Last update by Ali

diff --git a/CHAPITRE_04.tex b/CHAPITRE_04.tex
index c634430a8365f1bdadfac2262a8252867ff0e4ad..ff3f8f75a85dd5ba08dce8a40f81a20e9333e96f 100644 (file)
--- a/CHAPITRE_04.tex
+++ b/CHAPITRE_04.tex
@@ -191,12 +191,12 @@ Active  sensors  in the  period  will  execute  their sensing  task  to preserve
 
 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 $RE_j$ $\geq$ $E_{th}$ (the  amount of energy required to be alive during one period)). 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 criteria described in section \ref{ch4:sec:02:03:02}.
 %the  following  criteria   with  decreasing  importance:  larger  number  of neighbors, larger remaining energy, and  then in case of equality, larger index. 
 
 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 $RE_j$ $\geq$ $E_{th}$ (the  amount of energy required to be alive during one period)). 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 criteria described in section \ref{ch4:sec:02:03:02}.
 %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 ActiveSleep 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 ActiveSleep packet to know its state for the coming sensing phase. \textcolor{blue}{The flow chart of DiLCO protocol that executed in each sensor node is presented in \ref{flow4}.} 
+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 ActiveSleep 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 ActiveSleep packet to know its state for the coming sensing phase. \textcolor{blue}{The flowchart of DiLCO protocol executed in each sensor node is presented in Figure \ref{flow4}.} 

 
 \begin{figure}[ht!]
 \centering
 \includegraphics[scale=0.50]{Figures/ch4/Algo1.png} % 70mm
 
 \begin{figure}[ht!]
 \centering
 \includegraphics[scale=0.50]{Figures/ch4/Algo1.png} % 70mm

-\caption{The flow chart of DiLCO protocol.}
+\caption{The flowchart of DiLCO protocol.}

 \label{flow4}
 \end{figure} 
 
 \label{flow4}
 \end{figure} 
 


@@ -515,7 +515,7 @@ As shown in Figures~\ref{Figures/ch4/R1/EC}(a) and~\ref{Figures/ch4/R1/EC}(b), D
 \item {{\bf Execution Time}}
 %\subsubsection{Execution Time}
 
 \item {{\bf Execution Time}}
 %\subsubsection{Execution Time}
 

-In this experiment, the execution time of the 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 period. They are given for the different approaches and various numbers of sensors.  \\ \\% \\ \\ \\
+In this experiment, the execution time of the 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 period. They are given for the different approaches and various numbers of sensors.  % \\ \\ \\

 
 
 
 
 
 


@@ -545,7 +545,7 @@ In Figures~\ref{Figures/ch4/R1/LT}(a) and \ref{Figures/ch4/R1/LT}(b), network li
   \label{Figures/ch4/R1/LT}
 \end{figure}
 
   \label{Figures/ch4/R1/LT}
 \end{figure}
 

-For DiLCO-2 protocol, 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/LT}(a) and \ref{Figures/ch4/R1/LT}(b), the network lifetime obviously increases when the size of the network increases. The network lifetime also increases with the number of subregions, but only up to a given number. Thus we can see that DiLCO-16 leads to the larger lifetime improvement and not DiLCO-32. In fact, DilCO-32 protocol puts in active mode a larger number of sensor nodes especially near the borders of the subdivisions. It 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.
+For DiLCO-2 protocol, 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/LT}(a) and \ref{Figures/ch4/R1/LT}(b), the network lifetime obviously increases when the size of the network increases. The network lifetime also increases with the number of subregions, but only up to a given number. Thus we can see that DiLCO-16 leads to the larger lifetime improvement and not DiLCO-32. \\ \\ \\ \\In fact, DilCO-32 protocol puts in active mode a larger number of sensor nodes especially near the borders of the subdivisions.  It 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.

