Update by Ali

[ThesisAli.git] / CHAPITRE_04.tex

diff --git a/CHAPITRE_04.tex b/CHAPITRE_04.tex
old mode 100644 (file)
new mode 100755 (executable)
index ac6a480..603c6dd
--- a/CHAPITRE_04.tex
+++ b/CHAPITRE_04.tex
@@ -30,21 +30,15 @@ prolong the network lifetime and improve the coverage performance effectively.
 \section{Description of the DiLCO Protocol}
 \label{ch4:sec:02}
 
 \section{Description of the DiLCO Protocol}
 \label{ch4:sec:02}
 

-\noindent In this section, we introduce the DiLCO protocol which is distributed on  each subregion  in  the area  of interest.   It  is based  on two  efficient
-techniques: network leader election and sensor activity scheduling for coverage preservation  and  energy  conservation,  applied  periodically  to  efficiently
-maximize the lifetime in the network.
+\noindent In this section, we introduce the DiLCO protocol which is distributed on  each subregion in the area of interest. It is based  on two  efficient
+techniques: network leader election and sensor activity scheduling for coverage preservation and  energy conservation, applied periodically to efficiently maximize the lifetime in the network.

 
 \subsection{Assumptions and Network Model}
 \label{ch4:sec:02:01}
 
 \subsection{Assumptions and Network Model}
 \label{ch4:sec:02:01}

-\noindent  We consider  a sensor  network composed  of static  nodes distributed independently and uniformly at random.  A high density deployment ensures a high
-coverage ratio of the interested area at the start. The nodes are supposed to have homogeneous characteristics from a communication and a processing point of
-view, whereas they  have heterogeneous energy provisions.  Each  node has access to its location thanks,  either to a hardware component (like a  GPS unit), or a
-location discovery algorithm. Furthermore, we assume that sensor nodes are time synchronized in order to properly coordinate their operations to achieve complex sensing tasks~\cite{ref157}. The two sensor nodes have been supposed a neighbors if the euclidean distance between them is at most equal to 2$R_s$. 
+\noindent  We consider a sensor  network composed  of static  nodes distributed independently and uniformly at random.  A high-density deployment ensures a high coverage ratio of the interested area at the start. The nodes are supposed to have homogeneous characteristics from a communication and a processing point of view, whereas they  have heterogeneous energy provisions.  Each  node has access to its location thanks,  either to a hardware component (like a  GPS unit) or a location discovery algorithm. Furthermore, we assume that sensor nodes are time synchronized in order to properly coordinate their operations to achieve complex sensing tasks~\cite{ref157}. The two sensor nodes have been supposed neighbors if the euclidean distance between them is at most equal to 2$R_s$. 

  
 
  
 

-\indent We consider a boolean disk coverage model which is the most widely used sensor coverage  model in the  literature. Thus, since  a sensor has  a constant
-sensing range $R_s$, every space points  within a disk centered at a sensor with the radius of  the sensing range is said  to be covered by this  sensor. We also
-assume  that  the communication  range $R_c$ is at least twice the sensing range $R_s$ (i.e., $R_c \geq  2R_s$). In  fact, Zhang and Hou~\cite{ref126} proved  that if the transmission range  fulfills the previous hypothesis, a complete coverage of  a convex area implies connectivity among the working nodes in the active mode. We assume that each sensor node can directly transmit its measurements to a mobile sink node. For example, a sink can be an unmanned aerial vehicle (UAV) is flying regularly over the sensor field to collect measurements from sensor nodes. A mobile sink node collects the measurements and transmits them to the base station.
+\indent We consider a boolean disk coverage model which is the most widely used sensor coverage  model in the  literature. Thus, since  a sensor has a constant sensing range $R_s$, every space points within a disk centered at a sensor with the radius of the sensing range is said to be covered with this sensor. We also assume  that  the communication  range $R_c$ is at least twice the sensing range $R_s$ (i.e., $R_c \geq  2R_s$). In  fact, Zhang and Hou~\cite{ref126} proved  that if the transmission range  fulfills the previous hypothesis, a complete coverage of  a convex area implies connectivity among the working nodes in the active mode. We assume that each sensor node can directly transmit its measurements toward a mobile sink node. For example, a sink can be an unmanned aerial vehicle (UAV) is flying regularly over the sensor field to collect measurements from sensor nodes. A mobile sink node collects the measurements and transmits them to the base station.

