Update by Ali

diff --git a/CHAPITRE_02.tex b/CHAPITRE_02.tex
index 73e685f3fb8c3bafbd32986190b589202b859f36..b306630203b4b3ccf89e7abd6c670afc7d57e4d4 100755 (executable)
--- a/CHAPITRE_02.tex
+++ b/CHAPITRE_02.tex
@@ -92,17 +92,17 @@ Their work builds upon previous work in~\cite{ref116} and the  generated cover s
 
 
 The authors in~\cite{ref115} proposed  a heuristic  to compute  the  disjoint  set covers  (DSC).  In order  to compute the maximum number of  covers, they first transform DSC into a maximum-flow problem, which  is then formulated  as a  mixed integer programming  problem (MIP).  Based on  the solution  of the  MIP, they design a heuristic to compute  the final number of covers. The results show  a slight  performance  improvement  in terms  of  the number  of produced  DSC in comparison  to~\cite{ref116}, but it incurs  higher execution  time due to  the complexity of  the mixed integer programming solving. Zorbas  et  al.  \cite{ref228}  presented  B\{GOP\},  a  centralized target coverage  algorithm  introducing   sensor   candidate  categorization depending on their  coverage status and the notion  of critical target to  call  targets   that  are  associated  with  a   small  number  of sensors. The total running time of their heuristic is $0(m n^2)$ where
 
 
 The authors in~\cite{ref115} proposed  a heuristic  to compute  the  disjoint  set covers  (DSC).  In order  to compute the maximum number of  covers, they first transform DSC into a maximum-flow problem, which  is then formulated  as a  mixed integer programming  problem (MIP).  Based on  the solution  of the  MIP, they design a heuristic to compute  the final number of covers. The results show  a slight  performance  improvement  in terms  of  the number  of produced  DSC in comparison  to~\cite{ref116}, but it incurs  higher execution  time due to  the complexity of  the mixed integer programming solving. Zorbas  et  al.  \cite{ref228}  presented  B\{GOP\},  a  centralized target coverage  algorithm  introducing   sensor   candidate  categorization depending on their  coverage status and the notion  of critical target to  call  targets   that  are  associated  with  a   small  number  of sensors. The total running time of their heuristic is $0(m n^2)$ where

-$n$ is the number of sensors  and $m$ the number of targets. Compared to    algorithm's    results of  Slijepcevic and    Potkonjak \cite{ref116},  their   heuristic  produces  more cover sets with a slight growth rate in execution time.
+$n$ is the number of sensors  and $m$ the number of targets. Compared to    algorithm's    results of  Slijepcevic and    Potkonjak \cite{ref116},  their   heuristic  produces  more cover sets with a slight growth rate in execution time. More recently, Deschinkel and Hakem \cite{229} introduced  a near-optimal heuristic algorithm for solving the target coverage problem in WSN. The sensor nodes are organized into disjoint cover sets by the resolution an integer programming problem. Each cover set is capable of monitoring all the targets of the region of interest. Those covers sets are scheduled periodically. Their algorithm  able to construct the different cover sets in parallel. The results show that their algorithm achieves near-optimal solutions compared to the optimal ones obtained by the exact method.

 
 
 
 
 
 
 
 

-
-
-
-
-
-In the case of non-disjoint algorithms~\cite{ref117}, sensors may participate in more than one  cover set. In some cases, this may prolong the lifetime of the network in comparison  to the disjoint cover set algorithms, but designing  algorithms for  non-disjoint cover  sets generally  induces  a higher order  of complexity. Moreover, in  case of  a sensor's  failure, non-disjoint scheduling  policies are less resilient and reliable because a sensor may be involved in more than one cover sets.
+In the case of non-disjoint algorithms~\cite{ref117}, sensors may participate in more than one  cover set. In some cases, this may prolong the lifetime of the network in comparison  to the disjoint cover set algorithms, but designing  algorithms for  non-disjoint cover  sets generally  induces  a higher order  of complexity. Moreover, in  case of a sensor's  failure, non-disjoint scheduling  policies are less resilient and reliable because a sensor may be involved in more than one cover sets. For instance,  Cardei et al.~\cite{ref167}
+present a  linear programming (LP)  solution and a greedy  approach to
+extend the  sensor network lifetime  by organizing the sensors  into a
+maximal  number of  non-disjoint cover  sets. Simulation  results show
+that by allowing sensors to  participate in multiple sets, the network
+lifetime increases compared with related work~\cite{ref115}.

