X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/ThesisAli.git/blobdiff_plain/b526efd6a44846d1d89a70fedae4bf9bd2058885..a5b27a679ef2809581fb27959ecfcf348dcbbe39:/entete.tex?ds=inline

diff --git a/entete.tex b/entete.tex
index 61f2137..2eca775 100644
--- a/entete.tex
+++ b/entete.tex
@@ -30,10 +30,13 @@
 %\usepackage{subfig}
 \usepackage{multirow}
 \usepackage{array}
-
+\usepackage{picture}
 \usepackage[section]{placeins}
 \usepackage{float}
 
+\def\setgrouptext#1{\gdef\grouptext{#1}}
+\newenvironment{groupeditems}{\begin{displaymath}\left.\vbox\bgroup\setgrouptext}{%
+  \egroup\right\rbrace\hbox{\grouptext}\end{displaymath}}
 
 \newcommand*\rot{\rotatebox{90}}
 \newcommand*\OK{\ding{51}}
@@ -64,33 +67,38 @@
 %% 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}
-
  
 %%--------------------
 %% Set the author of the PhD thesis
 %%\addauthor[email]{PrÃ©nom}{Nom}
 \addauthor[aidness.ali@univ-fcomte.fr]{Ali Kadhum}{IDREES}
 
- %\iffalse
+ \iffalse
 %%--------------------
 %% Add a member of the jury
 %% \addjury{Firstname}{Lastname}{Role in the jury}{Position}
-\addjury{x1}{y1}{Examiner}{Professor at University of}
-\addjury{x2}{y2}{Examiner}{Professor at University of}
-\addjury{x3}{y3}{Examiner}{Professor at University of}
+\addjury{Sylvain}{CONTASSOT-VIVIER} {Examiner}{Professor at University of Lorraine}
+\addjury{Ye-Qiong}{SONG}{Reviewer} {Professor at University of Lorraine}
+\addjury{Hamida}{SEBA}{Reviewer}{Associate Professor (HDR) at University of Claude Bernard Lyon1}
 %\addjury{x4}{y4}{Examiner}{Professor at University of}
 \addjury{RaphaÃ«l}{Couturier}{Supervisor}{Professor at University of Franche-Comt\'e}
-\addjury{Karine}{Deschinkel}{Co-Supervisor}{Assistant Prof. at University of Franche-Comt\'e}
-\addjury{Michel}{Salomon}{Co-Supervisor}{Assistant Prof. at University of Franche-Comt\'e}
-
- %\fi
+\addjury{Karine}{Deschinkel}{Co-Supervisor}{Assistant Professor at University of Franche-Comt\'e}
+\addjury{Michel}{Salomon}{Co-Supervisor}{Assistant Professor at University of Franche-Comt\'e}
+\fi
+ \addjury {} {Prof Ye-Qiong SONG} {University of Lorraine} {Reviewer}
+\addjury{} {Assoc Prof Hamida SEBA (HDR)} {University of Claude Bernard Lyon1} {Reviewer}
+\addjury {}{Prof Sylvain CONTASSOT-VIVIER} {University of Lorraine} {Examiner}
+\addjury {} {Prof RaphaÃ«l Couturier} {University of Franche-Comt\'e} {Supervisor}
+\addjury {} {Asst Prof Karine Deschinkel} {University of Franche-Comt\'e} { Co-supervisor}
+\addjury {} {Asst Prof Michel Salomon} {University of Franche-Comt\'e} {Co-supervisor}
+ 
  
 % Supervisors:\\
 %Committee:\\
 %%--------------------
 %% Set the English abstract
 \thesisabstract[english]{
-In this dissertation, we focus on the area coverage problem, energy-efficiency is also the foremost requirement. We have considered distributed optimization protocols with the ultimate objective of prolonging the network lifetime. To address this problem, this dissertation proposes two-step approaches. Firstly, the sensing field is divided into smaller subregions using the concept of divide-and-conquer method. Secondly, one of our proposed distributed optimization protocols is distributed and applied on the sensor nodes in each  subregion so as to optimize the coverage and the lifetime performances. Three coverage optimization protocols are proposed, They combine two  efficient techniques: leader election for each subregion, followed by an optimization-based planning of sensor activity scheduling for  each subregion. Extensive simulations are conducted using the discrete event simulator OMNeT++ to validate the efficiency of each of our proposed protocols. We refer to the characteristics of a Medusa II sensor for the energy consumption and the time computation. In comparison with two other existing methods, our protocols are able to increase the WSN lifetime and provide improved coverage performance.
+In this dissertation, we focus on the area coverage problem, energy-efficiency is also the foremost requirement. We have considered distributed optimization protocols with the ultimate objective of prolonging the network lifetime. To address this problem, this dissertation proposes two-step approaches. Firstly, the sensing field is divided into smaller subregions using the concept of divide-and-conquer method. Secondly, one of our proposed distributed optimization protocols is distributed and applied on the sensor nodes in each  subregion so as to optimize the coverage and the lifetime performances. Three coverage optimization protocols are proposed. They combine two  efficient techniques: leader election for each subregion, followed by an optimization-based scheduling of sensor activity for  each subregion. This scheduling is carried by formulating and solving linear programs. For the first two protocols, undercoverage and overcoverage of a specified set of points are minimized. For the third protocol, the new proposed model is based on perimeter coverage level. Extensive simulations are conducted using the discrete event simulator OMNeT++ to validate the efficiency of each of our proposed protocols. We refer to the characteristics of a Medusa II sensor for the energy consumption and the time computation. In comparison with two other existing methods, our protocols are able to increase the WSN lifetime and provide improved coverage performance.
 }
 
