Update by Ali

diff --git a/CHAPITRE_01.tex b/CHAPITRE_01.tex
index 745949656080f8f545a09a817cbb38faa2cd420d..ddc551c02a3c63679e2f61d9ba8f03d417e2a0bc 100644 (file)
--- a/CHAPITRE_01.tex
+++ b/CHAPITRE_01.tex
@@ -116,7 +116,7 @@ This kind of WSN consists of low-cost wireless sensor nodes, which are embedded
 \section{Applications}
 \label{ch1:sec:04}
 %\indent The fast development in WSNs has been led to study  their different characteristics extensively. However, the WSN is concentrated on various applications. 
 \section{Applications}
 \label{ch1:sec:04}
 %\indent The fast development in WSNs has been led to study  their different characteristics extensively. However, the WSN is concentrated on various applications. 

-In this section, we describe different academic and commercial applications. A WSN can use various types of sensors such as \cite{ref17,ref19}: thermal, seismic, magnetic, visual, infrared, acoustic, and radar. These sensors are capable of observing a different physical conditions such as: temperature, humidity, pressure, speed, direction, movement, light, soil makeup, noise levels, presence or absence of certain kinds of objects, and mechanical stress levels on attached objects. Consequently, a wide range of WSN applications can be classified into five classes~\cite{ref22}, as shown in Figure~\ref{WSNAP}. 
+In this section, we describe different academic and commercial applications. A WSN can use various types of sensors such as \cite{ref17,ref19}: thermal, seismic, magnetic, visual, infrared, acoustic, and radar. These sensors are capable of observing  different physical conditions such as: temperature, humidity, pressure, speed, direction, movement, light, soil makeup, noise levels, presence or absence of certain kinds of objects, and mechanical stress levels on attached objects. Consequently, a wide range of WSN applications can be classified into five classes~\cite{ref22}, as shown in Figure~\ref{WSNAP}. 

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

diff --git a/CHAPITRE_05.tex b/CHAPITRE_05.tex
index 5a39fd7049beed870d4b961bc854c9c0d7b671c7..6604d953d889c5bd08ffff8c178cb9b6503d5559 100644 (file)
--- a/CHAPITRE_05.tex
+++ b/CHAPITRE_05.tex
@@ -41,11 +41,6 @@ mechanisms: subdividing the area of interest into several subregions (like a clu
 
 As can be seen in Figure~\ref{fig2},  our protocol  works in  periods fashion, where  each is  divided  into 4  phases: Information~Exchange,  Leader~Election, Decision, and Sensing. 
 %The information exchange among wireless sensor nodes is described in chapter 4, section \ref{ch4:sec:02:03:01}. The leader election in each subregion is explained in chapter 4, section \ref{ch4:sec:02:03:02}, 
 
 As can be seen in Figure~\ref{fig2},  our protocol  works in  periods fashion, where  each is  divided  into 4  phases: Information~Exchange,  Leader~Election, Decision, and Sensing. 
 %The information exchange among wireless sensor nodes is described in chapter 4, section \ref{ch4:sec:02:03:01}. The leader election in each subregion is explained in chapter 4, section \ref{ch4:sec:02:03:02}, 

-The difference with MuDiLCO in that the elected leader in each subregion is for each period. In the decision phase, each leader will solve an integer  program to select which  cover sets  will be activated in  the following  sensing phase  to cover the  subregion to  which it belongs.  The integer  program will produce $T$ cover sets,  one for each round. The leader will send an ActiveSleep  packet to each sensor in the subregion based on the algorithm's results, indicating if the sensor should be active or not in
-each round  of the  sensing phase. Each sensing phase is itself divided into $T$ rounds and for each round a set of sensors (a cover set) is responsible for the sensing task. 
-%Each sensor node in the subregion will receive an ActiveSleep packet from leader, informing it to stay awake or to go to sleep for  each round of the sensing  phase. 
-Algorithm~\ref{alg:MuDiLCO}, which will be  executed by each node at the beginning  of a period, explains  how the ActiveSleep packet is obtained. In this way, a multiround optimization  process is performed  during each
-period  after  Information~Exchange  and  Leader~Election phases,  in  order to produce $T$ cover sets that will take the mission of sensing for $T$ rounds.

 \begin{figure}[ht!]
 \centering \includegraphics[width=160mm]{Figures/ch5/GeneralModel.jpg} % 70mm  Modelgeneral.pdf
 \caption{MuDiLCO protocol.}
 \begin{figure}[ht!]
 \centering \includegraphics[width=160mm]{Figures/ch5/GeneralModel.jpg} % 70mm  Modelgeneral.pdf
 \caption{MuDiLCO protocol.}