 
 
 \end{enumerate}
 
 
 \end{enumerate}


@@ -572,7 +572,7 @@ Figure~\ref{Figures/ch4/R2/CR} shows the average coverage ratio for 150 deployed
 As can be seen in Figure~\ref{Figures/ch4/R2/CR}, at the beginning the models which use a larger number of primary points provide slightly better coverage ratios, but latter they are the worst. 
 %Moreover, when the number of periods increases, coverage ratio produced by Model-9, Model-13, Model-17, and Model-21 decreases in comparison with Model-5 due to a larger time computation for the decision process for larger number of primary points.
 Moreover, when the number of periods increases, coverage ratio produced by all models decrease, but Model-5 is the one with the slowest decrease due to a smaller time computation of decision process for a smaller number of primary points. 
 As can be seen in Figure~\ref{Figures/ch4/R2/CR}, at the beginning the models which use a larger number of primary points provide slightly better coverage ratios, but latter they are the worst. 
 %Moreover, when the number of periods increases, coverage ratio produced by Model-9, Model-13, Model-17, and Model-21 decreases in comparison with Model-5 due to a larger time computation for the decision process for larger number of primary points.
 Moreover, when the number of periods increases, coverage ratio produced by all models decrease, but Model-5 is the one with the slowest decrease due to a smaller time computation of decision process for a smaller number of primary points. 

-As shown in Figure ~\ref{Figures/ch4/R2/CR}, coverage ratio decreases when the number of periods increases due to dead nodes. Model-5 is slightly more efficient than other models, because it offers a good coverage ratio for a larger number of periods in comparison with other models.
+As shown in Figure ~\ref{Figures/ch4/R2/CR}, coverage ratio decreases when the number of periods increases due to dead nodes. \\\\\\Model-5 is slightly more efficient than other models, because it offers a good coverage ratio for a larger number of periods in comparison with other models.

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


@@ -601,7 +601,7 @@ Figure~\ref{Figures/ch4/R2/SR} illustrates the percentage of stopped simulation
 \label{Figures/ch4/R2/SR}
 \end{figure} 
 
 \label{Figures/ch4/R2/SR}
 \end{figure} 
 

-When the number of primary points is increased, the percentage of the stopped simulation runs per period is increased. The reason behind the increase is the increasing number of dead sensors when the primary points increase. Model-5 is better than other models because it conserves more energy by turning on less sensors during the sensing phase and in the same time it preserves a good coverage for a larger number of periods in comparison with other models. Model~5 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 period is increased. The reason behind the increase is the increasing number of dead sensors when the primary points increase. Model-5 is better than other models because it conserves more energy by turning on less sensors during the sensing phase and in the same time it preserves a good coverage for a larger number of periods in comparison with other models. Model~5 seems to be more suitable to be used in wireless sensor networks. \\\\\\\\

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


@@ -644,7 +644,7 @@ Moreover, Model-5 has more suitable execution times and coverage ratio that lead
 
 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. 
 
 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. 

-As highlighted by Figures~\ref{Figures/ch4/R2/LT}(a) and \ref{Figures/ch4/R2/LT}(b), the network lifetime obviously increases when the size of the network increases, with  Model-5 that leads to the larger lifetime improvement. \\ \\
+As highlighted by Figures~\ref{Figures/ch4/R2/LT}(a) and \ref{Figures/ch4/R2/LT}(b), 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 Model-5, which uses less number of primary points, is the best one because it is less energy consuming during the network lifetime. It is also the better one from the point of view of coverage ratio. Our proposed Model-5 efficiently prolongs the network lifetime with a good coverage ratio in comparison with other models. Therefore, we have chosen Model-5 for all the experiments presented thereafter. 
  
 
 Comparison shows that Model-5, which uses less number of primary points, is the best one because it is less energy consuming during the network lifetime. It is also the better one from the point of view of coverage ratio. Our proposed Model-5 efficiently prolongs the network lifetime with a good coverage ratio in comparison with other models. Therefore, we have chosen Model-5 for all the experiments presented thereafter. 
  


@@ -679,7 +679,7 @@ The average coverage ratio for 150 deployed nodes is demonstrated in Figure~\ref
 \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} 

-DESK and GAF provide a little better coverage ratio with 99.99\% and 99.91\% against 98.4\% and 98.9\% produced by DiLCO-16 and DiLCO-32 for the lowest number of periods. \\ \\ \\
+DESK and GAF provide a little better coverage ratio with 99.99\% and 99.91\% against 98.4\% and 98.9\% produced by DiLCO-16 and DiLCO-32 for the lowest number of periods. 

 
 This is due to the fact that DiLCO protocol versions put in sleep mode redundant sensors thanks to the optimization (which lightly decreases the coverage ratio), while there are more active nodes in the case of DESK and GAF.
 
 
 This is due to the fact that DiLCO protocol versions put in sleep mode redundant sensors thanks to the optimization (which lightly decreases the coverage ratio), while there are more active nodes in the case of DESK and GAF.
 


