Update by Ali

diff --git a/Abstruct.tex b/Abstruct.tex
index c8e686db9008c5c52ce934bb7227d8737022f4de..5ef2405894615e2674dc85388582a9e4c6399d0e 100644 (file)
--- a/Abstruct.tex
+++ b/Abstruct.tex
@@ -9,8 +9,8 @@
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 

-\emph{ \begin{center} \Large Distributed Coverage Optimization Techniques for Improving
-          Lifetime of Wireless Sensor Networks \end{center}}   
+\emph{ \begin{center} \Large Distributed coverage optimization techniques for improving
+          lifetime of wireless sensor networks \end{center}}   

 %\emph{ \begin{center} \large By \end{center}}  
 \emph{ \begin{center} \large Ali Kadhum Idrees \\ University of Franche-Comt\'e, 2015 \end{center}} 
 %\emph{ \begin{center} \large The University of Franche-Comt\'e, 2015 \end{center}}  
 %\emph{ \begin{center} \large By \end{center}}  
 \emph{ \begin{center} \large Ali Kadhum Idrees \\ University of Franche-Comt\'e, 2015 \end{center}} 
 %\emph{ \begin{center} \large The University of Franche-Comt\'e, 2015 \end{center}}  


@@ -19,8 +19,7 @@
   
 
 
   
 
 

-Wireless sensor networks (WSNs) have recently received a great deal of research attention due to their wide range of potential applications. Many important characteristics provided by the WSNs make them different from other wireless ad-hoc networks. Furthermore, these characteristics impose lots of limitations that lead to several challenges in the network. These challenges include coverage, topology control, routing, data fusion, security, and many others.  One of the main research challenges faced in wireless sensor networks is to preserve continuously and effectively the coverage of an area of interest to be monitored, while simultaneously preventing as much as possible a network failure due to battery-depleted nodes.
-
+%Wireless sensor networks (WSNs) have recently received a great deal of research attention due to their wide range of potential applications. Many important characteristics provided by the WSNs make them different from other wireless ad-hoc networks. Furthermore, these characteristics impose lots of limitations that lead to several challenges in the network. These challenges include coverage, topology control, routing, data fusion, security, and many others.  One of the main research challenges faced in wireless sensor networks is to preserve continuously and effectively the coverage of an area of interest to be monitored, while simultaneously preventing as much as possible a network failure due to battery-depleted nodes.

 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. 
 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. 


@@ -33,8 +32,7 @@ during which sets of sensor nodes are scheduled to remain active for a number of
 
 
 
 
 
 

-Last but not least, we propose a Perimeter-based Coverage Optimization (PeCO) protocol which is also distributed among sensor nodes in each  subregion.The novelty of our approach lies essentially in the formulation of a new
-mathematical optimization  model based on a perimeter coverage level to schedule sensors' activities, whereas we used primary points coverage model in the two previous models. A new integer program coverage model is solved by the leader during the decision phase so as to provide only one cover set of sensors for the sensing phase.
+Last but not least, we propose a Perimeter-based Coverage Optimization (PeCO) protocol which is also distributed among sensor nodes in each  subregion. The novelty of our approach lies essentially in the formulation of a new mathematical optimization  model based on a perimeter coverage level to schedule sensors' activities, whereas we used primary points coverage model in the two previous models. A new integer program coverage model is solved by the leader during the decision phase so as to provide only one cover set of sensors for the sensing phase.

 
 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.
 
 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.

diff --git a/CHAPITRE_06.tex b/CHAPITRE_06.tex
index 1fb0d60593faae2ef22a537a88c996827537f378..d566e4176f76f81fe7a58e67612c6460b679bf95 100644 (file)
--- a/CHAPITRE_06.tex
+++ b/CHAPITRE_06.tex
@@ -17,27 +17,25 @@
 In this chapter,  we propose an approach called Perimeter-based Coverage Optimization
 protocol (PeCO). 
 %The PeCO protocol merges between two energy efficient mechanisms, which are used the main advantages of the centralized and distributed approaches and avoids the most of their disadvantages. An energy-efficient activity scheduling mechanism based new optimization model is performed by each leader in the subregions. 
 In this chapter,  we propose an approach called Perimeter-based Coverage Optimization
 protocol (PeCO). 
 %The PeCO protocol merges between two energy efficient mechanisms, which are used the main advantages of the centralized and distributed approaches and avoids the most of their disadvantages. An energy-efficient activity scheduling mechanism based new optimization model is performed by each leader in the subregions. 

-The framework is similar to the one described in section \ref{ch4:sec:02:03}, but in this approach, the optimization model is based on the perimeter coverage model in order to producing the optimal cover set of active sensors, which are taken the responsibility of sensing during the current period. 
+The framework is similar to the one described in section \ref{ch4:sec:02:03}. But in this approach, the optimization model is based on the perimeter coverage model in order to produce the optimal cover set of active sensors, which are taken the responsibility of sensing during the current period. 

 
 
 
 

-The rest of the chapter is  organized as follows. The next section is devoted to the PeCO protocol description and section~\ref{ch6:sec:03} focuses on the coverage model formulation which is used  to schedule the activation  of sensor nodes based on perimeter coverage model.  Section~\ref{ch6:sec:04}  presents simulations results and discusses the comparison  with other approaches. Finally, concluding remarks   are  drawn in section~\ref{ch6:sec:05}.
+The rest of the chapter is organized as follows. The next section is devoted to the PeCO protocol description and section~\ref{ch6:sec:03} focuses on the coverage model formulation which is used  to schedule the activation  of sensor nodes.  Section~\ref{ch6:sec:04} presents simulation results and discusses the comparison with other approaches. Finally, concluding remarks   are  drawn in section~\ref{ch6:sec:05}.