 
 During the execution of the DiLCO protocol, two kinds of packet will be used:
 
 
 During the execution of the DiLCO protocol, two kinds of packet will be used:
 


@@ -65,19 +59,12 @@ There are five possible status for each sensor node in the network:
 
 \subsection{Primary Point Coverage Model}
 \label{ch4:sec:02:02}
 
 \subsection{Primary Point Coverage Model}
 \label{ch4:sec:02:02}

-\indent Instead of working with the coverage area, we consider for each
-sensor a set of points called primary points. We also assume that the
-sensing disk defined by a sensor is covered if all the primary points of
-this sensor are covered. By  knowing the  position (point  center: ($p_x,p_y$))  of  a wireless
-sensor node  and its $R_s$,  we calculate the primary  points directly
-based on the proposed model. We  use these primary points (that can be
-increased or decreased if necessary)  as references to ensure that the
-monitored  region  of interest  is  covered  by  the selected  set  of
-sensors, instead of using all the points in the area.
+\indent Instead of working with the coverage area, we consider for each sensor a set of points called primary points. We also assume that the sensing disk defined by a sensor is covered if all the primary points of this sensor are covered. By  knowing the  position (point  center: ($p_x,p_y$))  of  a wireless sensor node  and it's $R_s$,  we calculate the primary  points directly based on the proposed model. We  use these primary points (that can be increased or decreased if necessary)  as references to ensure that the monitored  region  of interest  is  covered by the selected  set  of sensors, instead of using all the points in the area. 

 
 \indent  We can  calculate  the positions of the selected primary
 points in the circle disk of the sensing range of a wireless sensor
 node (see figure~\ref{fig1}) as follows:\\
 
 \indent  We can  calculate  the positions of the selected primary
 points in the circle disk of the sensing range of a wireless sensor
 node (see figure~\ref{fig1}) as follows:\\

+

 $(p_x,p_y)$ = point center of wireless sensor node\\  
 $X_1=(p_x,p_y)$ \\ 
 $X_2=( p_x + R_s * (1), p_y + R_s * (0) )$\\           
 $(p_x,p_y)$ = point center of wireless sensor node\\  
 $X_1=(p_x,p_y)$ \\ 
 $X_2=( p_x + R_s * (1), p_y + R_s * (0) )$\\           


@@ -125,13 +112,13 @@ $X_{25}=( p_x + R_s * (\frac{1}{2}), p_y + R_s * (\frac{-\sqrt{3}}{2})) $.
 
 \subsection{Main Idea}
 \label{ch4:sec:02:03}
 
 \subsection{Main Idea}
 \label{ch4:sec:02:03}

-\noindent We start  by applying a divide-and-conquer algorithm  to partition the
+\noindent We start by applying a divide-and-conquer algorithm  to partition the

 area of interest  into smaller areas called subregions and  then our protocol is
 executed   simultaneously  in   each   subregion.
 
 \begin{figure}[ht!]
 \centering
 area of interest  into smaller areas called subregions and  then our protocol is
 executed   simultaneously  in   each   subregion.
 
 \begin{figure}[ht!]
 \centering

-\includegraphics[scale=0.60]{Figures/ch4/FirstModel.pdf} % 70mm
+\includegraphics[scale=0.80]{Figures/ch4/FirstModel.pdf} % 70mm

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


@@ -388,7 +375,7 @@ The modeling  language for Mathematical Programming (AMPL)~\cite{AMPL} is  emplo
 \subsection{Energy Consumption Model}
 \label{ch4:sec:04:03}
 
 \subsection{Energy Consumption Model}
 \label{ch4:sec:04:03}
 

-\indent In this dissertation, we used an energy consumption model proposed by~\cite{ref111} and based on \cite{ref112} with slight  modifications.  The energy consumption for  sending/receiving the packets is added, whereas the  part related to the sensing range is removed because we consider a fixed sensing range.
+\indent In this dissertation, we used an energy consumption model proposed by~\cite{DESK} and based on \cite{ref112} with slight  modifications.  The energy consumption for  sending/receiving the packets is added, whereas the  part related to the sensing range is removed because we consider a fixed sensing range.