     
     
    
     
     
    

diff --git a/INTRODUCTION.tex b/INTRODUCTION.tex
index 0921b966ca7c338fbfd65e3abc1ab3bf208d63b1..405b9c4885a34e3e3802292ab34106fc2dfb1398 100755 (executable)
--- a/INTRODUCTION.tex
+++ b/INTRODUCTION.tex
@@ -3,45 +3,55 @@
 \addcontentsline{toc}{chapter}{Introduction }
 
 %%-------------------------------------------------------------------------------------------------------%%
 \addcontentsline{toc}{chapter}{Introduction }
 
 %%-------------------------------------------------------------------------------------------------------%%

- \section{General Introduction}
-The enormous development of wireless networks, and the emergence of fourth and fifth-generation technology are leading to the provision of various services to customers around the world that make the Internet a more widely used everywhere. This kind of wireless networks may not be appropriate to be used in some sensitive areas that need to deploy a large number of wireless devices, which are capable of decide and communicate with each other in a distributed way so as to collect the sensed measurements directly from the physical dangerous environment such as volcanoes, nuclear reactors, forest fires, or military battles. Therefore, another type of wireless networks has been emerged to cope with these challenges, which is called Wireless Sensor Network (WSN). 

 
 

-The WSN is a special case of the ad hoc wireless networks and it consists of a large number of wireless cheap devices are called sensors, which are able to perform the communication, sensing, processing and storage with a limited capability. A WSN can be used by the human to monitor the physical phenomena remotely and without outside intervention. Inside a WSN, the wireless sensor nodes are self-contained units equipped with a radio transceiver, a microcontroller, a small memory, and a power source, usually a battery. These sensor nodes are cooperating together autonomously to perform the assigned tasks without the intervention or control from outside. The distributed self-organization and self-configuration capabilities of wireless sensor nodes make the distributed WSNs enable myriad applications for monitoring, sensing, and controlling the physical world.
+\section*{1. General Introduction}  
+\addcontentsline{toc}{section}{1. General Introduction }
+The enormous development of wireless networks, with the emergence of fourth and fifth-generation technology, are leading to the provision of various services to customers around the world that make the Internet more widely used. This kind of wireless networks may not be appropriate to be used in some sensitive areas that need to deploy a large number of wireless devices, which are able to sense, process, and communicate with each other in a distributed way so as to collect the sensed measurements directly from the physical dangerous environment such as volcanoes, nuclear reactors, forest fires, or military battles. Therefore, a specific type of wireless networks, called Wireless Sensor Network (WSN) has emerged to cope with these challenges. 

 
 

-The rapid advancement in Micro Electro-Mechanical Systems (MEMS), wireless communication hardware, digital electronics, and  system-on-chip has given rise to use large networks of tiny sensors are becoming cheaper and more and more commercially available. The sensor nodes have several limitations, such as the power source, processing capability, bandwidth, uncertainty of sensed data, and the vulnerability of sensor nodes to the physical world. These limitations have been tackled by many researchers during the last years, and consequently, many solutions have been proposed that take these constraints into account on the sensors. Sensor nodes are battery-powered without means, of recharging or replacing, usually due to environmental (hostile or unpractical environments) or cost reasons. Since the batteries are the most important limited resource inside the sensor nodes, therefore, it is desired that the WSNs are deployed with high densities so as to exploit the overlapping sensing regions of some sensor nodes to save energy by turning off some of them during the sensing phase to prolong the network lifetime.
+WSN is an ad hoc wireless networks, which consists of a large number of wireless cheap devices called sensors. Sensor node able to perform  communication, sensing, processing, and storage tasks with a limited capability. It can be used by human to monitor physical phenomena remotely and without any outside intervention. Wireless sensor nodes are self-contained units equipped with a radio transceiver, a microcontroller, a small memory, and a power source, usually a battery. These sensor nodes are cooperating together autonomously to perform the assigned tasks. The distributed self-organization and self-configuration capabilities of wireless sensor nodes enable myriad applications for monitoring, sensing and controlling the physical world.

