Update by Ali

[ThesisAli.git] / CHAPITRE_05.tex

diff --git a/CHAPITRE_05.tex b/CHAPITRE_05.tex
index 733232c3c5b504ce8d7b360e0f308f4f7441bb5e..5a39fd7049beed870d4b961bc854c9c0d7b671c7 100644 (file)
--- a/CHAPITRE_05.tex
+++ b/CHAPITRE_05.tex
@@ -330,7 +330,7 @@ Obviously, in  that case, DESK and GAF have fewer active nodes since they have a
 %\subsection{Stopped simulation runs}
 %\label{ch5:sec:03:02:03}
 
 %\subsection{Stopped simulation runs}
 %\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.  \\ \\ \\ \\
+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. \\
 


@@ -370,7 +370,7 @@ network sizes, for $Lifetime_{95}$ and $Lifetime_{50}$.
 
 The  results  show  that  MuDiLCO  is  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  status of the  sensor node. Among  the different versions of our protocol, the MuDiLCO-7  one consumes more energy than the other versions. This is  easy to understand since the bigger the  number of rounds and
 the number of  sensors involved in the integer program, the larger the time computation to solve the optimization problem. To improve the performances of MuDiLCO-7, we  should increase the  number of subregions  in order to  have fewer sensors to consider in the integer program.
 
 The  results  show  that  MuDiLCO  is  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  status of the  sensor node. Among  the different versions of our protocol, the MuDiLCO-7  one consumes more energy than the other versions. This is  easy to understand since the bigger the  number of rounds and
 the number of  sensors involved in the integer program, the larger the time computation to solve the optimization problem. To improve the performances of MuDiLCO-7, we  should increase the  number of subregions  in order to  have fewer sensors to consider in the integer program.

-\\ \\ \\
+

 
 
  \item {{\bf Execution time}}
 
 
  \item {{\bf Execution time}}


@@ -391,14 +391,15 @@ seconds (needed to solve optimization problem) for different values of $T$. The
 \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,
 \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,

-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. \\ \\ \\ \\ \\ \\ \\

 
 \item {{\bf Network lifetime}}
 %\subsection{Network lifetime}
 %\label{ch5:sec:03:02:06}
 The next  two figures,  Figures~\ref{fig8}(a) and \ref{fig8}(b),  illustrate the network lifetime  for different network sizes,  respectively for $Lifetime_{95}$ and  $Lifetime_{50}$.  Both  figures show  that the  network  lifetime increases together with the  number of sensor nodes, whatever the  protocol, thanks to the node  density  which  results in  more  and  more  redundant  nodes that  can  be deactivated and thus save energy.  Compared to the other approaches, our MuDiLCO
 
 \item {{\bf Network lifetime}}
 %\subsection{Network lifetime}
 %\label{ch5:sec:03:02:06}
 The next  two figures,  Figures~\ref{fig8}(a) and \ref{fig8}(b),  illustrate the network lifetime  for different network sizes,  respectively for $Lifetime_{95}$ and  $Lifetime_{50}$.  Both  figures show  that the  network  lifetime increases together with the  number of sensor nodes, whatever the  protocol, thanks to the node  density  which  results in  more  and  more  redundant  nodes that  can  be deactivated and thus save energy.  Compared to the other approaches, our MuDiLCO

-protocol  maximizes the  lifetime of  the network.   In particular,  the  gain in lifetime for a  coverage over 95\% is greater than 38\%  when switching from GAF to MuDiLCO-3. \\ \\ \\ 
+protocol  maximizes the  lifetime of  the network.   In particular,  the  gain in lifetime for a  coverage over 95\% is greater than 38\%  when switching from GAF to MuDiLCO-3.  

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


@@ -414,10 +415,11 @@ protocol  maximizes the  lifetime of  the network.   In particular,  the  gain i
 \end{figure}
 
 
 \end{figure}
 
 

-\end{enumerate} 

 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
 energy consumption,  since network lifetime and energy  consumption are directly linked.
 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
 energy consumption,  since network lifetime and energy  consumption are directly linked.

+\end{enumerate} 
+

 
 \section{Conclusion}
 \label{ch5:sec:05}
 
 \section{Conclusion}
 \label{ch5:sec:05}