Minor modifications

diff --git a/Abstruct.tex b/Abstruct.tex
index a5bc5b5ae9bafcb39432aa3b42b043777e17c51a..7f0ad75579f99bff7dfaece2d4e0a68865a40d84 100644 (file)
--- a/Abstruct.tex
+++ b/Abstruct.tex
@@ -1,3 +1,6 @@
+
+\afterpage{\blankpage}
+

 \chapter*{Abstract \markboth{Abstract}{Abstract}}
 \label{cha}
 \addcontentsline{toc}{chapter}{Abstract}
 \chapter*{Abstract \markboth{Abstract}{Abstract}}
 \label{cha}
 \addcontentsline{toc}{chapter}{Abstract}

diff --git a/CHAPITRE_04.tex b/CHAPITRE_04.tex
index ff3f8f75a85dd5ba08dce8a40f81a20e9333e96f..5254693bdd89044ef1458f2b2e3bab2bf9a199b3 100644 (file)
--- a/CHAPITRE_04.tex
+++ b/CHAPITRE_04.tex
@@ -20,7 +20,7 @@ divided  into subregions using  a divide-and-conquer  algorithm and  an activity
 
 The remainder of this chapter is organized as follows. The next section is devoted to the DiLCO protocol description. Section \ref{ch4:sec:03} gives the primary points based coverage problem formulation which is used to schedule the activation of sensors. Section \ref{ch4:sec:04} shows the simulation results obtained using the discrete event simulator OMNeT++ \cite{ref158}. They fully demonstrate the usefulness of the proposed approach. Finally, we give concluding remarks in section \ref{ch4:sec:05}.
 
 
 The remainder of this chapter is organized as follows. The next section is devoted to the DiLCO protocol description. Section \ref{ch4:sec:03} gives the primary points based coverage problem formulation which is used to schedule the activation of sensors. Section \ref{ch4:sec:04} shows the simulation results obtained using the discrete event simulator OMNeT++ \cite{ref158}. They fully demonstrate the usefulness of the proposed approach. Finally, we give concluding remarks in section \ref{ch4:sec:05}.
 

-
+\vfill

 
 \section{Description of the DiLCO Protocol}
 \label{ch4:sec:02}
 
 \section{Description of the DiLCO Protocol}
 \label{ch4:sec:02}


@@ -747,25 +747,24 @@ In fact,  the distribution of computation over the subregions greatly reduces th
 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.  
 
 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!]
-\centering
+%%\begin{figure}[p!]
+%%\centering

 % \begin{multicols}{0}
 % \begin{multicols}{0}

-\centering
-\includegraphics[scale=0.8]{Figures/ch4/R3/LT95.eps}\\~ ~ ~ ~ ~(a) \\
+%%\centering
+%%\includegraphics[scale=0.8]{Figures/ch4/R3/LT95.eps}\\~ ~ ~ ~ ~(a) \\

 %\hfill 
 %\hfill 

-\includegraphics[scale=0.8]{Figures/ch4/R3/LT50.eps}\\~ ~ ~ ~ ~(b)
+%%\includegraphics[scale=0.8]{Figures/ch4/R3/LT50.eps}\\~ ~ ~ ~ ~(b)

 
 %\end{multicols} 
 
 %\end{multicols} 

-\caption{Network lifetime for (a) $Lifetime_{95}$ and (b) $Lifetime_{50}$}
-  \label{Figures/ch4/R3/LT}
-\end{figure}
+%%\caption{Network lifetime for (a) $Lifetime_{95}$ and (b) $Lifetime_{50}$}
+%%  \label{Figures/ch4/R3/LT}
+%%\end{figure}

 
 
 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.
 

-

 \end{enumerate}
 
 \section{Conclusion}
 \end{enumerate}
 
 \section{Conclusion}


@@ -773,4 +772,15 @@ Comparison shows that DiLCO-16 protocol and DiLCO-32 protocol, which use distrib
 A crucial problem in WSN is to schedule the sensing activities of the different nodes  in order to ensure both coverage of  the area  of interest  and longer network lifetime. The inherent limitations of sensor nodes, in energy provision, communication, and computing capacities,  require protocols that optimize the use of the  available resources  to  fulfill the sensing  task. To address  this problem, this chapter proposes a  two-step approach. Firstly, the field of sensing
 is  divided into  smaller  subregions using  the  concept of  divide-and-conquer method. Secondly,  a distributed  protocol called Distributed  Lifetime Coverage Optimization is applied in each  subregion to optimize the coverage and lifetime performances. In a subregion,  our protocol  consists in  electing a  leader node, which will then perform a sensor activity scheduling. The challenges include how to  select the most efficient leader in each  subregion and  the  best representative set of active nodes to ensure a high level of coverage. To assess the performance of our approach, we  compared it with two other approaches using many performance metrics  like coverage ratio or network  lifetime. We have also studied the  impact of the  number of subregions  chosen to subdivide the  area of interest, considering  different  network  sizes. The  experiments  show  that increasing the  number of subregions improves  the lifetime. The  more subregions there are, the  more robust the network is against random disconnection resulting from dead nodes.  However, for  a given sensing field and network size there is an optimal number of  subregions. Therefore, in case of our simulation context  a subdivision in  $16$~subregions seems to be the most relevant.
 