@@ -701,7 +701,7 @@ It is important to have as few active nodes as possible in each period, in  orde
 \label{Figures/ch4/R3/ASR}
 \end{figure} 
 
 \label{Figures/ch4/R3/ASR}
 \end{figure} 
 

-The results presented in Figure~\ref{Figures/ch4/R3/ASR} show the superiority of the proposed DiLCO-16 protocol and DiLCO-32 protocol, in comparison with the other approaches.  DESK and GAF have, respectively, 37.5 \% and 44.5 \% active nodes, whereas DiLCO-16 and DiLCO-32 protocols compete perfectly with only 23.7 \% and 25.8 \%  active nodes for the first 14 periods. Then as the number of periods increases DiLCO-16 and DiLCO-32 protocols have larger number of active nodes in comparison with DESK and GAF, especially from period $35^{th}$ because they give a better coverage ratio than other approaches. We see that DESK and GAF have less number of active nodes beginning at the periods $35^{th}$ and $32^{th}$ because there are many dead nodes due to the high energy consumption by the redundant nodes during the previous sensing phases. \\
+The results presented in Figure~\ref{Figures/ch4/R3/ASR} show the superiority of the proposed DiLCO-16 protocol and DiLCO-32 protocol, in comparison with the other approaches.  DESK and GAF have, respectively, 37.5 \% and 44.5 \% active nodes, whereas DiLCO-16 and DiLCO-32 protocols compete perfectly with only 23.7 \% and 25.8 \%  active nodes for the first 14 periods. \\\\\\\\\\\\Then as the number of periods increases DiLCO-16 and DiLCO-32 protocols have larger number of active nodes in comparison with DESK and GAF, especially from period $35^{th}$ because they give a better coverage ratio than other approaches. We see that DESK and GAF have less number of active nodes beginning at the periods $35^{th}$ and $32^{th}$ because there are many dead nodes due to the high energy consumption by the redundant nodes during the previous sensing phases. 

 
 
 \item {{\bf Stopped simulation runs}}
 
 
 \item {{\bf Stopped simulation runs}}


@@ -741,13 +741,10 @@ Figures~\ref{Figures/ch4/R3/EC}(a) and~\ref{Figures/ch4/R3/EC}(b) illustrate the
 
 DiLCO-16 protocol and DiLCO-32 protocol are the most competitive from the energy consumption point of view. The other approaches have a high energy consumption due to activating a larger number of redundant nodes.
 %as well as the energy consumed during the different modes of sensor nodes. 
 
 DiLCO-16 protocol and DiLCO-32 protocol are the most competitive from the energy consumption point of view. The other approaches have a high energy consumption due to activating a larger number of redundant nodes.
 %as well as the energy consumed during the different modes of sensor nodes. 

-In fact,  the distribution of computation over the subregions greatly reduces the number of communications and the time of listening, thanks to the partitioning of the initial network into several independent subnetworks. 
-
+In fact,  the distribution of computation over the subregions greatly reduces the number of communications and the time of listening, thanks to the partitioning of the initial network into several independent subnetworks. \\\\\\\\

 
 \item {{\bf Network Lifetime}}
 
 \item {{\bf Network Lifetime}}

-%\subsubsection{The Network Lifetime}
-%In this experiment, we have observed the superiority of DiLCO-16 protocol and DiLCO-32 protocol against other two approaches in prolonging the network lifetime. 
-
+As highlighted by Figures~\ref{Figures/ch4/R3/LT}(a) and \ref{Figures/ch4/R3/LT}(b), the network lifetime obviously increases when the size of the network increases, with DiLCO-16 protocol and DiLCO-32 protocol which lead to maximize the lifetime of the network compared with other approaches. 

 %In figures~\ref{Figures/ch4/R3/LT95} and \ref{Figures/ch4/R3/LT50}, network lifetime, $Lifetime95$ and $Lifetime50$ respectively, are illustrated for different network sizes.  
 
 \begin{figure}[h!]
 %In figures~\ref{Figures/ch4/R3/LT95} and \ref{Figures/ch4/R3/LT50}, network lifetime, $Lifetime95$ and $Lifetime50$ respectively, are illustrated for different network sizes.  
 
 \begin{figure}[h!]