 
 
 
 
 
 

-\section{The PeCO Protocol Description}
+\section{Description of the PeCO Protocol}

 \label{ch6:sec:02}
 
 \label{ch6:sec:02}
 

-\noindent  In  this  section,  we  describe in  details  our  Lifetime  Coverage
-Optimization protocol.  First we present the  assumptions we made and the models
-we considered (in particular the perimeter coverage one), second we describe the
-background idea of our protocol, and third  we give the outline of the algorithm
-executed by each node.
+%\noindent  In  this  section,  we  describe in  details  our  Lifetime  Coverage Optimization protocol.  
+First we present the  assumptions we made and the models
+we considered (in particular the perimeter coverage one), second we describe the background idea of our protocol, and third  we give the outline of the algorithm executed by each node.

 
 
 
 \subsection{Assumptions and Models}
 \label{ch6:sec:02:01}
 
 
 
 \subsection{Assumptions and Models}
 \label{ch6:sec:02:01}

-The PeCO protocol uses the same assumptions and network model that presented in section \ref{ch4:sec:02:01}.
+The PeCO protocol uses the same assumptions and network model than both DiLCO and MuDiLCO protocols. All the hypotheses can be found in section \ref{ch4:sec:02:01}.

 The PeCO protocol  uses the  same perimeter-coverage  model as  Huang and Tseng in~\cite{ref133}. It  can be expressed as follows:  a sensor is said to be a perimeter covered if all the points on its  perimeter are covered by at least  one sensor  other than  itself.  They  proved that  a network  area is
 $k$-covered if and only if each sensor in the network is $k$-perimeter-covered (perimeter covered by at least $k$ sensors).
   
 The PeCO protocol  uses the  same perimeter-coverage  model as  Huang and Tseng in~\cite{ref133}. It  can be expressed as follows:  a sensor is said to be a perimeter covered if all the points on its  perimeter are covered by at least  one sensor  other than  itself.  They  proved that  a network  area is
 $k$-covered if and only if each sensor in the network is $k$-perimeter-covered (perimeter covered by at least $k$ sensors).
   


@@ -226,14 +224,12 @@ section.
 
 First, we have the following sets:
 \begin{itemize}
 
 First, we have the following sets:
 \begin{itemize}

-\item $J$ represents the set of WSN sensor nodes;
+\item $J$ represents the set of sensor nodes;

 \item $A \subseteq J $ is the subset of alive sensors;
 \item  $I_j$  designates  the  set  of  coverage  intervals  (CI)  obtained  for
 \item $A \subseteq J $ is the subset of alive sensors;
 \item  $I_j$  designates  the  set  of  coverage  intervals  (CI)  obtained  for

-  sensor~$j$.
+  sensor~$j$, which have been defined according to the  method introduced in section~\ref{ch6:sec:02:01}.

 \end{itemize}
 \end{itemize}

-$I_j$ refers to the set of  coverage intervals which have been defined according
-to the  method introduced in section~\ref{ch6:sec:02:01}. For a coverage  interval $i$,
-let $a^j_{ik}$ denotes  the indicator function of whether  sensor~$k$ is involved
+First, for a coverage  interval $i$, let $a^j_{ik}$ denotes  the indicator function of whether  sensor~$k$ is involved

 in coverage interval~$i$ of sensor~$j$, that is:
 \begin{equation}
 a^j_{ik} = \left \{ 
 in coverage interval~$i$ of sensor~$j$, that is:
 \begin{equation}
 a^j_{ik} = \left \{ 


@@ -332,8 +328,7 @@ interest of $(50 \times 25)~m^2 $ in such a way that they cover the field with a
 %Each node has an  initial energy level, in Joules, which is randomly drawn in the interval $[500-700]$. If its energy provision reaches a value below  the threshold $E_{th}=36$~Joules,  the minimum energy needed  for a node  to stay  active during  one period,  it will no more  participate in the coverage task. This value corresponds to the energy needed by the sensing phase, obtained by multiplying the energy consumed in active state (9.72 mW) with the time in seconds for one  period (3600 seconds), and  adding the energy  for the pre-sensing phases. According  to the interval of initial energy,  a sensor may be active during at most 20 periods.
 
 
 %Each node has an  initial energy level, in Joules, which is randomly drawn in the interval $[500-700]$. If its energy provision reaches a value below  the threshold $E_{th}=36$~Joules,  the minimum energy needed  for a node  to stay  active during  one period,  it will no more  participate in the coverage task. This value corresponds to the energy needed by the sensing phase, obtained by multiplying the energy consumed in active state (9.72 mW) with the time in seconds for one  period (3600 seconds), and  adding the energy  for the pre-sensing phases. According  to the interval of initial energy,  a sensor may be active during at most 20 periods.
 
