X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/ThesisAli.git/blobdiff_plain/ef80cbcb3fb44c6a51e2b6c56440e3bc67efe26b..6bf881a8ca453aa337be79c6945f1b97daf44039:/CHAPITRE_04.tex?ds=sidebyside

diff --git a/CHAPITRE_04.tex b/CHAPITRE_04.tex
old mode 100755
new mode 100644
index ad69a0b..8fc2e39
--- a/CHAPITRE_04.tex
+++ b/CHAPITRE_04.tex
@@ -8,7 +8,7 @@
 \label{ch4}
 
 
-
+\iffalse
 \section{Summary}
 \label{ch4:sec:01}
 In this chapter, a Distributed Lifetime Coverage Optimization protocol (DiLCO) to maintain
@@ -26,6 +26,23 @@ carried out  by a leader node,  which solves an integer  program.  Compared with
 some existing protocols, simulation results  show that the proposed protocol can
 prolong the network lifetime and improve the coverage performance effectively.
 
+\fi
+
+\section{Introduction}
+\label{ch4:sec:01}
+Energy efficiency is  a crucial issue in wireless  sensor networks since the sensory consumption, in  order to  maximize the network  lifetime, represents  the major difficulty when designing WSNs. As a consequence, one of the scientific research challenges in  WSNs, which has  been addressed by  a large amount  of literature
+during the  last few  years, is  the design of  energy efficient  approaches for coverage and connectivity~\cite{ref94}.   Coverage reflects how well a sensor  field is  monitored. On  the one  hand, we  want to  monitor the  area of interest in the most efficient way~\cite{ref95}.  On the other hand, we want to use  as little energy  as possible.   Sensor nodes  are battery-powered  with no means  of recharging  or replacing,  usually  due to  environmental (hostile  or
+unpractical environments)  or cost reasons.   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.
+
+In this chapter, 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. 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.  Our 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.
+
+The remainder of this chapter is organized as follows. The next section is devoted to the DiLCO protocol description. Section \ref{ch4:sec:03} gives the primary points based coverage problem formulation which is used to schedule the activation of sensors. Section \ref{ch4:sec:04} shows the simulation
+results obtained using the discrete event simulator OMNeT++ \cite{ref158}. They fully demonstrate the usefulness of the proposed approach. Finally, we give concluding remarks in section \ref{ch4:sec:05}.
+
+
 
 \section{Description of the DiLCO Protocol}
 \label{ch4:sec:02}
@@ -333,7 +350,7 @@ $w_{U}$ & $|P|^2$
 % is used to refer this table in the text
 \end{table}
 
-Simulations with five  different node densities going from  50 to 250~nodes were
+Simulations with five different node densities going from  50 to 250~nodes were
 performed  considering  each  time  25~randomly generated  networks,  to  obtain
 experimental results  which are relevant. The  nodes are deployed on  a field of
 interest of $(50 \times 25)~m^2 $ in such a way that they cover the field with a