@@ -763,7 +760,7 @@ In fact,  the distribution of computation over the subregions greatly reduces th
   \label{Figures/ch4/R3/LT}
 \end{figure}
 
   \label{Figures/ch4/R3/LT}
 \end{figure}
 

-As highlighted by Figures~\ref{Figures/ch4/R3/LT}(a) and \ref{Figures/ch4/R3/LT}(b), the network lifetime obviously increases when the size of the network increases, with DiLCO-16 protocol and DiLCO-32 protocol which lead to maximize the lifetime of the network compared with other approaches. 
+

 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 periods, DiLCO-16 protocol and DiLCO-32 protocol efficiently prolong the network lifetime. 
 Comparison shows that DiLCO-16 protocol and DiLCO-32 protocol, which use distributed optimization over the subregions, are the best ones because they are robust to network disconnection during the network lifetime as well as they consume less energy in comparison with other approaches. 
 %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.
 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 periods, DiLCO-16 protocol and DiLCO-32 protocol efficiently prolong the network lifetime. 
 Comparison shows that DiLCO-16 protocol and DiLCO-32 protocol, which use distributed optimization over the subregions, are the best ones because they are robust to network disconnection during the network lifetime as well as they consume less energy in comparison with other approaches. 
 %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.

diff --git a/CHAPITRE_05.tex b/CHAPITRE_05.tex
index f1dafcdfe279148489010d4babe196592709b8c1..85655683a31d321137951dc729695ba77c9ad427 100644 (file)
--- a/CHAPITRE_05.tex
+++ b/CHAPITRE_05.tex
@@ -92,12 +92,12 @@ The difference with MuDiLCO in that the elected leader in each subregion is for
 each round  of the  sensing phase. Each sensing phase is itself divided into $T$ rounds and for each round a set of sensors (a cover set) is responsible for the sensing task. 
 %Each sensor node in the subregion will receive an ActiveSleep packet from leader, informing it to stay awake or to go to sleep for  each round of the sensing  phase. 
 Algorithm~\ref{alg:MuDiLCO}, which will be  executed by each node at the beginning  of a period, explains  how the ActiveSleep packet is obtained. In this way, a multiround optimization  process is performed  during each
 each round  of the  sensing phase. Each sensing phase is itself divided into $T$ rounds and for each round a set of sensors (a cover set) is responsible for the sensing task. 
 %Each sensor node in the subregion will receive an ActiveSleep packet from leader, informing it to stay awake or to go to sleep for  each round of the sensing  phase. 
 Algorithm~\ref{alg:MuDiLCO}, which will be  executed by each node at the beginning  of a period, explains  how the ActiveSleep packet is obtained. In this way, a multiround optimization  process is performed  during each

-period  after  Information~Exchange  and  Leader~Election phases,  in  order to produce $T$ cover sets that will take the mission of sensing for $T$ rounds. \textcolor{blue}{The flow chart of MuDiLCO protocol that executed in each sensor node is presented in \ref{flow5}.} 
+period  after  Information~Exchange  and  Leader~Election phases,  in  order to produce $T$ cover sets that will take the mission of sensing for $T$ rounds. \textcolor{blue}{The flowchart of MuDiLCO protocol executed in each sensor node is presented in Figure \ref{flow5}.} 

 
 \begin{figure}[ht!]
 \centering
 \includegraphics[scale=0.50]{Figures/ch5/Algo2.png} % 70mm
 
 \begin{figure}[ht!]
 \centering
 \includegraphics[scale=0.50]{Figures/ch5/Algo2.png} % 70mm

-\caption{The flow chart of MuDiLCO protocol.}
+\caption{The flowchart of MuDiLCO protocol.}

 \label{flow5}
 \end{figure} 
 
 \label{flow5}
 \end{figure} 
 


@@ -340,8 +340,8 @@ Obviously, in  that case, DESK and GAF have fewer active nodes since they have a
 %\label{ch5:sec:03:02:03}
 
 Figure~\ref{fig6} reports the cumulative  percentage of stopped simulations runs per round for 150 deployed nodes. This figure gives the  breakpoint for each method.  
 %\label{ch5:sec:03:02:03}
 
 Figure~\ref{fig6} reports the cumulative  percentage of stopped simulations runs per round for 150 deployed nodes. This figure gives the  breakpoint for each method.  