 

-The values  of $\alpha^j_i$ and  $\beta^j_i$ have been chosen to ensure a good network coverage and a longer WSN lifetime as shown in Table \ref{my-beta-alfa}.  We have given a higher priority to the undercoverage  (by  setting  the  $\alpha^j_i$ with  a  larger  value  than $\beta^j_i$)  so as  to prevent  the non-coverage  for the  interval~$i$ of  the
-sensor~$j$.  On the  other hand,  we have assigned to
+The values  of $\alpha^j_i$ and  $\beta^j_i$ have been chosen to ensure a good network coverage and a longer WSN lifetime as shown in Table \ref{my-beta-alfa}. We set the values  of $\alpha^j_i$ and  $\beta^j_i$ to 0.6 and 0.4 respectively.  We have given a higher priority to the undercoverage  (by  setting  the  $\alpha^j_i$ with  a  larger  value  than $\beta^j_i$)  so as  to prevent  the non-coverage  for the  interval~$i$ of  the sensor~$j$.  On the  other hand,  we have assigned to

 $\beta^j_i$ a value which is slightly lower so as to minimize the number of active sensor nodes which contribute in covering the interval.
 
 \begin{table}[h]
 $\beta^j_i$ a value which is slightly lower so as to minimize the number of active sensor nodes which contribute in covering the interval.
 
 \begin{table}[h]


@@ -357,15 +352,16 @@ $\alpha^j_i$ & $\beta^j_i$ & $Lifetime_{50}$ & $Lifetime_{95}$ \\ \hline
 \end{tabular}
 \end{table}
 
 \end{tabular}
 \end{table}
 

-With the performance metrics, described in section \ref{ch4:sec:04:04}, we evaluate the efficiency of our approach. We use the modeling language and the optimization solver which are mentioned in section \ref{ch4:sec:04:02}. In addition, we use the same energy consumption model, presented in section \ref{ch4:sec:04:03}.
+With the performance metrics, described in section \ref{ch4:sec:04:04}, we evaluate the efficiency of our approach. We use the modeling language and the optimization solver which are mentioned in section \ref{ch4:sec:04:02}. In addition, we use the same energy consumption model, as previously, described in section \ref{ch4:sec:04:03}.

 
 
 \subsection{Simulation Results}
 \label{ch6:sec:04:02}
 
 
 
 \subsection{Simulation Results}
 \label{ch6:sec:04:02}
 

-In  order to  assess and  analyze  the  performance  of  our protocol  we  have implemented PeCO protocol in  OMNeT++~\cite{ref158} simulator.  Besides PeCO, three other protocols,  described in  the next paragraph,  will  be  evaluated for comparison purposes. 
+In  order to  assess and  analyze  the  performance  of  our protocol  we  have implemented PeCO protocol in  OMNeT++~\cite{ref158} simulator.  
+%Besides PeCO, three other protocols,  described in  the next paragraph,  will  be  evaluated for comparison purposes. 

 %The simulations were run  on a laptop DELL with an Intel Core~i3~2370~M (2.4~GHz) processor (2  cores) whose MIPS  (Million Instructions Per Second) rate  is equal to 35330. To  be consistent with the use  of a sensor node based on  Atmels AVR ATmega103L microcontroller (6~MHz) having  a MIPS rate equal to 6, the original execution time  on the laptop is  multiplied by 2944.2 $\left(\frac{35330}{2} \times  \frac{1}{6} \right)$.  The modeling  language for Mathematical Programming (AMPL)~\cite{AMPL} is  employed to generate the integer program instance  in a  standard format, which  is then read  and solved  by the optimization solver  GLPK (GNU  linear Programming Kit  available in  the public domain) \cite{glpk} through a Branch-and-Bound method.
 %The simulations were run  on a laptop DELL with an Intel Core~i3~2370~M (2.4~GHz) processor (2  cores) whose MIPS  (Million Instructions Per Second) rate  is equal to 35330. To  be consistent with the use  of a sensor node based on  Atmels AVR ATmega103L microcontroller (6~MHz) having  a MIPS rate equal to 6, the original execution time  on the laptop is  multiplied by 2944.2 $\left(\frac{35330}{2} \times  \frac{1}{6} \right)$.  The modeling  language for Mathematical Programming (AMPL)~\cite{AMPL} is  employed to generate the integer program instance  in a  standard format, which  is then read  and solved  by the optimization solver  GLPK (GNU  linear Programming Kit  available in  the public domain) \cite{glpk} through a Branch-and-Bound method.

-As said previously, the PeCO is  compared with three other approaches. The first one,  called  DESK,  is  a  fully distributed  coverage  algorithm  proposed  by \cite{DESK}. The second one,  called GAF~\cite{GAF}, consists in dividing  the monitoring  area into  fixed  squares. Then,  during the  decision phase, in each square, one sensor is  chosen to remain active during the sensing phase. The last  one, the DiLCO protocol~\cite{Idrees2}, is  an improved version of a research work we presented in~\cite{ref159}. Let us notice that PeCO and  DiLCO protocols are  based on the  same framework. In  particular, the choice for the simulations of a partitioning in 16~subregions was chosen because it corresponds to the configuration producing the better results for DiLCO. The protocols are distinguished from one another by the formulation  of the integer program providing the set of sensors which have to be activated in each sensing phase. DiLCO protocol tries to satisfy the coverage of a set of primary points, whereas PeCO protocol objective is to reach a desired level of coverage for each sensor perimeter. In our experimentations, we chose a level of coverage equal to one ($l=1$).
+PeCO protocol is  compared with three other approaches. DESK \cite{DESK}, GAF~\cite{GAF}, and DiLCO~\cite{Idrees2} is  an improved version of a research work we presented in~\cite{ref159}, where DiLCO protocol is described in chapter 4. Let us notice that PeCO and  DiLCO protocols are  based on the  same framework. In  particular, the choice for the simulations of a partitioning in 16~subregions was chosen because it corresponds to the configuration producing the better results for DiLCO. The protocols are distinguished from one another by the formulation  of the integer program providing the set of sensors which have to be activated in each sensing phase. DiLCO protocol tries to satisfy the coverage of a set of primary points, whereas PeCO protocol objective is to reach a desired level of coverage for each sensor perimeter. In our experimentations, we chose a level of coverage equal to one ($l=1$).

