures/ch4/R2/LT50}, the network lifetime obviously increases when the size of the network increases, with  Model-5 that leads to the larger lifetime improvement.
+Comparison shows that the Model-5, which uses less number of primary points, is the best one because it is less energy consumption during the network lifetime. It is also the worst one from the point of view of coverage ratio. Our proposed Model-9 efficiently prolongs the network lifetime with a good coverage ratio in comparison with other models.

  
 \end{enumerate}
 
 \subsection{Performance Comparison with other Approaches}
 \label{ch4:sec:04:07}
  
 \end{enumerate}
 
 \subsection{Performance Comparison with other Approaches}
 \label{ch4:sec:04:07}

-Based on the results, which are conducted from previous two subsections, \ref{ch4:sec:04:02} and \ref{ch4:sec:04:03}, we have found that DiLCO-16 protocol and DiLCO-32 protocol with Model~2 are the best candidates to be compared with other two approaches. The first approach is called DESK~\cite{DESK}, which is a fully distributed coverage algorithm. The second approach is called GAF~\cite{GAF}, consists in dividing the region into fixed squares.   During the decision phase, in each square, one sensor is chosen to remain on during the sensing phase time. 
+Based on the results, conducted in the previous subsections, \ref{ch4:sec:04:02} and \ref{ch4:sec:04:03}, DiLCO-16 protocol and DiLCO-32 protocol with Model-9 seems to be the best candidates to be compared with other two approaches. The first approach is called DESK~\cite{DESK}, which is a fully distributed coverage algorithm. The second approach called GAF~\cite{GAF}, consists in dividing the region into fixed squares.   During the decision phase, in each square, one sensor is chosen to remain on during the sensing phase time. 

 
 \begin{enumerate}[i)]
 
 \item {{\bf Coverage Ratio}}
 %\subsubsection{Coverage Ratio} 
 
 \begin{enumerate}[i)]
 
 \item {{\bf Coverage Ratio}}
 %\subsubsection{Coverage Ratio} 

-In this experiment, the average coverage ratio for 150 deployed nodes has been demonstrated figure~\ref{Figures/ch4/R3/CR}. 
+The average coverage ratio for 150 deployed nodes is demonstrated in Figure~\ref{Figures/ch4/R3/CR}. 

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

diff --git a/CHAPITRE_05.tex b/CHAPITRE_05.tex
index 91014fb9ba229257a060435a5450f0267123a523..714ecd7520b60575188f9d1963b297c21dc14cec 100644 (file)
--- a/CHAPITRE_05.tex
+++ b/CHAPITRE_05.tex
@@ -90,7 +90,7 @@ The  energy consumption  and some other constraints  can easily  be  taken into
   \BlankLine
   %\emph{Initialize the sensor node and determine it's position and subregion} \; 
   
   \BlankLine
   %\emph{Initialize the sensor node and determine it's position and subregion} \; 
   

-  \If{ $RE_j \geq E_{R}$ }{
+  \If{ $RE_j \geq E_{th}$ }{

       \emph{$s_j.status$ = COMMUNICATION}\;
       \emph{Send $INFO()$ packet to other nodes in the subregion}\;
       \emph{Wait $INFO()$ packet from other nodes in the subregion}\; 
       \emph{$s_j.status$ = COMMUNICATION}\;
       \emph{Send $INFO()$ packet to other nodes in the subregion}\;
       \emph{Wait $INFO()$ packet from other nodes in the subregion}\; 


@@ -199,7 +199,7 @@ Subject to
 \end{equation}
 
 \begin{equation}
 \end{equation}
 
 \begin{equation}

-  \sum_{t=1}^{T}  X_{t,j}   \leq  \lfloor {RE_{j}/E_{R}} \rfloor \hspace{6 mm} \forall j \in J, t = 1,\dots,T
+  \sum_{t=1}^{T}  X_{t,j}   \leq  \lfloor {RE_{j}/E_{th}} \rfloor \hspace{6 mm} \forall j \in J, t = 1,\dots,T

   \label{eq144} 
 \end{equation}
 
   \label{eq144} 
 \end{equation}
 


@@ -232,7 +232,7 @@ covered by at least  one sensor and, if it is not  always the case, overcoverage
 and undercoverage  variables help balancing the restriction  equations by taking
 positive values. The constraint  given by equation~(\ref{eq144}) guarantees that
 the sensor has enough energy ($RE_j$  corresponds to its remaining energy) to be
 and undercoverage  variables help balancing the restriction  equations by taking
 positive values. The constraint  given by equation~(\ref{eq144}) guarantees that
 the sensor has enough energy ($RE_j$  corresponds to its remaining energy) to be

-alive during  the selected rounds knowing  that $E_{R}$ is the  amount of energy
+alive during  the selected rounds knowing  that $E_{th}$ is the  amount of energy

 required to be alive during one round.
 
 There  are two main  objectives.  First,  we limit  the overcoverage  of primary
 required to be alive during one round.
 