 
 \indent For our energy consumption model, we refer to the sensor node Medusa~II which uses an Atmels  AVR ATmega103L microcontroller~\cite{ref112}. The typical architecture  of a  sensor  is composed  of four  subsystems: the  MCU subsystem which is capable of computation, communication subsystem (radio) which is responsible  for transmitting/receiving messages, the  sensing subsystem that collects  data, and  the  power supply  which  powers the  complete sensor  node \cite{ref112}. 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{table1}.
 
 
 \indent For our energy consumption model, we refer to the sensor node Medusa~II which uses an Atmels  AVR ATmega103L microcontroller~\cite{ref112}. The typical architecture  of a  sensor  is composed  of four  subsystems: the  MCU subsystem which is capable of computation, communication subsystem (radio) which is responsible  for transmitting/receiving messages, the  sensing subsystem that collects  data, and  the  power supply  which  powers the  complete sensor  node \cite{ref112}. 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{table1}.
 


@@ -512,7 +499,7 @@ In this experiment, Figure~\ref{Figures/ch4/R1/CR} shows the average coverage ra
 \parskip 0pt    
 \begin{figure}[h!]
 \centering
 \parskip 0pt    
 \begin{figure}[h!]
 \centering

- \includegraphics[scale=0.6] {Figures/ch4/R1/CR.pdf} 
+ \includegraphics[scale=0.8] {Figures/ch4/R1/CR.pdf} 

 \caption{Coverage ratio for 150 deployed nodes}
 \label{Figures/ch4/R1/CR}
 \end{figure} 
 \caption{Coverage ratio for 150 deployed nodes}
 \label{Figures/ch4/R1/CR}
 \end{figure} 


@@ -525,7 +512,7 @@ As shown in the figure ~\ref{Figures/ch4/R1/CR}, as the number of subregions inc
  Figure~\ref{Figures/ch4/R1/ASR} shows the average active nodes ratio for 150 deployed nodes.
 \begin{figure}[h!]
 \centering
  Figure~\ref{Figures/ch4/R1/ASR} shows the average active nodes ratio for 150 deployed nodes.
 \begin{figure}[h!]
 \centering

-\includegraphics[scale=0.6]{Figures/ch4/R1/ASR.pdf}  
+\includegraphics[scale=0.8]{Figures/ch4/R1/ASR.pdf}  

 \caption{Active sensors ratio for 150 deployed nodes }
 \label{Figures/ch4/R1/ASR}
 \end{figure} 
 \caption{Active sensors ratio for 150 deployed nodes }
 \label{Figures/ch4/R1/ASR}
 \end{figure} 


@@ -536,7 +523,7 @@ The results presented in figure~\ref{Figures/ch4/R1/ASR} show the increase in th
 Figure~\ref{Figures/ch4/R1/SR} illustrates the percentage of stopped simulation runs per round for 150 deployed nodes. 
 \begin{figure}[h!]
 \centering
 Figure~\ref{Figures/ch4/R1/SR} illustrates the percentage of stopped simulation runs per round for 150 deployed nodes. 
 \begin{figure}[h!]
 \centering

-\includegraphics[scale=0.6]{Figures/ch4/R1/SR.pdf} 
+\includegraphics[scale=0.8]{Figures/ch4/R1/SR.pdf} 

 \caption{Percentage of stopped simulation runs for 150 deployed nodes }
 \label{Figures/ch4/R1/SR}
 \end{figure} 
 \caption{Percentage of stopped simulation runs for 150 deployed nodes }
 \label{Figures/ch4/R1/SR}
 \end{figure} 


@@ -550,7 +537,7 @@ We measure the energy consumed by the sensors during the communication, listenin
 
 \begin{figure}[h!]
 \centering
 
 \begin{figure}[h!]
 \centering

-\includegraphics[scale=0.6]{Figures/ch4/R1/EC95.pdf} 
+\includegraphics[scale=0.8]{Figures/ch4/R1/EC95.pdf} 

 \caption{Energy Consumption for Lifetime95}
 \label{Figures/ch4/R1/EC95}
 \end{figure} 
 \caption{Energy Consumption for Lifetime95}
 \label{Figures/ch4/R1/EC95}
 \end{figure} 


@@ -560,7 +547,7 @@ The results show that DiLCO-16 and DiLCO-32 are the most competitive from the en
 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!]
 \centering
 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!]
 \centering

-\includegraphics[scale=0.6]{Figures/ch4/R1/EC50.pdf} 
+\includegraphics[scale=0.8]{Figures/ch4/R1/EC50.pdf} 