 
 
 
 
 
 


@@ -413,7 +409,7 @@ Figure \ref{fig333}.
 \label{fig444}
 \end{figure} 
 
 \label{fig444}
 \end{figure} 
 

-\subsubsection{The Energy Consumption}
+\subsubsection{Energy Consumption}

 \label{ch6:sec:04:02:03}
 
 We studied the effect of the energy  consumed by the WSN during the communication,
 \label{ch6:sec:04:02:03}
 
 We studied the effect of the energy  consumed by the WSN during the communication,


@@ -422,12 +418,9 @@ and  compared  it for  the  four  approaches.  Figures~\ref{fig3EC}(a)  and  (b)
 illustrate  the  energy   consumption  for  different  network   sizes  and  for
 $Lifetime95$ and  $Lifetime50$. The results show  that our PeCO protocol  is the
 most competitive  from the energy  consumption point of  view. As shown  in both
 illustrate  the  energy   consumption  for  different  network   sizes  and  for
 $Lifetime95$ and  $Lifetime50$. The results show  that our PeCO protocol  is the
 most competitive  from the energy  consumption point of  view. As shown  in both

-figures, PeCO consumes much less energy than the three other methods.  One might
-think that the  resolution of the integer  program is too costly  in energy, but
-the  results show  that it  is very  beneficial to  lose a  bit of  time in  the
-selection of  sensors to  activate.  Indeed the  optimization program  allows to
-reduce significantly the number of active  sensors and so the energy consumption
-while keeping a good coverage level.
+figures, PeCO consumes much less energy than the three other methods.  \\ \\ \\ \\ \\ \\
+
+One might think that the  resolution of the integer  program is too costly  in energy, but the  results show  that it  is very  beneficial to  lose a  bit of time in the selection of  sensors to  activate.  Indeed the optimization program  allows to reduce significantly the number of active  sensors and so the energy consumption while keeping a good coverage level.

 
 \begin{figure}[h!]
   \centering
 
 \begin{figure}[h!]
   \centering


@@ -441,20 +434,11 @@ while keeping a good coverage level.
 
 
 
 
 
 

-\subsubsection{The Network Lifetime}
+\subsubsection{Network Lifetime}

 \label{ch6:sec:04:02:04}
 
 \label{ch6:sec:04:02:04}
 

-We observe the superiority of PeCO and DiLCO protocols in comparison with the
-two    other   approaches    in    prolonging   the    network   lifetime.    In
-Figures~\ref{fig3LT}(a)  and (b),  $Lifetime95$ and  $Lifetime50$ are  shown for
-different  network  sizes.   As  highlighted  by  these  figures,  the  lifetime
-increases with the size  of the network, and it is clearly   largest for DiLCO
-and PeCO  protocols.  For instance,  for a  network of 300~sensors  and coverage
-ratio greater than 50\%, we can  see on Figure~\ref{fig3LT}(b) that the lifetime
-is about twice longer with  PeCO compared to DESK protocol.  The performance
-difference    is    more    obvious   in    Figure~\ref{fig3LT}(b)    than    in
-Figure~\ref{fig3LT}(a) because the gain induced  by our protocols increases with
- time, and the lifetime with a coverage  of 50\% is far  longer than with
+We observe the superiority of PeCO and DiLCO protocols in comparison with the two other   approaches in  prolonging the network lifetime. In
+Figures~\ref{fig3LT}(a)  and (b),  $Lifetime95$ and  $Lifetime50$ are  shown for different  network  sizes.   As  highlighted  by  these  figures,  the  lifetime increases with the size  of the network, and it is clearly   largest for DiLCO and PeCO  protocols.  For instance,  for a  network of 300~sensors  and coverage ratio greater than 50\%, we can  see on Figure~\ref{fig3LT}(b) that the lifetime is about twice longer with  PeCO compared to DESK protocol.  The performance difference    is    more    obvious   in    Figure~\ref{fig3LT}(b) than in Figure~\ref{fig3LT}(a) because the gain induced  by our protocols increases with  time, and the lifetime with a coverage  of 50\% is far  longer than with

 95\%. 
 
 \begin{figure} [p]
 95\%. 
 
 \begin{figure} [p]

diff --git a/CONCLUSION.tex b/CONCLUSION.tex
index fb930c9eb48c9e1aabed71f7fff90c27d8f487b3..7485fe19eb2095cc9cf6b860737a5fc3abf77b96 100644 (file)
--- a/CONCLUSION.tex
+++ b/CONCLUSION.tex
@@ -14,19 +14,19 @@ The first part of the dissertation has presented the scientific background inclu
 In chapter 1, We started with a general overview on wireless sensor networks. We have described various concepts, mechanisms, types, applications, and challenges in WSNs. Several energy-efficient techniques so as to improve the network lifetime of WSNs have been presented. The coverage problem, the network lifetime, and the energy consumption modeling in WSNs have been explained. A brief survey about literature on coverage algorithms is achieved in chapter 2.
 We have classified those works into centralized and distributed algorithms. We have given a brief comparison of the main characteristics of each approach. Finally we have included in chapter 3 a comparative study of different evaluation tools dedicated to WSNs. In addition, we have illustrated various commercial and free optimization solvers considering the main features of each one.
 