@@ -53,12 +48,6 @@ period  after  Information~Exchange  and  Leader~Election phases,  in  order to
 \end{figure} 
 
 
 \end{figure} 
 
 

-%This protocol minimizes the impact of unexpected node failure (not due to batteries running out of energy), because it works in periods. On the one hand, if a node failure is detected before making the decision, the node will not participate during this phase. On the other hand, if the node failure occurs after the decision, the sensing  task of the network will be temporarily affected:  only during  the period of sensing until a new period starts.
-
-%The  energy consumption  and some other constraints  can easily  be  taken into account since the  sensors  can  update and  then  exchange their  information (including their residual energy) at the beginning of each period.  However, the pre-sensing  phases (Information  Exchange, Leader  Election, and  Decision) are energy  consuming for some  nodes, even  when they  do not  join the  network to monitor the area.
-
- 
-

 \begin{algorithm}[h!]                
  % \KwIn{all the parameters related to information exchange}
 %  \KwOut{$winer-node$ (: the id of the winner sensor node, which is the leader of current round)}
 \begin{algorithm}[h!]                
  % \KwIn{all the parameters related to information exchange}
 %  \KwOut{$winer-node$ (: the id of the winner sensor node, which is the leader of current round)}


@@ -99,6 +88,19 @@ period  after  Information~Exchange  and  Leader~Election phases,  in  order to
 
 \end{algorithm}
 
 
 \end{algorithm}
 

+The difference with MuDiLCO in that the elected leader in each subregion is for each period. In the decision phase, each leader will solve an integer  program to select which  cover sets  will be activated in  the following  sensing phase  to cover the  subregion to  which it belongs.  The integer  program will produce $T$ cover sets,  one for each round. The leader will send an ActiveSleep  packet to each sensor in the subregion based on the algorithm's results, indicating if the sensor should be active or not in
+each round  of the  sensing phase. Each sensing phase is itself divided into $T$ rounds and for each round a set of sensors (a cover set) is responsible for the sensing task. 
+%Each sensor node in the subregion will receive an ActiveSleep packet from leader, informing it to stay awake or to go to sleep for  each round of the sensing  phase. 
+Algorithm~\ref{alg:MuDiLCO}, which will be  executed by each node at the beginning  of a period, explains  how the ActiveSleep packet is obtained. In this way, a multiround optimization  process is performed  during each
+period  after  Information~Exchange  and  Leader~Election phases,  in  order to produce $T$ cover sets that will take the mission of sensing for $T$ rounds.
+
+
+%This protocol minimizes the impact of unexpected node failure (not due to batteries running out of energy), because it works in periods. On the one hand, if a node failure is detected before making the decision, the node will not participate during this phase. On the other hand, if the node failure occurs after the decision, the sensing  task of the network will be temporarily affected:  only during  the period of sensing until a new period starts.
+
+%The  energy consumption  and some other constraints  can easily  be  taken into account since the  sensors  can  update and  then  exchange their  information (including their residual energy) at the beginning of each period.  However, the pre-sensing  phases (Information  Exchange, Leader  Election, and  Decision) are energy  consuming for some  nodes, even  when they  do not  join the  network to monitor the area.
+
+ 
+

 
 
 
 
 
 


@@ -393,7 +395,7 @@ seconds (needed to solve optimization problem) for different values of $T$. The
 As expected,  the execution time increases  with the number of  rounds $T$ taken into account to schedule the sensing phase. The times obtained for $T=1,3$ or $5$ seem bearable, but for $T=7$ they become quickly unsuitable for a sensor node, especially when  the sensor network size increases.   Again, we can notice that if we want  to schedule the nodes activities for a  large number of rounds,
 we need to choose a relevant number of subregions in order to avoid a complicated and cumbersome optimization.  
 
 As expected,  the execution time increases  with the number of  rounds $T$ taken into account to schedule the sensing phase. The times obtained for $T=1,3$ or $5$ seem bearable, but for $T=7$ they become quickly unsuitable for a sensor node, especially when  the sensor network size increases.   Again, we can notice that if we want  to schedule the nodes activities for a  large number of rounds,
 we need to choose a relevant number of subregions in order to avoid a complicated and cumbersome optimization.  
 