 \caption{Energy Consumption for Lifetime50}
 \label{Figures/ch4/R1/EC50}
 \end{figure} 
 \caption{Energy Consumption for Lifetime50}
 \label{Figures/ch4/R1/EC50}
 \end{figure} 


@@ -573,7 +560,7 @@ In this experiment, the execution time of the our distributed optimization appro
 
 \begin{figure}[h!]
 \centering
 
 \begin{figure}[h!]
 \centering

-\includegraphics[scale=0.6]{Figures/ch4/R1/T.pdf}  
+\includegraphics[scale=0.8]{Figures/ch4/R1/T.pdf}  

 \caption{Execution Time (in seconds)}
 \label{Figures/ch4/R1/T}
 \end{figure} 
 \caption{Execution Time (in seconds)}
 \label{Figures/ch4/R1/T}
 \end{figure} 


@@ -588,7 +575,7 @@ In figure~\ref{Figures/ch4/R1/LT95} and \ref{Figures/ch4/R1/LT50}, network lifet
 
 \begin{figure}[h!]
 \centering
 
 \begin{figure}[h!]
 \centering

-\includegraphics[scale=0.6]{Figures/ch4/R1/LT95.pdf}  
+\includegraphics[scale=0.8]{Figures/ch4/R1/LT95.pdf}  

 \caption{Network Lifetime for $Lifetime95$}
 \label{Figures/ch4/R1/LT95}
 \end{figure} 
 \caption{Network Lifetime for $Lifetime95$}
 \label{Figures/ch4/R1/LT95}
 \end{figure} 


@@ -600,7 +587,7 @@ letting the other ones sleep in order to be used later in next rounds, DiLCO-16
 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.
 \begin{figure}[h!]
 \centering
 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.
 \begin{figure}[h!]
 \centering

-\includegraphics[scale=0.6]{Figures/ch4/R1/LT50.pdf}  
+\includegraphics[scale=0.8]{Figures/ch4/R1/LT50.pdf}  

 \caption{Network Lifetime for $Lifetime50$}
 \label{Figures/ch4/R1/LT50}
 \end{figure} 
 \caption{Network Lifetime for $Lifetime50$}
 \label{Figures/ch4/R1/LT50}
 \end{figure} 


@@ -623,7 +610,7 @@ In this experiment, we Figure~\ref{Figures/ch4/R2/CR} shows the average coverage
 \parskip 0pt    
 \begin{figure}[h!]
 \centering
 \parskip 0pt    
 \begin{figure}[h!]
 \centering

- \includegraphics[scale=0.6] {Figures/ch4/R2/CR.pdf} 
+ \includegraphics[scale=0.8] {Figures/ch4/R2/CR.pdf} 

 \caption{Coverage ratio for 150 deployed nodes}
 \label{Figures/ch4/R2/CR}
 \end{figure} 
 \caption{Coverage ratio for 150 deployed nodes}
 \label{Figures/ch4/R2/CR}
 \end{figure} 


@@ -635,7 +622,7 @@ thanks to  Model~2, which is slightly more efficient than other Models, because
  Figure~\ref{Figures/ch4/R2/ASR} shows the average active nodes ratio for 150 deployed nodes.
 \begin{figure}[h!]
 \centering
  Figure~\ref{Figures/ch4/R2/ASR} shows the average active nodes ratio for 150 deployed nodes.
 \begin{figure}[h!]
 \centering

-\includegraphics[scale=0.6]{Figures/ch4/R2/ASR.pdf}  
+\includegraphics[scale=0.8]{Figures/ch4/R2/ASR.pdf}  

 \caption{Active sensors ratio for 150 deployed nodes }
 \label{Figures/ch4/R2/ASR}
 \end{figure} 
 \caption{Active sensors ratio for 150 deployed nodes }
 \label{Figures/ch4/R2/ASR}
 \end{figure} 


@@ -650,7 +637,7 @@ In this study, we want to show the effect of increasing the primary points on th
 
 \begin{figure}[h!]
 \centering
 
 \begin{figure}[h!]
 \centering

-\includegraphics[scale=0.6]{Figures/ch4/R2/SR.pdf} 
+\includegraphics[scale=0.8]{Figures/ch4/R2/SR.pdf} 