-DESK stops first,  after approximately 45~rounds, because it consumes the more energy by  turning on a large number of redundant  nodes during the sensing phase. GAF  stops secondly for the  same reason than  DESK.  MuDiLCO overcomes DESK and GAF because the  optimization process distributed on several subregions leads  to coverage  preservation and  so extends  the network  lifetime.  Let us
-emphasize that the  simulation continues as long as a network  in a subregion is still connected. \\
+DESK stops first,  after approximately 45~rounds, because it consumes the more energy by  turning on a large number of redundant  nodes during the sensing phase. GAF  stops secondly for the  same reason than  DESK. \\\\\\ MuDiLCO overcomes DESK and GAF because the  optimization process distributed on several subregions leads  to coverage  preservation and  so extends  the network  lifetime.  Let us
+emphasize that the  simulation continues as long as a network  in a subregion is still connected. 

 
 
 \begin{figure}[t]
 
 
 \begin{figure}[t]


@@ -388,7 +388,7 @@ the number of  sensors involved in the integer program, the larger the time comp
 
 We observe  the impact of the  network size and of  the number of  rounds on the
 computation  time.   Figure~\ref{fig77} gives  the  average  execution times  in
 
 We observe  the impact of the  network size and of  the number of  rounds on the
 computation  time.   Figure~\ref{fig77} gives  the  average  execution times  in

-seconds (needed to solve optimization problem) for different values of $T$. The original execution time is computed as described in chapter 4, section \ref{ch4:sec:04:02}.
+seconds (needed to solve optimization problem) for different values of $T$. \\\\\\ 

 
 %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   $\left( \frac{35330}{2} \times  \frac{1}{6} \right)$ and  reported on Figure~\ref{fig77} for different network sizes.
 
 
 %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   $\left( \frac{35330}{2} \times  \frac{1}{6} \right)$ and  reported on Figure~\ref{fig77} for different network sizes.
 


@@ -399,7 +399,7 @@ seconds (needed to solve optimization problem) for different values of $T$. The
 \label{fig77}
 \end{figure} 
 
 \label{fig77}
 \end{figure} 
 

-As expected,  the execution time increases  with the number of  rounds $T$ taken into account to schedule the sensing phase. The times obtained for $T=1,3$ or $5$ seem 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,
+The original execution time is computed as described in chapter 4, section \ref{ch4:sec:04:02}. As expected,  the execution time increases  with the number of  rounds $T$ taken into account to schedule the sensing phase. The times obtained for $T=1,3$ or $5$ seem 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 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 optimization problem. %\\ \\ \\ \\ \\ \\ \\
 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 optimization problem. %\\ \\ \\ \\ \\ \\ \\


@@ -425,7 +425,7 @@ protocol  maximizes the  lifetime of  the network.   In particular,  the  gain i
 
 
 The  slight decrease that can be observed  for MuDiLCO-7 in case of  $Lifetime_{95}$  with  large  wireless  sensor  networks  results  from  the difficulty  of the optimization  problem to  be solved  by the  integer program.
 
 
 The  slight decrease that can be observed  for MuDiLCO-7 in case of  $Lifetime_{95}$  with  large  wireless  sensor  networks  results  from  the difficulty  of the optimization  problem to  be solved  by the  integer program.

-This  point was  already noticed  in \ref{subsec:EC} devoted  to the
+\\\\\\\\This  point was  already noticed  in \ref{subsec:EC} devoted  to the

 energy consumption,  since network lifetime and energy  consumption are directly linked.
 \end{enumerate} 
 
 energy consumption,  since network lifetime and energy  consumption are directly linked.
 \end{enumerate} 
 

diff --git a/CHAPITRE_06.tex b/CHAPITRE_06.tex
index 0ea43653617cdafbac212ee6e2494a13b177c1a9..66f0e6a60a94373c698e2f3f696b9dc13ab15d1e 100644 (file)
--- a/CHAPITRE_06.tex
+++ b/CHAPITRE_06.tex
@@ -132,7 +132,7 @@ In the PeCO protocol,  the  scheduling  of  the  sensor nodes'  activities  is f
 
 \noindent The  WSN area of interest is, in a  first step, divided  into regular homogeneous subregions  using a divide-and-conquer  algorithm. In a  second step our  protocol  will  be  executed  in a distributed way in each subregion simultaneously to schedule nodes' activities for one sensing period. Sensor nodes  are assumed to be deployed  almost uniformly over the  region. The regular subdivision  is made such that the number of hops between  any pairs of sensors inside a subregion is less than or equal to 3.
 
