From: ali <ali@ali>
Date: Fri, 8 May 2015 00:19:21 +0000 (+0200)
Subject: Update by Ali
X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/ThesisAli.git/commitdiff_plain/b90ddc92fe317cadc93b6130e57ef6368cd53569?hp=2357863679fe0c8c15b85903f16b2fc13f553811

Update by Ali
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diff --git a/CONCLUSION.tex b/CONCLUSION.tex
index 6706da0..fb930c9 100644
--- a/CONCLUSION.tex
+++ b/CONCLUSION.tex
@@ -7,7 +7,7 @@
 
 \section{Conclusion}
 
-In this dissertation, we have concentrated on on the design of distributed optimization protocols so as to prolong the lifetime of wireless sensor networks. We have addressed the problem of the area coverage and the lifetime optimization in wireless sensor networks. The ultimate goal is the coverage preservation and the extension of the network lifetime continuously and effectively when monitoring a certain area of interest. 
+In this dissertation, we have concentrated on the design of distributed optimization protocols so as to prolong the lifetime of wireless sensor networks. We have addressed the problem of the area coverage and the lifetime optimization in wireless sensor networks. The ultimate goal is the coverage preservation and the extension of the network lifetime continuously and effectively when monitoring a certain area of interest. 
 
 The first part of the dissertation has presented the scientific background including WSNs, brief survey of related works, and evaluation tools as well as optimization solvers.  
 
diff --git a/INTRODUCTION.tex b/INTRODUCTION.tex
index 847f097..715cab3 100644
--- a/INTRODUCTION.tex
+++ b/INTRODUCTION.tex
@@ -43,7 +43,7 @@ The main contributions in this dissertation concentrate on designing distributed
 \item We devise a framework to schedule nodes to be activated alternatively such that the network lifetime is prolonged  while ensuring that a certain level of coverage is preserved. A key idea in our framework is  to exploit a spatial-temporal subdivision. On the one hand, the area of interest is divided into several smaller subregions.On the other hand, the timeline is divided into periods of equal length. In each subregion, the sensor nodes will cooperatively choose a  leader which will schedule  nodes' activities, and this grouping of sensors is similar to typical cluster architecture.
 
 
-\item We design, called the Distributed Lifetime Coverage Optimization (DILCO) protocol, which maintains the coverage and improves the lifetime in WSNs. DILCO protocol is presented in chapter 4. It is an extension of our approach introduced in \cite{ref159}. In \cite{ref159}, the protocol is deployed over only two subregions. In DILCO protocol, the area of interest is first divided into subregions using a divide-and-conquer algorithm and an activity scheduling for sensor nodes is then planned by the elected leader in each subregion. In fact, the nodes in a subregion can be seen as a cluster where each node sends sensing data to the cluster head or the sink node. Furthermore, the activities in a subregion/cluster can continue even if another cluster stops due to too many node failures. DiLCO protocol considers periods, where a period starts with a discovery phase to exchange information between sensors of the same subregion, in order to choose in a suitable manner a sensor node (the leader) to carry out the coverage strategy. In each subregion, the activation of the sensors for the sensing phase of the current period is obtained by solving an integer program. The resulting activation vector is broadcasted by the leader to every node of its subregion.
+\item We design a protocol, called the Distributed Lifetime Coverage Optimization (DILCO) protocol, which maintains the coverage and improves the lifetime in WSNs. DILCO protocol is presented in chapter 4. It is an extension of our approach introduced in \cite{ref159}. In \cite{ref159}, the protocol is deployed over only two subregions. In DILCO protocol, the area of interest is first divided into subregions using a divide-and-conquer algorithm and an activity scheduling for sensor nodes is then planned by the elected leader in each subregion. In fact, the nodes in a subregion can be seen as a cluster where each node sends sensing data to the cluster head or the sink node. Furthermore, the activities in a subregion/cluster can continue even if another cluster stops due to too many node failures. DiLCO protocol considers periods, where a period starts with a discovery phase to exchange information between sensors of the same subregion, in order to choose in a suitable manner a sensor node (the leader) to carry out the coverage strategy. In each subregion, the activation of the sensors for the sensing phase of the current period is obtained by solving an integer program. The resulting activation vector is broadcasted by the leader to every node of its subregion.
 