 \caption{Percentage of stopped simulation runs for 150 deployed nodes }
 \label{Figures/ch4/R2/SR}
 \end{figure} 
 \caption{Percentage of stopped simulation runs for 150 deployed nodes }
 \label{Figures/ch4/R2/SR}
 \end{figure} 


@@ -663,14 +650,14 @@ As shown in Figure~\ref{Figures/ch4/R2/SR}, when the number of primary points ar
 In this experiment, we study the effect of increasing the primary points to represent the area of the sensor on the energy consumed by the wireless sensor network for different network densities.  Figures~\ref{Figures/ch4/R2/EC95} and ~\ref{Figures/ch4/R2/EC50} illustrate the energy consumption for different network sizes for $Lifetime95$ and $Lifetime50$.
 \begin{figure}[h!]
 \centering
 In this experiment, we study the effect of increasing the primary points to represent the area of the sensor on the energy consumed by the wireless sensor network for different network densities.  Figures~\ref{Figures/ch4/R2/EC95} and ~\ref{Figures/ch4/R2/EC50} illustrate the energy consumption for different network sizes for $Lifetime95$ and $Lifetime50$.
 \begin{figure}[h!]
 \centering

-\includegraphics[scale=0.6]{Figures/ch4/R2/EC95.pdf} 
+\includegraphics[scale=0.8]{Figures/ch4/R2/EC95.pdf} 

 \caption{Energy Consumption with $95\%-Lifetime$}
 \label{Figures/ch4/R2/EC95}
 \end{figure} 
  
 \begin{figure}[h!]
 \centering
 \caption{Energy Consumption with $95\%-Lifetime$}
 \label{Figures/ch4/R2/EC95}
 \end{figure} 
  
 \begin{figure}[h!]
 \centering

-\includegraphics[scale=0.6]{Figures/ch4/R2/EC50.pdf} 
+\includegraphics[scale=0.8]{Figures/ch4/R2/EC50.pdf} 

 \caption{Energy Consumption with $Lifetime50$}
 \label{Figures/ch4/R2/EC50}
 \end{figure} 
 \caption{Energy Consumption with $Lifetime50$}
 \label{Figures/ch4/R2/EC50}
 \end{figure} 


@@ -683,7 +670,7 @@ In this experiment, we have studied the impact of the increase in primary points
 
 \begin{figure}[h!]
 \centering
 
 \begin{figure}[h!]
 \centering

-\includegraphics[scale=0.6]{Figures/ch4/R2/T.pdf}  
+\includegraphics[scale=0.8]{Figures/ch4/R2/T.pdf}  

 \caption{Execution Time(s) vs The Number of Sensors }
 \label{Figures/ch4/R2/T}
 \end{figure} 
 \caption{Execution Time(s) vs The Number of Sensors }
 \label{Figures/ch4/R2/T}
 \end{figure} 


@@ -697,7 +684,7 @@ Finally, we will study the effect of increasing the primary points on the lifeti
 
 \begin{figure}[h!]
 \centering
 
 \begin{figure}[h!]
 \centering

-\includegraphics[scale=0.6]{Figures/ch4/R2/LT95.pdf}  
+\includegraphics[scale=0.8]{Figures/ch4/R2/LT95.pdf}  

 \caption{Network Lifetime for $Lifetime95$}
 \label{Figures/ch4/R2/LT95}
 \end{figure} 
 \caption{Network Lifetime for $Lifetime95$}
 \label{Figures/ch4/R2/LT95}
 \end{figure} 


@@ -705,7 +692,7 @@ Finally, we will study the effect of increasing the primary points on the lifeti
 
 \begin{figure}[h!]
 \centering
 
 \begin{figure}[h!]
 \centering

-\includegraphics[scale=0.6]{Figures/ch4/R2/LT50.pdf}  
+\includegraphics[scale=0.8]{Figures/ch4/R2/LT50.pdf}  

 \caption{Network Lifetime for $Lifetime50$}
 \label{Figures/ch4/R2/LT50}
 \end{figure} 
 \caption{Network Lifetime for $Lifetime50$}
 \label{Figures/ch4/R2/LT50}
 \end{figure} 


@@ -729,7 +716,7 @@ In this experiment, the average coverage ratio for 150 deployed nodes has been d
 \parskip 0pt    
 \begin{figure}[h!]
 \centering
 \parskip 0pt    
 \begin{figure}[h!]
 \centering