 
 

-Since the network lifetime depends on sensor lifetime, the power depletion represents the most significant part during designing of the WSN protocols because of the limited capacity of the sensor batteries.  The major goal is to extend the network lifetime, taking into consideration the energy source limitations. Several energy-efficient approaches have been suggested so as to minimize the energy consumption and extend the network lifetime during monitoring a certain area by WSN. For example, one of the ways is to turn off the redundant sensors and put them in sleep mode to maintain the energy, whilst the active sensors perform the sensing coverage task during their life. Specifically, the energy-efficient protocols, which are proposed in this dissertation focuses on the area coverage problem in WSNs. The major goal of the area coverage problem is to ensure a maximum area coverage ratio for the entire sensing field of the WSN and for a long time as possible. The area coverage problem is closely related to the performance of systems in many applications, such as monitoring the battlefield, target detection, tracking, personal protection, animal habit monitoring, and homeland security.
+The sensor nodes have several limitations, such as the power source, processing capability, bandwidth, uncertainty of sensed data, and the vulnerability of sensor nodes to the physical world. These limitations have been tackled by many researchers during the last years, and consequently, many solutions take these constraints into account on the sensors have been proposed. Sensor nodes are battery-powered without means of recharging or replacing, usually due to environmental (hostile or unpractical environments) or cost reasons. %Since batteries are the most important limited resource inside sensor nodes, it is desirable that WSNs are deployed with high densities so as to exploit the overlapping sensing regions of some sensor nodes to save energy by turning off some of them during the sensing phase to prolong the network lifetime.

 
 

+Since the network lifetime depends on sensor lifetime, the power depletion represents the most significant part during designing of the WSN protocols because of the limited capacity of the sensor batteries.  The major goal is to extend the network lifetime, taking into consideration the energy source limitations. Several energy-efficient approaches have been suggested to minimize the energy consumption and extend the network lifetime during monitoring a certain area by WSN. %For example, one of the ways is to turn off the redundant sensors and put them in sleep mode to maintain the energy, whilst the active sensors perform the sensing coverage task during their life. 
+Specifically, the energy-efficient protocols proposed in this dissertation focuses on the area coverage problem in WSNs. The major goal of the area coverage problem is to ensure monitoring the entire sensing field for a long time as possible. The area coverage problem is closely related to the performance of WSNs in many applications, such as monitoring the battlefield, target detection, tracking, personal protection, animal habit monitoring, and homeland security.

 
 
 
 

-\section{Motivation of the Dissertation}
+\section*{2. Motivation of the Dissertation}
+\addcontentsline{toc}{section}{2. Motivation of the Dissertation }

 One of the fundamental challenges in Wireless Sensor Networks (WSNs) is the coverage preservation and the extension of the network lifetime continuously and effectively when monitoring a certain area (or region) of interest. Since sensor nodes have limited battery life; since it is impossible to replace batteries, especially in remote and hostile
 environments, it is desirable that a WSN should be deployed with high density because spatial redundancy can then be exploited to increase the lifetime of the network. In such a high-density network, if all sensor nodes were to be activated at the same time, the lifetime would be reduced. To extend the lifetime of the network, the main idea is to take advantage of the overlapping sensing regions of some sensor nodes to save energy by turning off some of them during the sensing phase. Obviously, the deactivation of nodes is only relevant if the coverage of the monitored area is not affected.
 
 One of the fundamental challenges in Wireless Sensor Networks (WSNs) is the coverage preservation and the extension of the network lifetime continuously and effectively when monitoring a certain area (or region) of interest. Since sensor nodes have limited battery life; since it is impossible to replace batteries, especially in remote and hostile
 environments, it is desirable that a WSN should be deployed with high density because spatial redundancy can then be exploited to increase the lifetime of the network. In such a high-density network, if all sensor nodes were to be activated at the same time, the lifetime would be reduced. To extend the lifetime of the network, the main idea is to take advantage of the overlapping sensing regions of some sensor nodes to save energy by turning off some of them during the sensing phase. Obviously, the deactivation of nodes is only relevant if the coverage of the monitored area is not affected.
 

-Although many works have introduced in this area, there is still need for a protocol which can schedule sensor nodes in an efficient way with minimum number of sensors and a less communication overhead so as to maintain the coverage and extend the network lifetime as long as possible. The main question is how to reduce the redundancy while maintaining a good coverage with minimum energy consumption?
+Although many works on energy-efficient coverage problem are introduced, there is still need for a protocol, which can schedule sensor nodes in an efficient way with: a minimum number of active sensors and less communication overhead so as to maintain the coverage and extend the network lifetime as long as possible. The main question is how to reduce the redundancy while maintaining a good coverage with minimum energy consumption?