 In chapter 1, We started with a general overview on wireless sensor networks. We have described various concepts, mechanisms, types, applications, and challenges in WSNs. Several energy-efficient techniques so as to improve the network lifetime of WSNs have been presented. The coverage problem, the network lifetime, and the energy consumption modeling in WSNs have been explained. A brief survey about literature on coverage algorithms is achieved in chapter 2.
 We have classified those works into centralized and distributed algorithms. We have given a brief comparison of the main characteristics of each approach. Finally we have included in chapter 3 a comparative study of different evaluation tools dedicated to WSNs. In addition, we have illustrated various commercial and free optimization solvers considering the main features of each one.
 

-In the second part of the dissertation, We have designed three new different optimization protocols, which schedules nodes’ activities (wake up and sleep stages) with the objective of maintaining a good coverage ratio while maximizing the network lifetime. We propose two-step approaches. Firstly, the field of sensing is divided into smaller subregions using the concept of divide-and-conquer method. Secondly, one of the proposed optimization protocols is applied in each subregion in a distributed parallel way to optimize both coverage and lifetime performances. The proposed protocols combine two efficient mechanisms: network leader election and sensor activity scheduling, where the challenges include how to select the most efficient leader in each subregion, the best
+In the second part of the dissertation, We design three new different optimization protocols, which schedules nodes’ activities (wake up and sleep stages) with the objective of maintaining a good coverage ratio while maximizing the network lifetime. We propose two-step approaches. Firstly, the field of sensing is divided into smaller subregions using the concept of divide-and-conquer method. Secondly, one of the proposed optimization protocols is applied in each subregion in a distributed parallel way to optimize both coverage and lifetime performances. The proposed protocols combine two efficient mechanisms: network leader election and sensor activity scheduling, where the challenges include how to select the most efficient leader in each subregion, the best

 representative active nodes that will optimize the network lifetime while taking the responsibility of covering the corresponding subregion.
 
 
 
 representative active nodes that will optimize the network lifetime while taking the responsibility of covering the corresponding subregion.
 
 
 

-In chapter 4, we have proposed an optimization protocol called Distributed Lifetime Coverage Optimization protocol (DiLCO), which optimizes the coverage and the lifetime of a wireless sensor network. DiLCO protocol is distributed in each sensor node in the subregions of the sensing field. It is implemented in each subregion simultaneously and independently. The proposed DiLCO protocol is a periodic protocol where each period consists of 4 phases: information exchange, leader election, decision, and sensing.  The sensor nodes collaborate in each subregion to elect the leader. The leader applies activity scheduling based optimization in order to provide only one optimal set of active sensor nodes that takes the mission of sensing during this period. The performance of our DiLCO protocol has been evaluated by a series of simulations. We have presented a comparison between our proposed protocol and two other existing protocols known in the literature: DESK and GAF. The experimental results have validated our protocol and showed its efficiency in the optimization of the coverage and the lifetime compared to the two benchmarking methods.
+In chapter 4, we  propose an optimization protocol called Distributed Lifetime Coverage Optimization protocol (DiLCO), which optimizes the coverage and the lifetime of a wireless sensor network. DiLCO protocol is distributed in each sensor node in the subregions of the sensing field. It is implemented in each subregion simultaneously and independently. The proposed DiLCO protocol is a periodic protocol where each period consists of 4 phases: information exchange, leader election, decision, and sensing.  The sensor nodes collaborate in each subregion to elect the leader. The leader applies activity scheduling based optimization in order to provide only one optimal set of active sensor nodes that takes the mission of sensing during this period. The performance of our DiLCO protocol has been evaluated by a series of simulations. We have presented a comparison between our proposed protocol and two other existing protocols known in the literature: DESK and GAF. The experimental results have validated our protocol and showed its efficiency in the optimization of the coverage and the lifetime compared to the two benchmarking methods.

 
 
 Next, we propose in chapter 5 a method called Multiround Distributed Lifetime Coverage Optimization protocol (MuDiLCO), which is an extension of the DiLCO protocol introduced in chapter 4.  MuDiLCO implemented an activity scheduling based optimization in order to provide multiple sets of active sensor nodes, for several rounds in the sensing phase. We have thus introduced an improved coverage optimization model that make a multiround optimization, whilst it was a single round optimization in DiLCO. We have conducted many simulations comparing the proposed MuDiLCO protocol for different number of rounds, as well as with DiLCO, DESK, and GAF. 
 
 
 
 Next, we propose in chapter 5 a method called Multiround Distributed Lifetime Coverage Optimization protocol (MuDiLCO), which is an extension of the DiLCO protocol introduced in chapter 4.  MuDiLCO implemented an activity scheduling based optimization in order to provide multiple sets of active sensor nodes, for several rounds in the sensing phase. We have thus introduced an improved coverage optimization model that make a multiround optimization, whilst it was a single round optimization in DiLCO. We have conducted many simulations comparing the proposed MuDiLCO protocol for different number of rounds, as well as with DiLCO, DESK, and GAF. 
 