 
 \noindent The  WSN area of interest is, in a  first step, divided  into regular homogeneous subregions  using a divide-and-conquer  algorithm. In a  second step our  protocol  will  be  executed  in a distributed way in each subregion simultaneously to schedule nodes' activities for one sensing period. Sensor nodes  are assumed to be deployed  almost uniformly over the  region. The regular subdivision  is made such that the number of hops between  any pairs of sensors inside a subregion is less than or equal to 3.
 

-As  shown in  Figure~\ref{fig2}, node  activity  scheduling is  produced by  our protocol in a periodic manner. Each period is divided into 4 stages: Information (INFO)  Exchange,  Leader Election,  Decision  (the  result of  an  optimization problem),  and  Sensing.   For  each  period, there  is  exactly  one  set  cover responsible for  the sensing task.  Protocols  based on a periodic  scheme, like 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
+As shown in  Figure~\ref{fig2}, node  activity  scheduling is  produced by  our protocol in a periodic manner. Each period is divided into 4 stages: Information (INFO)  Exchange,  Leader Election,  Decision  (the  result of  an  optimization problem),  and  Sensing.   For  each  period, there  is  exactly  one  set  cover responsible for  the sensing task.  Protocols  based on a periodic  scheme, like 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 network will be temporarily affected: only  during the period of sensing until a new period starts, since a new set cover will take charge of the sensing task in the next period. The energy consumption and some other constraints can easily be taken  into  account since  the  sensors  can  update  and then  exchange  their information (including their  residual energy) at the beginning  of each period. However, the pre-sensing  phases (INFO Exchange, Leader  Election, and Decision)
 are energy consuming, even for nodes that will not join the set cover to monitor the area.
 
 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 network will be temporarily affected: only  during the period of sensing until a new period starts, since a new set cover will take charge of the sensing task in the next period. The energy consumption and some other constraints can easily be taken  into  account since  the  sensors  can  update  and then  exchange  their information (including their  residual energy) at the beginning  of each period. However, the pre-sensing  phases (INFO Exchange, Leader  Election, and Decision)
 are energy consuming, even for nodes that will not join the set cover to monitor the area.
 


@@ -209,12 +209,12 @@ The sensors  inside a same  region cooperate to  elect a leader.   The selection
 \item larger  remaining energy;
 \item and then  in case  of equality,  larger index.
 \end{enumerate}
 \item larger  remaining energy;
 \item and then  in case  of equality,  larger index.
 \end{enumerate}

-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. \textcolor{blue}{The flow chart of PeCO protocol that executed in each sensor node is presented in \ref{flow6}.} 
+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. \textcolor{blue}{The flowchart of PeCO protocol executed in each sensor node is presented in Figure \ref{flow6}.} 

 
 \begin{figure}[ht!]
 \centering
 \includegraphics[scale=0.45]{Figures/ch6/Algo3.pdf} % 70mm
 
 \begin{figure}[ht!]
 \centering
 \includegraphics[scale=0.45]{Figures/ch6/Algo3.pdf} % 70mm

-\caption{The flow chart of PeCO protocol.}
+\caption{The flowchart of PeCO protocol.}

 \label{flow6}
 \end{figure} 
 
 \label{flow6}
 \end{figure} 
 

diff --git a/Thesis.toc b/Thesis.toc
index 5f74a126f07f07704e85a1728fa8489ce8cf9d27..2b8d1d43fa3b38176bb5070b2db69ae36b25bdd4 100644 (file)
--- a/Thesis.toc
+++ b/Thesis.toc
@@ -74,35 +74,35 @@
 \contentsline {subsection}{\numberline {4.4.4}Performance Metrics}{86}{subsection.4.4.4}
 \contentsline {subsection}{\numberline {4.4.5}Performance Analysis for Different Number of Subregions}{87}{subsection.4.4.5}
 \contentsline {subsection}{\numberline {4.4.6}Performance Analysis for Different Number of Primary Points}{92}{subsection.4.4.6}
 \contentsline {subsection}{\numberline {4.4.4}Performance Metrics}{86}{subsection.4.4.4}
 \contentsline {subsection}{\numberline {4.4.5}Performance Analysis for Different Number of Subregions}{87}{subsection.4.4.5}
 \contentsline {subsection}{\numberline {4.4.6}Performance Analysis for Different Number of Primary Points}{92}{subsection.4.4.6}