-On the one hand, a large value  for $T$ permits to reduce the  energy overhead due  to the three  pre-sensing phases, on  the other hand  a leader  node may  waste a  considerable amount  of energy  to  solve the optimization problem. \\ \\ \\ \\ \\ \\ \\
+On the one hand, a large value  for $T$ permits to reduce the  energy overhead due  to the three  pre-sensing phases, on  the other hand  a leader  node may  waste a  considerable amount  of energy  to  solve the optimization problem. %\\ \\ \\ \\ \\ \\ \\

 
 \item {{\bf Network lifetime}}
 %\subsection{Network lifetime}
 
 \item {{\bf Network lifetime}}
 %\subsection{Network lifetime}

diff --git a/Thesis.toc b/Thesis.toc
index 633865a78a958b9c6444b483f5acb78b212ba3ea..638501492ca5faed94d410c33cb3fb03f319cd6b 100644 (file)
--- a/Thesis.toc
+++ b/Thesis.toc
@@ -80,8 +80,8 @@
 \contentsline {section}{\numberline {5.3}Primary Points based Multiround Coverage Problem Formulation}{103}{section.5.3}
 \contentsline {section}{\numberline {5.4}Experimental Study and Analysis}{104}{section.5.4}
 \contentsline {subsection}{\numberline {5.4.1}Simulation Setup}{104}{subsection.5.4.1}
 \contentsline {section}{\numberline {5.3}Primary Points based Multiround Coverage Problem Formulation}{103}{section.5.3}
 \contentsline {section}{\numberline {5.4}Experimental Study and Analysis}{104}{section.5.4}
 \contentsline {subsection}{\numberline {5.4.1}Simulation Setup}{104}{subsection.5.4.1}

-\contentsline {subsection}{\numberline {5.4.2}Metrics}{105}{subsection.5.4.2}
-\contentsline {subsection}{\numberline {5.4.3}Results Analysis and Comparison }{106}{subsection.5.4.3}
+\contentsline {subsection}{\numberline {5.4.2}Metrics}{104}{subsection.5.4.2}
+\contentsline {subsection}{\numberline {5.4.3}Results Analysis and Comparison }{105}{subsection.5.4.3}

 \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}

diff --git a/entete.tex b/entete.tex
index f63aa1473a2810bee0366d3b89e1ae25c8e85934..2eca7756aee1610f494b20231a6c95ed5ffcf1a6 100644 (file)
--- a/entete.tex
+++ b/entete.tex
@@ -85,12 +85,12 @@
 \addjury{Karine}{Deschinkel}{Co-Supervisor}{Assistant Professor at University of Franche-Comt\'e}
 \addjury{Michel}{Salomon}{Co-Supervisor}{Assistant Professor at University of Franche-Comt\'e}
 \fi
 \addjury{Karine}{Deschinkel}{Co-Supervisor}{Assistant Professor at University of Franche-Comt\'e}
 \addjury{Michel}{Salomon}{Co-Supervisor}{Assistant Professor at University of Franche-Comt\'e}
 \fi

- \addjury {}{Prof Sylvain CONTASSOT-VIVIER} {University of Lorraine} {Examiner}
-\addjury {} {Prof Ye-Qiong SONG} {University of Lorraine} {Reviewer}
+ \addjury {} {Prof Ye-Qiong SONG} {University of Lorraine} {Reviewer}

 \addjury{} {Assoc Prof Hamida SEBA (HDR)} {University of Claude Bernard Lyon1} {Reviewer}
 \addjury{} {Assoc Prof Hamida SEBA (HDR)} {University of Claude Bernard Lyon1} {Reviewer}

-\addjury {} {Prof Raphaël Couturier} {University of Franche-Comt\'e} {Director}
-\addjury {} {Asst Prof Karine Deschinkel} {University of Franche-Comt\'e} {Supervisor}
-\addjury {} {Asst Prof Michel Salomon} {University of Franche-Comt\'e} {Supervisor}
+\addjury {}{Prof Sylvain CONTASSOT-VIVIER} {University of Lorraine} {Examiner}
+\addjury {} {Prof Raphaël Couturier} {University of Franche-Comt\'e} {Supervisor}
+\addjury {} {Asst Prof Karine Deschinkel} {University of Franche-Comt\'e} { Co-supervisor}
+\addjury {} {Asst Prof Michel Salomon} {University of Franche-Comt\'e} {Co-supervisor}

  
  
 % Supervisors:\\
  
  
 % Supervisors:\\