 \item %We extend our work that explained in chapter 4 and present a generalized framework that can be applied to provide the cover sets of all rounds in each period. 
 The MuDiLCO protocol for Multiround Distributed Lifetime Coverage Optimization protocol, presented in chapter 5, is an extension of the approach introduced in chapter 4. In DiLCO protocol, the activity scheduling based optimization is planned for each subregion periodically only for one sensing round. Whilst, we study the possibility of dividing the sensing phase into multiple rounds. In fact, we make a multiround optimization, while it was a single round optimization in our previous contribution.
diff --git a/entete.tex b/entete.tex
index 3b17d31..68e0256 100644
--- a/entete.tex
+++ b/entete.tex
@@ -86,20 +86,20 @@
 %%--------------------
 %% Set the English abstract
 \thesisabstract[english]{
-In this dissertation, we highly 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. The proposed distributed optimization protocols (including algorithms, models, and solving integer programs) should be energy-efficient protocols. 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. In this dissertation, three coverage optimization protocols are proposed. These protocols combine two  efficient techniques: leader election for each subregion, followed by an optimization-based planning of sensor activity scheduling decisions 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 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.
 }
 
-\thesiskeywords[english]{ Wireless Networks, Wireless Sensor Networks, Area Coverage, Network Lifetime, Optimization, Scheduling, Distributed Algorithms, Centralized Algorithms, Robustness, Connectivity, Energy-efficiency, Heterogeneous Energy Network, Homogeneous Network, Network Simulation, Performance Evaluation, Wireless Green Communications and Networking.}
+\thesiskeywords[english]{ Wireless Sensor Networks, Area Coverage, Network Lifetime, Distributed Optimization, Scheduling.}
 
   
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 %% Set the French abstract
 \thesisabstract[french]{
-Dans cette thèse, nous nous sommes intéressés au problème de la zone de couverture ainsi qu'à l'efficacité énergétique qui est une exigence essentielle dans un réseau de capteurs sans fil. Nous avons étudiés les protocoles d'optimisation distribués avec l'objectif ultime de prolonger la durée de vie du réseau. Les protocoles d'optimisation distribués proposés (y compris les algorithmes, les modèles et la résolution des programmes entiers) doivent être efficaces en terme d'énergie. Pour résoudre ce problème, nous avons proposé de nouvelles approches en deux phases. Dans un premier temps, le champ de surveillance est divisé en petites sous-régions en utilisant le concept de la méthode diviser pour mieux régner. Ensuite, l'un de nos protocoles d'optimisation distribués est appliqué sur les n\oe uds de capteurs dans chaque sous-régions afin d'optimiser la couverture et la durée de vie du réseau. Dans cette thèse, nous avons proposé trois protocoles distribués pour l'optimisation de la couverture. Ces protocoles permettent de combiner deux techniques efficaces: une élection de leader pour chaque sous-région, suivie par un processus d'optimisation de l'ordonnancement d'activité de décisions des capteurs pour chaque sous-région. Nous avons effectué plusieurs simulations en utilisant le simulateur à évènements discrets OMNeT++ pour valider l'efficacité de nos protocoles proposés. Nous avons pris en considération les caractéristiques d'un capteur Medusa II pour la consommation d'énergie et le temps de calcul. En comparaison avec deux autres méthodes existantes, nos protocoles ont la capacité d'augmenter la durée de vie du réseau de capteurs et d'améliorer les performances de couverture.
+Dans cette thèse, nous nous sommes intéressé au problème de la zone de couverture ainsi qu'à l'efficacité énergétique qui est une exigence essentielle dans un réseau de capteurs sans fil. Nous avons étudié des protocoles d'optimisation distribués avec l'objectif ultime de prolonger la durée de vie du réseau. Pour résoudre le problème, nous avons proposé de nouvelles approches en deux phases. Dans un premier temps, la  région à  surveiller est divisée en petites sous-régions en utilisant le concept de la méthode diviser pour mieux régner. Ensuite, l'un de nos protocoles d'optimisation distribués est exécuté par chaque n\oe ud capteur dans chaque sous-région, afin d'optimiser la couverture et la durée de vie du réseau. Nous proposons trois protocoles distribués qui combinent, chacun, deux techniques efficaces: l'élection d'un n\oe ud leader dans chaque sous-région, suivie par la mise en oeuvre par celui-ci d'un processus de décision via l'optimisation de l'ordonnancement d'activité des n\oe uds capteurs de sa sous-région. Nous avons effectué plusieurs simulations en utilisant le simulateur à évènements discrets OMNeT++ pour valider l'efficacité de nos protocoles proposés. Nous avons pris en considération les caractéristiques d'un capteur Medusa II pour la consommation d'énergie et le temps de calcul. En comparaison avec deux autres méthodes existantes, nos protocoles ont la capacité d'augmenter la durée de vie du réseau de capteurs et d'améliorer les performances de couverture.
 
 }
 
-\thesiskeywords[french]{Réseaux sans fil, Réseaux de capteurs sans fil, Zone de couverture, Durée de vie du réseau, Optimisation, Ordonnancement, Algorithmes distribués, Algorithmes centralisés, Robustesse, Connectivité, Efficacité énergétique, \'Energie des réseaux hétérogènes, Réseaux homogènes, Simulation des Réseaux, Evaluation de Performance, Les Communications sans Fil Ecologiques et le Réseautage. }
+\thesiskeywords[french]{Réseaux de capteurs sans fil, Zone de couverture, Durée de vie du réseau, Optimisation distribué, Ordonnancement. }
 
   
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