Update by Ali

[ThesisAli.git] / CHAPITRE_04.tex

diff --git a/CHAPITRE_04.tex b/CHAPITRE_04.tex
index 5aa3d1981f07ec65c3371f4fbdebc06227b0b6b2..b518209226c2b3992c1d0d464addff24bd1baf5c 100644 (file)
--- a/CHAPITRE_04.tex
+++ b/CHAPITRE_04.tex
@@ -766,12 +766,6 @@ Comparison shows that DiLCO-16 protocol and DiLCO-32 protocol, which use distrib
 %It also means that distributing the algorithm in each node and subdividing the sensing field into many subregions, which are managed independently and simultaneously, is the most relevant way to maximize the lifetime of a network.
 
 \end{enumerate}
 %It also means that distributing the algorithm in each node and subdividing the sensing field into many subregions, which are managed independently and simultaneously, is the most relevant way to maximize the lifetime of a network.
 
 \end{enumerate}

-
-\section{Conclusion}
-\label{ch4:sec:05}
-A crucial problem in WSN is to schedule the sensing activities of the different nodes  in order to ensure both coverage of  the area  of interest  and longer network lifetime. The inherent limitations of sensor nodes, in energy provision, communication, and computing capacities,  require protocols that optimize the use of the  available resources  to  fulfill the sensing  task. To address  this problem, this chapter proposes a  two-step approach. Firstly, the field of sensing
-is  divided into  smaller  subregions using  the  concept of  divide-and-conquer method. Secondly,  a distributed  protocol called Distributed  Lifetime Coverage Optimization is applied in each  subregion to optimize the coverage and lifetime performances. In a subregion,  our protocol  consists in  electing a  leader node, which will then perform a sensor activity scheduling. The challenges include how to  select the most efficient leader in each  subregion and  the  best representative set of active nodes to ensure a high level of coverage. To assess the performance of our approach, we  compared it with two other approaches using many performance metrics  like coverage ratio or network  lifetime. We have also studied the  impact of the  number of subregions  chosen to subdivide the  area of interest, considering  different  network  sizes. The  experiments  show  that increasing the  number of subregions improves  the lifetime. The  more subregions there are, the  more robust the network is against random disconnection resulting from dead nodes.  However, for  a given sensing field and network size there is an optimal number of  subregions. Therefore, in case of our simulation context  a subdivision in  $16$~subregions seems to be the most relevant.
-

 \begin{figure}[p!]
 \centering
 % \begin{multicols}{0}
 \begin{figure}[p!]
 \centering
 % \begin{multicols}{0}


@@ -784,3 +778,9 @@ is  divided into  smaller  subregions using  the  concept of  divide-and-conquer
 \caption{Network lifetime for (a) $Lifetime_{95}$ and (b) $Lifetime_{50}$}
   \label{Figures/ch4/R3/LT}
 \end{figure}
 \caption{Network lifetime for (a) $Lifetime_{95}$ and (b) $Lifetime_{50}$}
   \label{Figures/ch4/R3/LT}
 \end{figure}

+\section{Conclusion}
+\label{ch4:sec:05}
+A crucial problem in WSN is to schedule the sensing activities of the different nodes  in order to ensure both coverage of  the area  of interest  and longer network lifetime. The inherent limitations of sensor nodes, in energy provision, communication, and computing capacities,  require protocols that optimize the use of the  available resources  to  fulfill the sensing  task. To address  this problem, this chapter proposes a  two-step approach. Firstly, the field of sensing
+is  divided into  smaller  subregions using  the  concept of  divide-and-conquer method. Secondly,  a distributed  protocol called Distributed  Lifetime Coverage Optimization is applied in each  subregion to optimize the coverage and lifetime performances. In a subregion,  our protocol  consists in  electing a  leader node, which will then perform a sensor activity scheduling. The challenges include how to  select the most efficient leader in each  subregion and  the  best representative set of active nodes to ensure a high level of coverage. To assess the performance of our approach, we  compared it with two other approaches using many performance metrics  like coverage ratio or network  lifetime. We have also studied the  impact of the  number of subregions  chosen to subdivide the  area of interest, considering  different  network  sizes. The  experiments  show  that increasing the  number of subregions improves  the lifetime. The  more subregions there are, the  more robust the network is against random disconnection resulting from dead nodes.  However, for  a given sensing field and network size there is an optimal number of  subregions. Therefore, in case of our simulation context  a subdivision in  $16$~subregions seems to be the most relevant.
+
+