  
 
  
 

-\section{The Objective of this Dissertation}
-The primary objective of this dissertation is to develop energy-efficient distributed optimization protocols in wireless sensor networks that optimize both coverage and network lifetime so as to maintain the coverage and prolong the lifetime of the WSN. The developed protocols should schedule node’ activities (wake up and sleep stages) with the objective of maintaining a good coverage ratio while maximizing the network lifetime.
+\section*{3. The Objective of this Dissertation}
+\addcontentsline{toc}{section}{3. The Objective of this Dissertation}
+The primary objective of this dissertation is to develop energy-efficient distributed optimization protocols in wireless sensor networks that optimize both coverage and network lifetime. The developed protocols should schedule node’ activities (wake up and sleep stages) with the objective of maintaining a good coverage ratio while maximizing the network lifetime.

 
 The proposed protocols should be able to combine two efficient techniques: network leader
 
 The proposed protocols should be able to combine two efficient techniques: network leader

-election and sensor activity scheduling based optimization, where the challenges include how to select the most efficient leader in each subregion and the best representative active nodes, which take the mission of monitoring during the current round.
+election and sensor activity scheduling based optimization, where the challenges include how to select the most efficient leader in each subregion and the best representative active nodes, which take the mission of monitoring during the current period.

 
 

-
- The developed optimization protocols should be able to perform a distributed optimization process on the subregions where the sensor nodes in each subregion collaborate to select the leader by which the optimization algorithm is executed. In addition, the proposed protocols should be able to achieve an effective trade-off between quality of coverage and the consumed energy in each subregion in the sensing field in order to achieve extended network lifetime whilst maintaining adequate coverage ratio. 
-
-\section{The main Contributions of this Dissertation}
+ In addition, the developed optimization protocols should be able to perform a distributed optimization process on the subregions where the sensor nodes in each subregion collaborate to select the leader by which the optimization algorithm is executed. 

  
  

-The coverage problem in WSNs is becoming more and more important for many applications ranging from military applications such as battlefield surveillance to the civilian applications such as health-care surveillance and habitant monitoring. The main contributions in this dissertation concentrate on design a distributed optimization protocols so as to extend the lifetime of the WSNs.  We summarize the main contributions of our research as follows:
+
+\section*{4. Main Contributions of this Dissertation}
+ \addcontentsline{toc}{section}{4. Main Contributions of this Dissertation}
+%The coverage problem in WSNs is becoming more and more important for many applications ranging from military applications such as battlefield surveillance to the civilian applications such as health-care surveillance and habitant monitoring. 
+The main contributions in this dissertation concentrate on design a distributed optimization protocols so as to extend the lifetime of the WSNs.  We summarize the main contributions of our research as follows:

  
 \begin{enumerate} [i)]
  
 \begin{enumerate} [i)]

+\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 the spatial-temporal subdivision. On the one hand, the area of interest is divided into several smaller subregions and, 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 a protocol that focuses on the area coverage problem with the objective of maximizing the network lifetime. Our proposition, the Distributed Lifetime Coverage Optimization (DILCO) protocol, maintains the coverage and improves the lifetime in WSNs. DILCO protocol presented in chapter 4 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 broadcast by a leader to every node of its subregion.
 
  \item We design a protocol that focuses on the area coverage problem with the objective of maximizing the network lifetime. Our proposition, the Distributed Lifetime Coverage Optimization (DILCO) protocol, maintains the coverage and improves the lifetime in WSNs. DILCO protocol presented in chapter 4 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 broadcast by a leader to every node of its subregion.
 

-\item We extend our work that explained in chapter 5 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 4 is an extension of the approach introduced in chapter 3. In DiLCO protocol, the activity scheduling based optimization is planned for each subregion periodically only for one 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.
+\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 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.
+
+%\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 the spatial-temporal subdivision. On the one hand, the area of interest is divided into several smaller subregions and, 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 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 the spatial-temporal subdivision. On the one hand, the area of interest is divided into several smaller subregions and, 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. We  propose a new mathematical  optimization model. Instead of  trying to cover a set of specified points/targets as in most of the methods proposed in the literature, we formulate an integer program based on perimeter coverage of each sensor. The model involves integer variables to capture the deviations between the actual level of coverage and the required  level. So that an optimal scheduling  will be  obtained by  minimizing a  weighted sum  of these deviations. This contribution is demonstrated in Chapter 6.
+\item We have designed a new protocol, called Perimeter-based Coverage Optimization (PeCO), which schedules nodes’ activities (wake up and sleep stages) with the objective of maintaining a good coverage ratio while maximizing the network lifetime. This protocol is applied in a distributed way in regular subregions obtained after partitioning the area of interest in a preliminary step. It works in periods and is based on the resolution of an integer program to select the subset of sensors operating in active status for each period.  We have proposed a new mathematical optimization model. Instead of  trying to cover a set of specified points/targets as in most of the methods proposed in the literature, we formulate an integer program based on perimeter coverage of each sensor. The model involves integer variables to capture the deviations between the actual level of coverage and the required  level. So that an optimal scheduling  will be  obtained by  minimizing a  weighted sum  of these deviations. This contribution is demonstrated in Chapter 6.