-In chapter 6, we have proposed an approach called Perimeter-based Coverage Optimization protocol (PeCO) in order to optimize the lifetime coverage, so that it provides activity scheduling which ensures sensing coverage as long as possible. Like DiLCO and MuDiLCO, PeCO protocol is distributed among sensor nodes in each subregion. The novelty of our approach, in comparison with DiLCO and MuDiLCO, lies essentially in the formulation of a new mathematical optimization model based on the perimeter coverage level to schedule sensors’ activities. A leader provides one schedule during the current period by executing the new integer program during the decision phase.  The extensive simulation experiments have demonstrated that PeCO can offer longer lifetime coverage for WSNs.
+In chapter 6, we propose an approach called Perimeter-based Coverage Optimization protocol (PeCO) in order to optimize the lifetime coverage, so that it provides activity scheduling which ensures sensing coverage as long as possible. Like DiLCO and MuDiLCO, the PeCO protocol is distributed among sensor nodes in each subregion. The novelty of our approach, in comparison with DiLCO and MuDiLCO, lies essentially in the formulation of a new mathematical optimization model based on the perimeter coverage level to schedule sensors’ activities. A leader provides one schedule during the current period by executing the new integer program during the decision phase.  The extensive simulation experiments have demonstrated that PeCO can offer longer lifetime coverage for WSNs.

 
 

-Finally, we outlined some interesting issues that will be considered in our perspectives which are discussed in more detail next.
+Finally, we outline some interesting issues that will be considered in our perspectives which are discussed in more detail next.

 
 
 
 
 
 

diff --git a/INTRODUCTION.tex b/INTRODUCTION.tex
index 7a18e636a06b26977ba7070061737f27ea46eeef..f0a2c74361bac050da6b137a3594664b494a4637 100644 (file)
--- a/INTRODUCTION.tex
+++ b/INTRODUCTION.tex
@@ -6,11 +6,12 @@
 
 \section*{1. General Introduction}  
 \addcontentsline{toc}{section}{1. General Introduction }
 
 \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 physical dangerous environments such as volcanoes, nuclear reactors, forest fires, or military battle fields. Therefore, a specific type of wireless networks, called Wireless Sensor Network (WSN), has emerged to cope with these challenges. 
+%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 physical dangerous environments such as volcanoes, nuclear reactors, forest fires, or military battle fields. Therefore, a specific type of wireless networks, called Wireless Sensor Network (WSN), has emerged to cope with these challenges. 
+Wireless sensor networks (WSNs) have recently received a great deal of research attention due to their wide range of potential applications. Many important characteristics provided by the WSNs make them different from other wireless ad-hoc networks. Furthermore, these characteristics impose lots of limitations that lead to several challenges in the network. These challenges include coverage, topology control, routing, data fusion, security, and many others.  One of the main research challenges faced in wireless sensor networks is to preserve continuously and effectively the coverage of an area of interest to be monitored, while simultaneously preventing as much as possible a network failure due to battery-depleted nodes.

 
 WSN is an ad hoc wireless networks, which consists of a large number of wireless cheap devices called sensors. A sensor node can 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.
 
 
 WSN is an ad hoc wireless networks, which consists of a large number of wireless cheap devices called sensors. A sensor node can 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.
 

-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 taking these constraints into account 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.
+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 taking these constraints into account have been proposed. Most of 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 when designing of the WSN protocols due to 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 a 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 focus on the area coverage problem in WSNs. The major goal of the area coverage problem is to ensure monitoring the entire sensing field for as long as possible. The area coverage problem is closely related to the performance of WSNs in many applications, such as monitoring a battlefield, target detection, tracking, personal protection, animal habit monitoring, and homeland security.
 
 Since the network lifetime depends on sensor lifetime, the power depletion represents the most significant part when designing of the WSN protocols due to 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 a 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 focus on the area coverage problem in WSNs. The major goal of the area coverage problem is to ensure monitoring the entire sensing field for as long as possible. The area coverage problem is closely related to the performance of WSNs in many applications, such as monitoring a battlefield, target detection, tracking, personal protection, animal habit monitoring, and homeland security.


@@ -18,10 +19,10 @@ Specifically, the energy-efficient protocols proposed in this dissertation focus
 
 \section*{2. Motivation of the Dissertation}
 \addcontentsline{toc}{section}{2. 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
+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 some 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 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.
 
 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 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 on energy-efficient coverage have been 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?
+Although many works on energy-efficient coverage have been introduced, there is still need for  strategies  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?

  
 \iffalse
 \section*{3. The Objective of this Dissertation}
  
 \iffalse
 \section*{3. The Objective of this Dissertation}


@@ -40,13 +41,14 @@ election and sensor activity scheduling based optimization, where the challenges
 The main contributions in this dissertation concentrate on designing distributed optimization protocols to extend the lifetime of WSNs.  We summarize the main contributions of our research as follows:
  
 \begin{enumerate} [i)]
 The main contributions in this dissertation concentrate on designing distributed optimization protocols to extend the lifetime of WSNs.  We summarize the main contributions of our research as follows:
  
 \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 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 a scheme 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 scheme 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 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 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 the 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. 
 
 \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. The activation of the sensors is planned for many rounds in advance compared with the previous approach. 
+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 the 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. The activation of the sensors is planned for many rounds in advance compared with the previous approach. 