-\contentsline {subsection}{\numberline {4.4.7}Performance Comparison with other Approaches}{98}{subsection.4.4.7}
-\contentsline {section}{\numberline {4.5}Conclusion}{104}{section.4.5}
-\contentsline {chapter}{\numberline {5}Multiround Distributed Lifetime Coverage Optimization Protocol}{105}{chapter.5}
-\contentsline {section}{\numberline {5.1}Introduction}{105}{section.5.1}
-\contentsline {section}{\numberline {5.2}Description of the MuDiLCO Protocol }{105}{section.5.2}
-\contentsline {section}{\numberline {5.3}Primary Points based Multiround Coverage Problem Formulation}{107}{section.5.3}
-\contentsline {section}{\numberline {5.4}Experimental Study and Analysis}{109}{section.5.4}
-\contentsline {subsection}{\numberline {5.4.1}Simulation Setup}{109}{subsection.5.4.1}
-\contentsline {subsection}{\numberline {5.4.2}Metrics}{109}{subsection.5.4.2}
-\contentsline {subsection}{\numberline {5.4.3}Results Analysis and Comparison }{110}{subsection.5.4.3}
-\contentsline {section}{\numberline {5.5}Conclusion}{116}{section.5.5}
-\contentsline {chapter}{\numberline {6} Perimeter-based Coverage Optimization to Improve Lifetime in WSNs}{117}{chapter.6}
-\contentsline {section}{\numberline {6.1}Introduction}{117}{section.6.1}
-\contentsline {section}{\numberline {6.2}Description of the PeCO Protocol}{117}{section.6.2}
-\contentsline {subsection}{\numberline {6.2.1}Assumptions and Models}{117}{subsection.6.2.1}
-\contentsline {subsection}{\numberline {6.2.2}PeCO Protocol Algorithm}{120}{subsection.6.2.2}
-\contentsline {section}{\numberline {6.3}Perimeter-based Coverage Problem Formulation}{123}{section.6.3}
-\contentsline {section}{\numberline {6.4}Performance Evaluation and Analysis}{124}{section.6.4}
-\contentsline {subsection}{\numberline {6.4.1}Simulation Settings}{124}{subsection.6.4.1}
-\contentsline {subsection}{\numberline {6.4.2}Simulation Results}{125}{subsection.6.4.2}
-\contentsline {subsubsection}{\numberline {6.4.2.1}Coverage Ratio}{125}{subsubsection.6.4.2.1}
-\contentsline {subsubsection}{\numberline {6.4.2.2}Active Sensors Ratio}{125}{subsubsection.6.4.2.2}
-\contentsline {subsubsection}{\numberline {6.4.2.3}Energy Consumption}{126}{subsubsection.6.4.2.3}
-\contentsline {subsubsection}{\numberline {6.4.2.4}Network Lifetime}{126}{subsubsection.6.4.2.4}
-\contentsline {subsubsection}{\numberline {6.4.2.5}Impact of $\alpha $ and $\beta $ on PeCO's performance}{127}{subsubsection.6.4.2.5}
-\contentsline {section}{\numberline {6.5}Conclusion}{131}{section.6.5}
-\contentsline {part}{III\hspace {1em}Conclusion and Perspectives}{133}{part.3}
-\contentsline {chapter}{\numberline {7}Conclusion and Perspectives}{135}{chapter.7}
-\contentsline {section}{\numberline {7.1}Conclusion}{135}{section.7.1}
-\contentsline {section}{\numberline {7.2}Perspectives}{136}{section.7.2}
-\contentsline {part}{Publications}{139}{chapter*.15}
-\contentsline {part}{Bibliographie}{154}{chapter*.19}
+\contentsline {subsection}{\numberline {4.4.7}Performance Comparison with other Approaches}{96}{subsection.4.4.7}
+\contentsline {section}{\numberline {4.5}Conclusion}{105}{section.4.5}
+\contentsline {chapter}{\numberline {5}Multiround Distributed Lifetime Coverage Optimization Protocol}{107}{chapter.5}
+\contentsline {section}{\numberline {5.1}Introduction}{107}{section.5.1}
+\contentsline {section}{\numberline {5.2}Description of the MuDiLCO Protocol }{107}{section.5.2}
+\contentsline {section}{\numberline {5.3}Primary Points based Multiround Coverage Problem Formulation}{109}{section.5.3}