 
 

-\item We add an improved model of energy consumption to assess the efficiency of our protocols as well as we conducted extensive simulation experiments, using the discrete event simulator OMNeT++, to demonstrate the  efficiency of our protocols. We compared our proposed distributed optimization protocols to two approaches found in the literature: DESK~\cite{DESK} and GAF~\cite{GAF}, simulation results based on multiple criteria (energy consumption, coverage ratio, network lifetime and so on) show that the proposed protocols can prolong efficiently the network lifetime and improve the coverage performance.
+\item We add an improved model of energy consumption to assess the efficiency of our protocols. We conducted extensive simulation experiments using the discrete event simulator OMNeT++, to demonstrate the  efficiency of our protocols. We compared our proposed distributed optimization protocols to two approaches found in the literature: DESK~\cite{DESK} and GAF~\cite{GAF}, simulation results based on multiple criteria (energy consumption, coverage ratio, network lifetime and so on) show that the proposed protocols can prolong efficiently the network lifetime and improve the coverage performance.

 
 
 \end{enumerate}
 
 
 \end{enumerate}


@@ -53,7 +63,7 @@ The coverage problem in WSNs is becoming more and more important for many applic
 
 % \section{ Refereed Journal and Conference Publications}
  
 
 % \section{ Refereed Journal and Conference Publications}
  

-\section{Dissertation Outline}
+\section*{5. Dissertation Outline}
+\addcontentsline{toc}{section}{5. Dissertation Outline}

 The dissertation is organized as follows: the next chapter presents a scientific background about wireless sensor networks. Chapter 2 states a review of the related literatures to the coverage problem in WSN, the prior works, and the current works. The evaluation tools and optimization solvers have been investigated in Chapter 3. Chapter 4 describes the the proposed DiLCO protocol. Chapter 5 presents the MuDiLCO protocol. The PeCO protocol is illustrated in Chapter 6. Finally, we conclude our work in Chapter 7.
  
 The dissertation is organized as follows: the next chapter presents a scientific background about wireless sensor networks. Chapter 2 states a review of the related literatures to the coverage problem in WSN, the prior works, and the current works. The evaluation tools and optimization solvers have been investigated in Chapter 3. Chapter 4 describes the the proposed DiLCO protocol. Chapter 5 presents the MuDiLCO protocol. The PeCO protocol is illustrated in Chapter 6. Finally, we conclude our work in Chapter 7.
  

- 
\ No newline at end of file

diff --git a/Thesis.toc b/Thesis.toc
index 08f62f17dadea2f154f36511f0fde0a038c6ff0e..24ef221578c9516dee911beea82c0264c12fd387 100755 (executable)
--- a/Thesis.toc
+++ b/Thesis.toc
@@ -4,11 +4,11 @@
 \contentsline {chapter}{List of Tables}{7}{chapter*.3}
 \contentsline {chapter}{List of Algorithms}{9}{chapter*.4}
 \contentsline {chapter}{Introduction }{11}{chapter*.5}
 \contentsline {chapter}{List of Tables}{7}{chapter*.3}
 \contentsline {chapter}{List of Algorithms}{9}{chapter*.4}
 \contentsline {chapter}{Introduction }{11}{chapter*.5}

-\contentsline {section}{\numberline {0.1}General Introduction}{11}{section.0.1}
-\contentsline {section}{\numberline {0.2}Motivation of the Dissertation}{12}{section.0.2}
-\contentsline {section}{\numberline {0.3}The Objective of this Dissertation}{12}{section.0.3}
-\contentsline {section}{\numberline {0.4}The main Contributions of this Dissertation}{13}{section.0.4}
-\contentsline {section}{\numberline {0.5}Dissertation Outline}{14}{section.0.5}
+\contentsline {section}{1. General Introduction }{11}{section*.6}
+\contentsline {section}{2. Motivation of the Dissertation }{12}{section*.7}
+\contentsline {section}{3. The Objective of this Dissertation}{12}{section*.8}
+\contentsline {section}{4. Main Contributions of this Dissertation}{12}{section*.9}
+\contentsline {section}{5. Dissertation Outline}{14}{section*.10}