 A crucial problem in WSN is to schedule the sensing activities of the different nodes  in order to ensure both coverage of  the area  of interest  and longer network lifetime. The inherent limitations of sensor nodes, in energy provision, communication, and computing capacities,  require protocols that optimize the use of the  available resources  to  fulfill the sensing  task. To address  this problem, this chapter proposes a  two-step approach. Firstly, the field of sensing
 is  divided into  smaller  subregions using  the  concept of  divide-and-conquer method. Secondly,  a distributed  protocol called Distributed  Lifetime Coverage Optimization is applied in each  subregion to optimize the coverage and lifetime performances. In a subregion,  our protocol  consists in  electing a  leader node, which will then perform a sensor activity scheduling. The challenges include how to  select the most efficient leader in each  subregion and  the  best representative set of active nodes to ensure a high level of coverage. To assess the performance of our approach, we  compared it with two other approaches using many performance metrics  like coverage ratio or network  lifetime. We have also studied the  impact of the  number of subregions  chosen to subdivide the  area of interest, considering  different  network  sizes. The  experiments  show  that increasing the  number of subregions improves  the lifetime. The  more subregions there are, the  more robust the network is against random disconnection resulting from dead nodes.  However, for  a given sensing field and network size there is an optimal number of  subregions. Therefore, in case of our simulation context  a subdivision in  $16$~subregions seems to be the most relevant.
 

+\begin{figure}[p!]
+\centering
+% \begin{multicols}{0}
+\centering
+\includegraphics[scale=0.8]{Figures/ch4/R3/LT95.eps}\\~ ~ ~ ~ ~(a) \\
+%\hfill 
+\includegraphics[scale=0.8]{Figures/ch4/R3/LT50.eps}\\~ ~ ~ ~ ~(b)

 
 

+%\end{multicols} 
+\caption{Network lifetime for (a) $Lifetime_{95}$ and (b) $Lifetime_{50}$}
+  \label{Figures/ch4/R3/LT}
+\end{figure}

diff --git a/CHAPITRE_05.tex b/CHAPITRE_05.tex
index 85655683a31d321137951dc729695ba77c9ad427..7cf87259722adab6b067a9bef1e88dfb6f42e372 100644 (file)
--- a/CHAPITRE_05.tex
+++ b/CHAPITRE_05.tex
@@ -106,11 +106,6 @@ period  after  Information~Exchange  and  Leader~Election phases,  in  order to
 
 %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 (Information  Exchange, Leader  Election, and  Decision) are energy  consuming for some  nodes, even  when they  do not  join the  network to monitor the area.
 
 
 %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 (Information  Exchange, Leader  Election, and  Decision) are energy  consuming for some  nodes, even  when they  do not  join the  network to monitor the area.
 

- 
-
-
-
-

 \section{Primary Points based Multiround Coverage Problem Formulation}
 \label{ch5:sec:03}
 
 \section{Primary Points based Multiround Coverage Problem Formulation}
 \label{ch5:sec:03}
 


@@ -121,6 +116,8 @@ period  after  Information~Exchange  and  Leader~Election phases,  in  order to
 
 We extend the mathematical formulation given in section \ref{ch4:sec:03} to take into account multiple rounds.
 
 
 We extend the mathematical formulation given in section \ref{ch4:sec:03} to take into account multiple rounds.
 

+\newpage
+

 For a  primary point  $p$, let $\alpha_{j,p}$  denote the indicator  function of
 whether the point $p$ is covered, that is
 \begin{equation}
 For a  primary point  $p$, let $\alpha_{j,p}$  denote the indicator  function of
 whether the point $p$ is covered, that is
 \begin{equation}


@@ -439,4 +436,4 @@ In this chapter, we have presented a protocol, called MuDiLCO (Multiround  Distr
 Simulations results show the  relevance of the proposed  protocol in  terms of lifetime, coverage  ratio, active  sensors ratio, energy  consumption, execution time. Indeed,  when dealing with  large wireless sensor networks,  a distributed approach, like  the one we  propose, allows to reduce the difficulty of  a single global optimization problem by partitioning it into many smaller problems, one per subregion, that can be solved  more easily. Nevertheless, results also show that it is not possible to plan the activity of sensors over too many rounds because the resulting optimization problem leads to too high-resolution times and thus to an excessive energy consumption. Compared with DiLCO, it is clear that MuDiLCO improves the network lifetime especially for the dense network, but it is  less robust than DiLCO under sensor nodes failures. Therefore, choosing the number of rounds $T$ depends on the type of application the WSN is deployed for. 
 