+\contentsline {section}{\numberline {5.4}Experimental Study and Analysis}{111}{section.5.4}
+\contentsline {subsection}{\numberline {5.4.1}Simulation Setup}{111}{subsection.5.4.1}
+\contentsline {subsection}{\numberline {5.4.2}Metrics}{111}{subsection.5.4.2}
+\contentsline {subsection}{\numberline {5.4.3}Results Analysis and Comparison }{112}{subsection.5.4.3}
+\contentsline {section}{\numberline {5.5}Conclusion}{118}{section.5.5}
+\contentsline {chapter}{\numberline {6} Perimeter-based Coverage Optimization to Improve Lifetime in WSNs}{119}{chapter.6}
+\contentsline {section}{\numberline {6.1}Introduction}{119}{section.6.1}
+\contentsline {section}{\numberline {6.2}Description of the PeCO Protocol}{119}{section.6.2}
+\contentsline {subsection}{\numberline {6.2.1}Assumptions and Models}{119}{subsection.6.2.1}
+\contentsline {subsection}{\numberline {6.2.2}PeCO Protocol Algorithm}{122}{subsection.6.2.2}
+\contentsline {section}{\numberline {6.3}Perimeter-based Coverage Problem Formulation}{125}{section.6.3}
+\contentsline {section}{\numberline {6.4}Performance Evaluation and Analysis}{126}{section.6.4}
+\contentsline {subsection}{\numberline {6.4.1}Simulation Settings}{126}{subsection.6.4.1}
+\contentsline {subsection}{\numberline {6.4.2}Simulation Results}{127}{subsection.6.4.2}
+\contentsline {subsubsection}{\numberline {6.4.2.1}Coverage Ratio}{127}{subsubsection.6.4.2.1}
+\contentsline {subsubsection}{\numberline {6.4.2.2}Active Sensors Ratio}{127}{subsubsection.6.4.2.2}
+\contentsline {subsubsection}{\numberline {6.4.2.3}Energy Consumption}{128}{subsubsection.6.4.2.3}
+\contentsline {subsubsection}{\numberline {6.4.2.4}Network Lifetime}{128}{subsubsection.6.4.2.4}
+\contentsline {subsubsection}{\numberline {6.4.2.5}Impact of $\alpha $ and $\beta $ on PeCO's performance}{129}{subsubsection.6.4.2.5}
+\contentsline {section}{\numberline {6.5}Conclusion}{133}{section.6.5}
+\contentsline {part}{III\hspace {1em}Conclusion and Perspectives}{135}{part.3}
+\contentsline {chapter}{\numberline {7}Conclusion and Perspectives}{137}{chapter.7}
+\contentsline {section}{\numberline {7.1}Conclusion}{137}{section.7.1}
+\contentsline {section}{\numberline {7.2}Perspectives}{138}{section.7.2}
+\contentsline {part}{Publications}{141}{chapter*.15}
+\contentsline {part}{Bibliographie}{156}{chapter*.19}

diff --git a/entete.tex b/entete.tex
index 309653e6a02a6e2f403977ccb0d412ffb4469d33..e812f1011910ced42acc64ac544af9d71b7549af 100644 (file)
--- a/entete.tex
+++ b/entete.tex
@@ -66,7 +66,7 @@
 %% The second mandatory parameter is the date of the PhD defense.
 %% The third mandatory parameter is the reference number given by the University Library after the PhD defense.
 %%\declarethesis[Sous-titre]{Titre}{17 septembre 2012}{XXX}
 %% The second mandatory parameter is the date of the PhD defense.
 %% The third mandatory parameter is the reference number given by the University Library after the PhD defense.
 %%\declarethesis[Sous-titre]{Titre}{17 septembre 2012}{XXX}

-\declarethesis{Distributed Coverage Optimization Techniques for Improving Lifetime of Wireless Sensor Networks}{30 September 2015}{2015930}
+\declarethesis{Distributed Coverage Optimization Techniques for Improving Lifetime of Wireless Sensor Networks}{30 September 2015}{2015050}

  
 %%--------------------
 %% Set the author of the PhD thesis
  
 %%--------------------
 %% Set the author of the PhD thesis