 \contentsline {part}{I\hspace {1em}Scientific Background}{15}{part.1}
 \contentsline {chapter}{\numberline {1}Wireless Sensor Networks}{17}{chapter.1}
 \contentsline {section}{\numberline {1.1}Introduction}{17}{section.1.1}
 \contentsline {part}{I\hspace {1em}Scientific Background}{15}{part.1}
 \contentsline {chapter}{\numberline {1}Wireless Sensor Networks}{17}{chapter.1}
 \contentsline {section}{\numberline {1.1}Introduction}{17}{section.1.1}


@@ -42,10 +42,10 @@
 \contentsline {chapter}{\numberline {2}Related Works on Coverage Problems}{41}{chapter.2}
 \contentsline {section}{\numberline {2.1}Introduction}{41}{section.2.1}
 \contentsline {section}{\numberline {2.2}Centralized Algorithms}{43}{section.2.2}
 \contentsline {chapter}{\numberline {2}Related Works on Coverage Problems}{41}{chapter.2}
 \contentsline {section}{\numberline {2.1}Introduction}{41}{section.2.1}
 \contentsline {section}{\numberline {2.2}Centralized Algorithms}{43}{section.2.2}

-\contentsline {section}{\numberline {2.3}Distributed Algorithms}{45}{section.2.3}
-\contentsline {subsection}{\numberline {2.3.1}GAF}{46}{subsection.2.3.1}
-\contentsline {subsection}{\numberline {2.3.2}DESK}{48}{subsection.2.3.2}
-\contentsline {section}{\numberline {2.4}Conclusion}{49}{section.2.4}
+\contentsline {section}{\numberline {2.3}Distributed Algorithms}{46}{section.2.3}
+\contentsline {subsection}{\numberline {2.3.1}GAF}{47}{subsection.2.3.1}
+\contentsline {subsection}{\numberline {2.3.2}DESK}{49}{subsection.2.3.2}
+\contentsline {section}{\numberline {2.4}Conclusion}{50}{section.2.4}

 \contentsline {chapter}{\numberline {3}Evaluation Tools and Optimization Solvers}{53}{chapter.3}
 \contentsline {section}{\numberline {3.1}Introduction}{53}{section.3.1}
 \contentsline {section}{\numberline {3.2}Evaluation Tools}{53}{section.3.2}
 \contentsline {chapter}{\numberline {3}Evaluation Tools and Optimization Solvers}{53}{chapter.3}
 \contentsline {section}{\numberline {3.1}Introduction}{53}{section.3.1}
 \contentsline {section}{\numberline {3.2}Evaluation Tools}{53}{section.3.2}


@@ -103,4 +103,4 @@
 \contentsline {chapter}{\numberline {7}Conclusion and Perspectives}{125}{chapter.7}
 \contentsline {section}{\numberline {7.1}Conclusion}{125}{section.7.1}
 \contentsline {section}{\numberline {7.2}Perspectives}{126}{section.7.2}
 \contentsline {chapter}{\numberline {7}Conclusion and Perspectives}{125}{chapter.7}
 \contentsline {section}{\numberline {7.1}Conclusion}{125}{section.7.1}
 \contentsline {section}{\numberline {7.2}Perspectives}{126}{section.7.2}

-\contentsline {part}{Bibliographie}{141}{chapter*.6}
+\contentsline {part}{Bibliographie}{141}{chapter*.11}

diff --git a/bib.bib b/bib.bib
index 4fded24895f0baccb8adb37d947f8c4ba5cb75f2..8c815d9cbd06013c8b8c7786e3daa74b34658880 100755 (executable)
--- a/bib.bib
+++ b/bib.bib
@@ -2151,3 +2151,10 @@ ISSN={2153-0025},}
  
 } 
 
  
 } 
 

+@inproceedings{ref229,
+  title={A near optimal algorithm for lifetime optimization in wireless sensor networks},
+  author={Deschinkel, Karine and Hakem, Mourad},
+  booktitle={SENSORNETS 2013, 2nd Int. Conf. on Sensor Networks},
+  pages={197--202},
+  year={2013}
+}
\ No newline at end of file