 
 Simulations results show the  relevance of the proposed  protocol in  terms of lifetime, coverage  ratio, active  sensors ratio, energy  consumption, execution time. Indeed,  when dealing with  large wireless sensor networks,  a distributed approach, like  the one we  propose, allows to reduce the difficulty of  a single global optimization problem by partitioning it into many smaller problems, one per subregion, that can be solved  more easily. Nevertheless, results also show that it is not possible to plan the activity of sensors over too many rounds because the resulting optimization problem leads to too high-resolution times and thus to an excessive energy consumption. Compared with DiLCO, it is clear that MuDiLCO improves the network lifetime especially for the dense network, but it is  less robust than DiLCO under sensor nodes failures. Therefore, choosing the number of rounds $T$ depends on the type of application the WSN is deployed for. 
 
 

- 
\ No newline at end of file
+ 

diff --git a/Thesis.tex b/Thesis.tex
index 593881a33fc36a220b08625efbd48b343afa0d56..ab840033284e684a4a2ab221ae4dff1d4bb4d134 100644 (file)
--- a/Thesis.tex
+++ b/Thesis.tex
@@ -13,9 +13,11 @@
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

+

 \include{Abstruct}  
 \include{Resume}
 \include{Abstruct}  
 \include{Resume}

-%% Sommaire   
+%% Sommaire
+

 \tableofcontents
 \addcontentsline{toc}{chapter}{Table of Contents}
 
 \tableofcontents
 \addcontentsline{toc}{chapter}{Table of Contents}
 

diff --git a/Thesis.toc b/Thesis.toc
index 2b8d1d43fa3b38176bb5070b2db69ae36b25bdd4..d31c9b5c7aec2cf0b898a0a4eb63a9cda96bd72b 100644 (file)
--- a/Thesis.toc
+++ b/Thesis.toc
@@ -75,34 +75,34 @@
 \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}{96}{subsection.4.4.7}
 \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}{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}
+\contentsline {section}{\numberline {4.5}Conclusion}{103}{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}

diff --git a/acknowledgements.tex b/acknowledgements.tex
index 7ef04e2a17a3be35a1bd3af2b12b3b05ff0d4b7f..1ae47db3405667c376b363e801739913255eb199 100644 (file)
--- a/acknowledgements.tex
+++ b/acknowledgements.tex
@@ -14,13 +14,13 @@
 
 The long journey of my Ph.D. study has finished. It is with great pleasure that I acknowledge my debts to those who have greatly contributed to the success of this dissertation. It was only through support and encouragement of many that I have been able to complete this amazing journey.
 
 
 The long journey of my Ph.D. study has finished. It is with great pleasure that I acknowledge my debts to those who have greatly contributed to the success of this dissertation. It was only through support and encouragement of many that I have been able to complete this amazing journey.
 

-Foremost, I would like to express my sincere gratitude to all my supervisors Prof. Dr. Rapha\"el Couturier, Asst. Prof. Dr. Karine Deschinkel, and Asst. Prof. Dr. Michel Salomon for their continuous support, encouragement, and advice they have provided throughout my Ph.D. study. Their patience, motivation, enthusiasm, and immense knowledge taught me alot. Their tireless guidance has helped me immensely in researching and writing this dissertation. I have been extremely lucky to have supervisors who cared so much about my work, and who responded to my questions and queries so promptly. 
+Foremost, I would like to express my sincere gratitude to all my supervisors: Prof.~Dr.~Rapha\"el~Couturier, Asst.~Prof.~Dr.~Karine~Deschinkel, and Asst.~Prof. Dr.~Michel~Salomon for their continuous support, encouragement, and advice they have provided throughout my Ph.D.~study. Their patience, motivation, enthusiasm, and immense knowledge taught me a lot. Their tireless guidance has helped me immensely in researching and writing this dissertation. I have been extremely lucky to have supervisors who cared so much about my work, and who responded to my questions and queries so promptly. 