 There  are two main  objectives.  First,  we limit  the overcoverage  of primary


@@ -290,7 +290,7 @@ Network size &  50, 100, 150, 200 and 250~nodes   \\
 Initial energy  & 500-700~joules  \\  
 %\hline
 Sensing time for one round & 60 Minutes \\
 Initial energy  & 500-700~joules  \\  
 %\hline
 Sensing time for one round & 60 Minutes \\

-$E_{R}$ & 36 Joules\\
+$E_{th}$ & 36 Joules\\

 $R_s$ & 5~m   \\     
 %\hline
 $W_{\Theta}$ & 1   \\
 $R_s$ & 5~m   \\     
 %\hline
 $W_{\Theta}$ & 1   \\


@@ -317,7 +317,7 @@ it. Therefore, we have set the number of subregions to 16 rather than 32.
 
 We used the modeling language and the optimization solver which are mentioned in chapter 4, section \ref{ch4:sec:04:02}. In addition, we employed an energy consumption model, which is presented in chapter 4, section \ref{ch4:sec:04:03}. 
 
 
 We used the modeling language and the optimization solver which are mentioned in chapter 4, section \ref{ch4:sec:04:02}. In addition, we employed an energy consumption model, which is presented in chapter 4, section \ref{ch4:sec:04:03}. 
 

-%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 less than  $E_{R}=36~\mbox{Joules}$, the minimum  energy needed for the  node to stay alive  during one round.  This value has  been computed by  multiplying the energy consumed in  active state (9.72 mW) by the time in second  for one round (3600 seconds). According to the  interval of initial energy, a sensor may be alive during at most 20 rounds.
+%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 less than  $E_{th}=36~\mbox{Joules}$, the minimum  energy needed for the  node to stay alive  during one round.  This value has  been computed by  multiplying the energy consumed in  active state (9.72 mW) by the time in second  for one round (3600 seconds). According to the  interval of initial energy, a sensor may be alive during at most 20 rounds.

 
 \subsection{Metrics}
 \label{ch5:sec:04:02}
 
 \subsection{Metrics}
 \label{ch5:sec:04:02}

diff --git a/Thesis.toc b/Thesis.toc
index a915a0c415d08099a9793f8a5081dbf34a41be2a..4da90a805c2588235f09b6a2855396c09059e590 100644 (file)
--- a/Thesis.toc
+++ b/Thesis.toc
@@ -54,7 +54,7 @@
 \contentsline {section}{\numberline {3.3}Optimization Solvers}{68}{section.3.3}
 \contentsline {section}{\numberline {3.4}Conclusion}{71}{section.3.4}
 \contentsline {part}{II\hspace {1em}Contributions}{73}{part.2}
 \contentsline {section}{\numberline {3.3}Optimization Solvers}{68}{section.3.3}
 \contentsline {section}{\numberline {3.4}Conclusion}{71}{section.3.4}
 \contentsline {part}{II\hspace {1em}Contributions}{73}{part.2}

-\contentsline {chapter}{\numberline {4}Distributed Lifetime Coverage Optimization Protocol in WSNs}{75}{chapter.4}
+\contentsline {chapter}{\numberline {4}Distributed Lifetime Coverage Optimization Protocol in Wireless Sensor Networks}{75}{chapter.4}

 \contentsline {section}{\numberline {4.1}Introduction}{75}{section.4.1}
 \contentsline {section}{\numberline {4.2}Description of the DiLCO Protocol}{76}{section.4.2}
 \contentsline {subsection}{\numberline {4.2.1}Assumptions and Network Model}{76}{subsection.4.2.1}
 \contentsline {section}{\numberline {4.1}Introduction}{75}{section.4.1}
 \contentsline {section}{\numberline {4.2}Description of the DiLCO Protocol}{76}{section.4.2}
 \contentsline {subsection}{\numberline {4.2.1}Assumptions and Network Model}{76}{subsection.4.2.1}


@@ -70,7 +70,7 @@
 \contentsline {subsection}{\numberline {4.4.2}Modeling Language and Optimization Solver}{83}{subsection.4.4.2}
 \contentsline {subsection}{\numberline {4.4.3}Energy Consumption Model}{83}{subsection.4.4.3}
 \contentsline {subsection}{\numberline {4.4.4}Performance Metrics}{84}{subsection.4.4.4}
 \contentsline {subsection}{\numberline {4.4.2}Modeling Language and Optimization Solver}{83}{subsection.4.4.2}
 \contentsline {subsection}{\numberline {4.4.3}Energy Consumption Model}{83}{subsection.4.4.3}
 \contentsline {subsection}{\numberline {4.4.4}Performance Metrics}{84}{subsection.4.4.4}

-\contentsline {subsection}{\numberline {4.4.5}Performance Analysis for Different Subregions}{85}{subsection.4.4.5}
+\contentsline {subsection}{\numberline {4.4.5}Performance Analysis for Different Number of Subregions}{85}{subsection.4.4.5}

 \contentsline {subsection}{\numberline {4.4.6}Performance Analysis for Primary Point Models}{90}{subsection.4.4.6}
 \contentsline {subsection}{\numberline {4.4.7}Performance Comparison with other Approaches}{95}{subsection.4.4.7}
 \contentsline {section}{\numberline {4.5}Conclusion}{102}{section.4.5}
 \contentsline {subsection}{\numberline {4.4.6}Performance Analysis for Primary Point Models}{90}{subsection.4.4.6}
 \contentsline {subsection}{\numberline {4.4.7}Performance Comparison with other Approaches}{95}{subsection.4.4.7}
 \contentsline {section}{\numberline {4.5}Conclusion}{102}{section.4.5}