 \thesiskeywords[english]{ Wireless Sensor Networks, Area Coverage, Network Lifetime, Distributed Optimization, Scheduling.}
@@ -99,7 +107,7 @@ In this dissertation, we focus on the area coverage problem, energy-efficiency i
 %%--------------------
 %% Set the French abstract
 \thesisabstract[french]{
-Dans cette thÃ¨se, nous nous sommes intÃ©ressÃ© au problÃ¨me de la zone de couverture ainsi qu'Ã  l'efficacitÃ© Ã©nergÃ©tique qui est une exigence essentielle dans un rÃ©seau de capteurs sans fil. Nous avons Ã©tudiÃ© des protocoles d'optimisation distribuÃ©s avec l'objectif ultime de prolonger la durÃ©e de vie du rÃ©seau. Pour rÃ©soudre le problÃ¨me, nous avons proposÃ© de nouvelles approches en deux phases. Dans un premier temps, la  rÃ©gion Ã   surveiller est divisÃ©e en petites sous-rÃ©gions en utilisant le concept de la mÃ©thode diviser pour mieux rÃ©gner. Ensuite, l'un de nos protocoles d'optimisation distribuÃ©s est exÃ©cutÃ© par chaque n\oe ud capteur dans chaque sous-rÃ©gion, afin d'optimiser la couverture et la durÃ©e de vie du rÃ©seau. Nous proposons trois protocoles distribuÃ©s qui combinent, chacun, deux techniques efficaces: l'Ã©lection d'un n\oe ud leader dans chaque sous-rÃ©gion, suivie par la mise en oeuvre par celui-ci d'un processus de dÃ©cision via l'optimisation de l'ordonnancement d'activitÃ© des n\oe uds capteurs de sa sous-rÃ©gion. Nous avons effectuÃ© plusieurs simulations en utilisant le simulateur Ã  Ã©vÃ¨nements discrets OMNeT++ pour valider l'efficacitÃ© de nos protocoles proposÃ©s. Nous avons pris en considÃ©ration les caractÃ©ristiques d'un capteur Medusa II pour la consommation d'Ã©nergie et le temps de calcul. En comparaison avec deux autres mÃ©thodes existantes, nos protocoles ont la capacitÃ© d'augmenter la durÃ©e de vie du rÃ©seau de capteurs et d'amÃ©liorer les performances de couverture.
+Dans cette thÃ¨se, nous nous sommes intÃ©ressÃ©s au problÃ¨me de  couverture ainsi qu'Ã  l'efficacitÃ© Ã©nergÃ©tique qui est une exigence essentielle dans un rÃ©seau de capteurs sans fil. Nous avons Ã©tudiÃ© des protocoles d'optimisation distribuÃ©s avec l'objectif  de prolonger la durÃ©e de vie du rÃ©seau. Pour rÃ©soudre le problÃ¨me, nous avons proposÃ© de nouvelles approches articulÃ©es en deux phases. Dans un premier temps, la  rÃ©gion Ã   surveiller est divisÃ©e en petites sous-rÃ©gions en utilisant le concept de la mÃ©thode diviser pour mieux rÃ©gner. Ensuite, l'un de nos protocoles d'optimisation distribuÃ©s est exÃ©cutÃ© par chaque n\oe ud capteur dans chaque sous-rÃ©gion, afin d'optimiser la couverture et la durÃ©e de vie du rÃ©seau. Nous proposons trois protocoles distribuÃ©s qui combinent, chacun, deux techniques efficaces: l'Ã©lection d'un n\oe ud leader dans chaque sous-rÃ©gion, suivie par la mise en oeuvre par celui-ci d'un processus  d'ordonnancement d'activitÃ© des n\oe uds capteurs de sa sous-rÃ©gion. Cet ordonnancement est portÃ© par la formulation et la rÃ©solution de programmes linÃ©aires. Pour les deux premiers protocoles, il s'agit de minimiser simultanÃ©ment la non couverture ou la sous-couverture d'un ensemble de points particuliers. Pour le troisiÃ¨me protocole, le nouveau modÃ¨le propose repose sur la couverture du pÃ©rimÃ¨tre de chacun des capteurs. Nous avons effectuÃ© plusieurs simulations en utilisant le simulateur Ã  Ã©vÃ¨nements discrets OMNeT++ pour valider l'efficacitÃ© de nos protocoles proposÃ©s. Nous avons pris en considÃ©ration les caractÃ©ristiques d'un capteur Medusa II pour la consommation d'Ã©nergie et le temps de calcul. En comparaison avec deux autres mÃ©thodes existantes, nos protocoles ont la capacitÃ© d'augmenter la durÃ©e de vie du rÃ©seau de capteurs et d'amÃ©liorer les performances de couverture.
 
 }
 