 
 

-
-Besides my supervisors, I would like to express my gratitude to Prof. Dr. YE-Q IONG SONG and Assoc Prof. Dr. HAMIDA SEBA (HDR) for accepting to review my dissertation and for their insightful and appreciated comments. I would like to thank also Prof. Dr. SYLVAIN CONTASSOT-VIVIER for accepting to participate in my dissertation committee.
+Besides my supervisors, I would like to express my gratitude to Prof.~Dr.~Ye-Qiong~Song and Assoc~Prof.~Dr.~Hamida~Seba (HDR) for accepting to review my dissertation and for their insightful and appreciated comments. I would like to thank also Prof.~Dr.~Sylvain~Contassot-Vivier for accepting to participate in my dissertation committee.

 
 I would like to gratefully acknowledge the University of Babylon, Iraq for financial support; Campus France and University of Franche-Comté for the received support.
 
 
 I would like to gratefully acknowledge the University of Babylon, Iraq for financial support; Campus France and University of Franche-Comté for the received support.
 

-My appreciation and thanks go to the members of the team AND (Algorithms and Distributed Digital) for the warm and friendly atmosphere in which he allowed me to work. So thank you to Jacques Bahi, Pierre-Cyrille HEAM, Huu Quan Do, Bassam Alkindy, Abdallah Makhoul, Huda AL-NAYYEF, Jean-Claude Charr, Jean-François Couchot, Ahmed Mostefaoui, Ahmed Fanfakh, Yousra Ahmed Fadil, Stéphane Domas, Pierre SAENGER, Zeinab FAWAZ, Arnaud Giersch, Christophe Guyeux, Mourad Hakem, David LAIYMANI, Gilles PERROT, Christian Salim, Bashar AL-NUAIMI, Santiago Costarelli, Carol Habib, Hassan MOUSTAFA HARB, and Ke Du. I would like to express my thanks and my best wishes to Ingrid Couturier for all the received assistance during my study. I would also like to express my thanks to Dr. Lilia Ziane Khodja, PostDoc at $LTAS-A\&M$, Liege, Belgium. I would like to thank Béatrice Domenge and Maxim Moureaux in the Reprographie service for all received assistance during my study. I would also like to thank Patricia Py in the Computer Department secretary. I dare not risk missing to mention anyone’s name, so I will simply say 'thank you ALL for being there for me'.
+My appreciation and thanks go to the members of the team AND ({\it Algorithmique Numérique Distribuée}) for the warm and friendly atmosphere in which they allowed me to work. So thank you to Jacques Bahi, Pierre-Cyrille Héam, Huu Quan Do, Bassam Alkindy, Abdallah Makhoul, Huda Al-Nayyef, Jean-Claude Charr, Jean-François Couchot, Ahmed Mostefaoui, Ahmed Fanfakh, Yousra Ahmed Fadil, Stéphane Domas, Pierre Saenger, Zeinab Fawaz, Arnaud Giersch, Christophe Guyeux, Mourad Hakem, David Laiymani, Gilles Perrot, Christian Salim, Bashar Al-Nuaimi, Santiago Costarelli, Carol Habib, Hassan Moustafa Harb, and Ke Du. I would like to express my thanks and my best wishes to Ingrid Couturier for all the received assistance during my study. I would also like to express my thanks to Dr.~Lilia~Ziane Khodja, PostDoc at $LTAS-A\&M$, Liege, Belgium. I would like to thank Béatrice Domenge and Maxim Moureaux from the {\it Reprographie service} for all received assistance during my study. I would also like to thank Patricia Py, Computer Science Department secretary. I dare not risk missing to mention anyone’s name, so I will simply say 'thank you ALL for being there for me'.

 
 

-Last but not the least, I would like to extend a warm appreciation to those families and friends who have been encouraging along the way.
\ No newline at end of file
+Last but not the least, I would like to extend a warm appreciation to those families and friends who have been encouraging along the way.
+       

diff --git a/entete.tex b/entete.tex
index e812f1011910ced42acc64ac544af9d71b7549af..5c2217127071cefba77b4510ca02af151f07b114 100644 (file)
--- a/entete.tex
+++ b/entete.tex
@@ -33,6 +33,13 @@
 \usepackage{picture}
 \usepackage[section]{placeins}
 \usepackage{float}
 \usepackage{picture}
 \usepackage[section]{placeins}
 \usepackage{float}

+\usepackage{afterpage}
+
+\newcommand\blankpage{%
+    \null
+    \thispagestyle{empty}%
+    \addtocounter{page}{-1}%
+    \newpage}

 
 \def\setgrouptext#1{\gdef\grouptext{#1}}
 \newenvironment{groupeditems}{\begin{displaymath}\left.\vbox\bgroup\setgrouptext}{%
 
 \def\setgrouptext#1{\gdef\grouptext{#1}}
 \newenvironment{groupeditems}{\begin{displaymath}\left.\vbox\bgroup\setgrouptext}{%