- \includegraphics[scale=0.6] {Figures/ch4/R3/CR.pdf} 
+ \includegraphics[scale=0.8] {Figures/ch4/R3/CR.pdf} 

 \caption{Coverage ratio for 150 deployed nodes}
 \label{Figures/ch4/R3/CR}
 \end{figure} 
 \caption{Coverage ratio for 150 deployed nodes}
 \label{Figures/ch4/R3/CR}
 \end{figure} 


@@ -744,7 +731,7 @@ It is important to have as few active nodes as possible in each round, in  order
 
 \begin{figure}[h!]
 \centering
 
 \begin{figure}[h!]
 \centering

-\includegraphics[scale=0.6]{Figures/ch4/R3/ASR.pdf}  
+\includegraphics[scale=0.8]{Figures/ch4/R3/ASR.pdf}  

 \caption{Active sensors ratio for 150 deployed nodes }
 \label{Figures/ch4/R3/ASR}
 \end{figure} 
 \caption{Active sensors ratio for 150 deployed nodes }
 \label{Figures/ch4/R3/ASR}
 \end{figure} 


@@ -759,7 +746,7 @@ Figure~\ref{Figures/ch4/R3/SR} illustrates the percentage of stopped simulation
 runs per round for 150 deployed nodes. 
 \begin{figure}[h!]
 \centering
 runs per round for 150 deployed nodes. 
 \begin{figure}[h!]
 \centering

-\includegraphics[scale=0.6]{Figures/ch4/R3/SR.pdf} 
+\includegraphics[scale=0.8]{Figures/ch4/R3/SR.pdf} 

 \caption{Percentage of stopped simulation runs for 150 deployed nodes }
 \label{Figures/ch4/R3/SR}
 \end{figure} 
 \caption{Percentage of stopped simulation runs for 150 deployed nodes }
 \label{Figures/ch4/R3/SR}
 \end{figure} 


@@ -772,14 +759,14 @@ In this experiment, we have studied the effect of the energy consumed by the wir
 
 \begin{figure}[h!]
 \centering
 
 \begin{figure}[h!]
 \centering

-\includegraphics[scale=0.6]{Figures/ch4/R3/EC95.pdf} 
+\includegraphics[scale=0.8]{Figures/ch4/R3/EC95.pdf} 

 \caption{Energy Consumption with $95\%-Lifetime$}
 \label{Figures/ch4/R3/EC95}
 \end{figure} 
 
 \begin{figure}[h!]
 \centering
 \caption{Energy Consumption with $95\%-Lifetime$}
 \label{Figures/ch4/R3/EC95}
 \end{figure} 
 
 \begin{figure}[h!]
 \centering

-\includegraphics[scale=0.6]{Figures/ch4/R3/EC50.pdf} 
+\includegraphics[scale=0.8]{Figures/ch4/R3/EC50.pdf} 

 \caption{Energy Consumption with $Lifetime50$}
 \label{Figures/ch4/R3/EC50}
 \end{figure} 
 \caption{Energy Consumption with $Lifetime50$}
 \label{Figures/ch4/R3/EC50}
 \end{figure} 


@@ -793,7 +780,7 @@ In this experiment, we have observed the superiority of DiLCO-16 protocol and Di
 
 \begin{figure}[h!]
 \centering
 
 \begin{figure}[h!]
 \centering

-\includegraphics[scale=0.6]{Figures/ch4/R3/LT95.pdf}  
+\includegraphics[scale=0.8]{Figures/ch4/R3/LT95.pdf}  

 \caption{Network Lifetime for $Lifetime95$}
 \label{Figures/ch4/R3/LT95}
 \end{figure}
 \caption{Network Lifetime for $Lifetime95$}
 \label{Figures/ch4/R3/LT95}
 \end{figure}


@@ -801,7 +788,7 @@ In this experiment, we have observed the superiority of DiLCO-16 protocol and Di
 
 \begin{figure}[h!]
 \centering
 
 \begin{figure}[h!]
 \centering

-\includegraphics[scale=0.6]{Figures/ch4/R3/LT50.pdf}  
+\includegraphics[scale=0.8]{Figures/ch4/R3/LT50.pdf}  

 \caption{Network Lifetime for $Lifetime50$}
 \label{Figures/ch4/R3/LT50}
 \end{figure} 
 \caption{Network Lifetime for $Lifetime50$}
 \label{Figures/ch4/R3/LT50}
 \end{figure}