 
 %\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. 
 

diff --git a/Resume.tex b/Resume.tex
index 7429425605312336b5c1be07b53dd0d8b4859332..978f736fe554582b9ff38536ce89be1961516b70 100644 (file)
--- a/Resume.tex
+++ b/Resume.tex
@@ -9,22 +9,22 @@
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 

-\emph{ \begin{center} \Large Techniques d'Optimisation Distribuées de la Couverture pour Améliorer la Durée de Vie des Réseaux de Capteurs sans Fil   \end{center}}   
+\emph{ \begin{center} \Large Techniques d'optimisation distribuées de la couverture pour améliorer la durée de vie des réseaux de capteurs  sans  fil   \end{center}}   

 %\emph{ \begin{center} \large By \end{center}}  
 \emph{ \begin{center} \large Ali Kadhum Idrees \\ Université de Franche-Comt\'e, 2015 \end{center}} 
 %\emph{ \begin{center} \large The University of Franche-Comt\'e, 2015 \end{center}}  
 \emph{ \begin{center} \large Encadrants: Raphaël Couturier, Karine Deschinkel et Michel Salomon \end{center}}  
 
 
 %\emph{ \begin{center} \large By \end{center}}  
 \emph{ \begin{center} \large Ali Kadhum Idrees \\ Université de Franche-Comt\'e, 2015 \end{center}} 
 %\emph{ \begin{center} \large The University of Franche-Comt\'e, 2015 \end{center}}  
 \emph{ \begin{center} \large Encadrants: Raphaël Couturier, Karine Deschinkel et Michel Salomon \end{center}}  
 
 

-Les réseaux de capteurs sans fil ont suscité beaucoup de travaux de recherche au cours des dernières années en raison de leur large gamme d'applications potentielles. Les caractéristiques des n\oe uds capteurs imposent des contraints enterme de consommation d'énergie et de capacité de traitement qui rendent caduque les protocoles des réseaux ad-hoc sans fil, avec de nombreux défis à résoudre. Parmi ces défis, on peut noter la préservation de la couverture, le contrôle de la topologie, le routage, la fusion de données, la sécurité, etc. La préservation de la couverture d'une région à surveiller, de manière permanente et efficace, tout en empêchant autant que possible un dysfonctionnement du réseau en raison du déchargement de la batterie de certains n\oe uds, est une des problématique de recherche majeures.   
+Les réseaux de capteurs sans fil ont suscité beaucoup de travaux de recherche au cours des dernières années en raison de leur large gamme d'applications potentielles. Les caractéristiques des n\oe uds capteurs imposent des contraintes de consommation d'énergie et de capacité de traitement qui rendent caduque les protocoles des réseaux ad-hoc sans fil, avec de nombreux défis à résoudre. Parmi ces défis, on peut noter la préservation de la couverture, le contrôle de la topologie, le routage, la fusion de données, la sécurité, etc. La préservation de la couverture d'une région à surveiller, de manière permanente et efficace, tout en empêchant autant que possible un dysfonctionnement du réseau en raison du déchargement de la batterie de certains n\oe uds, est une des problématiques  de recherche majeures.   

 
 
 
 

-Dans cette thèse, nous nous sommes intéressés au problème de la préservation de la couverture, ainsi qu'à l'efficatité 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 opérationnelle du réseau. Les protocoles proposés doivent être efficaces en terme de consommation énergétique induite par les calculs et les communications. Pour résoudre le problème, nous avons proposé des nouvelles approches en deux étapes. 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. Dans un second temps, un de nos protocoles est exécuté par chacun des n\oe uds capteurs 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. 
+Dans cette thèse, nous nous sommes intéressés au problème de la préservation de la couverture, ainsi qu'à l'efficatité qui est une exigence essentielle dans un réseau de capteurs sans fil. Nous avons étudié  les protocoles d'optimisation distribués avec l'objectif ultime de prolonger la durée de vie opérationnelle du réseau. Les protocoles proposés doivent être efficaces en  consommation énergétique induite par les calculs et les communications. Pour résoudre le problème, nous avons proposé des nouvelles approches en deux étapes. 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. Dans un second temps, un protocole est exécuté par chacun des n\oe uds capteurs 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. 

 
 Le premier protocole proposé est appelé DiLCO, pour Distributed Lifetime Coverage Optimization. Dans ce protocole, la durée de vie est divisée en périodes, avec chaque période qui est composée de 4 phases: échange d'informations entre les n\oe uds d'une sous-région, élection d'un n\oe ud leader, décision et surveillance. Le processus de décision est mis en oeuvre par le n\oe ud leader en résolvant un programme linéaire en nombres entiers qui permet de définir un seul ensemble de n\oe uds de capteurs devant être actifs pour assurer la couverture durant la période courante. 
 
 
 
 Le premier protocole proposé est appelé DiLCO, pour Distributed Lifetime Coverage Optimization. Dans ce protocole, la durée de vie est divisée en périodes, avec chaque période qui est composée de 4 phases: échange d'informations entre les n\oe uds d'une sous-région, élection d'un n\oe ud leader, décision et surveillance. Le processus de décision est mis en oeuvre par le n\oe ud leader en résolvant un programme linéaire en nombres entiers qui permet de définir un seul ensemble de n\oe uds de capteurs devant être actifs pour assurer la couverture durant la période courante. 
 
