X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/ThesisAli.git/blobdiff_plain/df0ded57ac60ab03ead82d37326459a25812634a..025698f711cfa8d174abdf309b09da921696771e:/CHAPITRE_05.tex?ds=inline

diff --git a/CHAPITRE_05.tex b/CHAPITRE_05.tex
index f1dafcd..8565568 100644
--- 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
-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
-\caption{The flow chart of MuDiLCO protocol.}
+\caption{The flowchart of MuDiLCO protocol.}
 \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.  
-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]
@@ -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
-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.
 
@@ -399,7 +399,7 @@ seconds (needed to solve optimization problem) for different values of $T$. The
 \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. %\\ \\ \\ \\ \\ \\ \\
@@ -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.
-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}