 

-Dans le second protocole, qui est une évolution de DiLCO, nous cherchons à construire simultanément plusieurs ensembles de n\oe uds de capteurs de couverture pour la phase de surveillance. Cette  dernière est ainsi diviseé en "rondes" de surveillance, d'où le nom Multiround DiLCO ou MuDiLCO donné à ce protocole. Le processus de décision est toujours effectué par un n\oe ud leader, qui détermine les ensembles de n\oe uds capteurs à activer successivement via la résolution d'un nouveau programme linéaire en nombres entiers.
+Dans le second protocole, qui est une évolution de DiLCO, nous cherchons à construire simultanément plusieurs ensembles de n\oe uds de capteurs de couverture pour la phase de surveillance. Cette  dernière est ainsi divisée en "rondes" de surveillance, d'où le nom Multiround DiLCO ou MuDiLCO donné à ce protocole. Le processus de décision est toujours effectué par un n\oe ud leader, qui détermine les ensembles de n\oe uds capteurs à activer successivement via la résolution d'un nouveau programme linéaire en nombres entiers.

 
 
 
 
 
 

diff --git a/Thesis.tex b/Thesis.tex
index 1c1c5f8896e98d26f2a50ce9b4830cd8a62d57c1..fffe1ba44c4525b92a4e4223180c4fc6b6e3e532 100644 (file)
--- a/Thesis.tex
+++ b/Thesis.tex
@@ -6,7 +6,6 @@
 %% The content of the PhD thesis
 \begin{document}
 
 %% The content of the PhD thesis
 \begin{document}
 

-

 % set the page numbers to be arabic, starting at page 1 %
 
 \setcounter{page}{1}
 % set the page numbers to be arabic, starting at page 1 %
 
 \setcounter{page}{1}

diff --git a/Thesis.toc b/Thesis.toc
index 0bb74b545844754a82d1e65dcfac8e032e7c67bd..44067d77b504b5e253ada98f504fc6b3d125c72b 100644 (file)
--- a/Thesis.toc
+++ b/Thesis.toc
@@ -85,7 +85,7 @@
 \contentsline {section}{\numberline {5.5}Conclusion}{112}{section.5.5}
 \contentsline {chapter}{\numberline {6} Perimeter-based Coverage Optimization to Improve Lifetime in WSNs}{113}{chapter.6}
 \contentsline {section}{\numberline {6.1}Introduction}{113}{section.6.1}
 \contentsline {section}{\numberline {5.5}Conclusion}{112}{section.5.5}
 \contentsline {chapter}{\numberline {6} Perimeter-based Coverage Optimization to Improve Lifetime in WSNs}{113}{chapter.6}
 \contentsline {section}{\numberline {6.1}Introduction}{113}{section.6.1}

-\contentsline {section}{\numberline {6.2}The PeCO Protocol Description}{113}{section.6.2}
+\contentsline {section}{\numberline {6.2}Description of the PeCO Protocol}{113}{section.6.2}

 \contentsline {subsection}{\numberline {6.2.1}Assumptions and Models}{113}{subsection.6.2.1}
 \contentsline {subsection}{\numberline {6.2.2}PeCO Protocol Algorithm}{116}{subsection.6.2.2}
 \contentsline {section}{\numberline {6.3}Perimeter-based Coverage Problem Formulation}{118}{section.6.3}
 \contentsline {subsection}{\numberline {6.2.1}Assumptions and Models}{113}{subsection.6.2.1}
 \contentsline {subsection}{\numberline {6.2.2}PeCO Protocol Algorithm}{116}{subsection.6.2.2}
 \contentsline {section}{\numberline {6.3}Perimeter-based Coverage Problem Formulation}{118}{section.6.3}


@@ -94,8 +94,8 @@
 \contentsline {subsection}{\numberline {6.4.2}Simulation Results}{120}{subsection.6.4.2}
 \contentsline {subsubsection}{\numberline {6.4.2.1}Coverage Ratio}{120}{subsubsection.6.4.2.1}
 \contentsline {subsubsection}{\numberline {6.4.2.2}Active Sensors Ratio}{120}{subsubsection.6.4.2.2}
 \contentsline {subsection}{\numberline {6.4.2}Simulation Results}{120}{subsection.6.4.2}
 \contentsline {subsubsection}{\numberline {6.4.2.1}Coverage Ratio}{120}{subsubsection.6.4.2.1}
 \contentsline {subsubsection}{\numberline {6.4.2.2}Active Sensors Ratio}{120}{subsubsection.6.4.2.2}

-\contentsline {subsubsection}{\numberline {6.4.2.3}The Energy Consumption}{120}{subsubsection.6.4.2.3}
-\contentsline {subsubsection}{\numberline {6.4.2.4}The Network Lifetime}{123}{subsubsection.6.4.2.4}
+\contentsline {subsubsection}{\numberline {6.4.2.3}Energy Consumption}{120}{subsubsection.6.4.2.3}
+\contentsline {subsubsection}{\numberline {6.4.2.4}Network Lifetime}{122}{subsubsection.6.4.2.4}

 \contentsline {section}{\numberline {6.5}Conclusion}{126}{section.6.5}
 \contentsline {part}{III\hspace {1em}Conclusion and Perspectives}{127}{part.3}
 \contentsline {chapter}{\numberline {7}Conclusion and Perspectives}{129}{chapter.7}
 \contentsline {section}{\numberline {6.5}Conclusion}{126}{section.6.5}
 \contentsline {part}{III\hspace {1em}Conclusion and Perspectives}{127}{part.3}
 \contentsline {chapter}{\numberline {7}Conclusion and Perspectives}{129}{chapter.7}