Update by Ali

[ThesisAli.git] / CHAPITRE_05.tex

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--- a/CHAPITRE_05.tex
+++ b/CHAPITRE_05.tex
@@ -7,6 +7,8 @@
 \chapter{Multiround Distributed Lifetime Coverage Optimization Protocol in Wireless Sensor Networks}
 \label{ch5}
 
 \chapter{Multiround Distributed Lifetime Coverage Optimization Protocol in Wireless Sensor Networks}
 \label{ch5}
 

+\iffalse
+

 \section{Summary}
 \label{ch5:sec:01}
 Coverage and lifetime are two paramount problems in Wireless  Sensor Networks (WSNs). In this paper, a method called Multiround Distributed Lifetime Coverage
 \section{Summary}
 \label{ch5:sec:01}
 Coverage and lifetime are two paramount problems in Wireless  Sensor Networks (WSNs). In this paper, a method called Multiround Distributed Lifetime Coverage


@@ -17,6 +19,47 @@ lifetime of  WSN. The decision process is  carried out by a  leader node, which
 during the rounds  of the sensing phase. Compared  with some existing protocols, simulation  results based  on  multiple criteria  (energy consumption,  coverage
 ratio, and  so on) show that  the proposed protocol can  prolong efficiently the network lifetime and improve the coverage performance.
 
 during the rounds  of the sensing phase. Compared  with some existing protocols, simulation  results based  on  multiple criteria  (energy consumption,  coverage
 ratio, and  so on) show that  the proposed protocol can  prolong efficiently the network lifetime and improve the coverage performance.
 

+\fi
+
+\section{Introduction}
+\label{ch5:sec:01}
+
+\indent  The fast  developments of low-cost  sensor devices  and  wireless
+communications have allowed the emergence of WSNs. A WSN includes a large number
+of small, limited-power sensors that  can sense, process, and transmit data over
+a wireless  communication. They communicate  with each other by using multi-hop
+wireless communications and cooperate together  to monitor the area of interest,
+so that  each measured data can be  reported to a monitoring  center called sink
+for further  analysis~\cite{ref222}.  There  are several fields  of application
+covering  a wide  spectrum for a  WSN, including health, home, environmental,
+military, and industrial applications~\cite{ref19}.
+
+On the one hand sensor nodes run on batteries with limited capacities, and it is
+often  costly  or  simply  impossible  to  replace  and/or  recharge  batteries,
+especially in remote and hostile environments. Obviously, to achieve a long life
+of the  network it is important  to conserve battery  power. Therefore, lifetime
+optimization is one of the most critical issues in wireless sensor networks. On
+the other hand we must guarantee  coverage over the area of interest. To fulfill
+these two objectives, the main idea  is to take advantage of overlapping sensing
+regions to turn-off redundant sensor nodes  and thus save energy. In this paper,
+we concentrate  on the area coverage  problem, with the  objective of maximizing
+the network lifetime by using an optimized multiround scheduling.
+
+We study the problem of designing an energy-efficient optimization algorithm that divides the sensors in a WSN into multiple cover sets such that the area of interest is monitored as long as possible. Providing multiple cover sets can be used to improve the energy efficiency of WSNs. Therefore, in order to increase the longevity of the WSN and conserve the energy, it can be useful to provide multiple cover sets in one time after that schedule them for multiple rounds, so that the battery life of a sensor is not wasted due to the repeated execution of the coverage optimization algorithm, as well as the information exchange and leader election.
+
+The MuDiLCO protocol (for Multiround Distributed Lifetime Coverage Optimization protocol) presented in this chapter is an extension of the approach introduced in chapter 4. Simulation results have shown that it was more interesting to divide the area into several subregions, given the computation complexity. Compared to our protocol in chapter 4, in this one we study the possibility of dividing the sensing phase into multiple rounds. In fact, in this chapter we make a multiround optimization, while it was a single round optimization in our protocol in chapter 4.
+
+The remainder of the chapter continues with section \ref{ch5:sec:02} where a detail of MuDiLCO Protocol is presented. The next section describes the Primary Points based Multiround Coverage Problem formulation  which is used to schedule the activation of sensors in T cover sets. Section \ref{ch5:sec:04} shows the simulation
+results. The chapter ends with a conclusion and some suggestions for further work.
+
+
+ 
+
+
+
+
+
+

 \section{MuDiLCO Protocol Description}
 \label{ch5:sec:02}
 \noindent In this section, we introduce the MuDiLCO protocol which is distributed on each subregion in the area of interest. It is based on two energy-efficient
 \section{MuDiLCO Protocol Description}
 \label{ch5:sec:02}
 \noindent In this section, we introduce the MuDiLCO protocol which is distributed on each subregion in the area of interest. It is based on two energy-efficient


@@ -100,8 +143,9 @@ The  energy consumption  and some other constraints  can easily  be  taken into
 
 
 
 
 
 

-\subsection{Primary Points based Multiround Coverage Problem Formulation}
-%\label{ch5:sec:02:02}
+\section{Primary Points based Multiround Coverage Problem Formulation}
+\label{ch5:sec:03}
+

 
 According to our algorithm~\ref{alg:MuDiLCO}, the integer program is based on the model
 proposed by  \cite{ref156} with some modifications, where  the objective is
 
 According to our algorithm~\ref{alg:MuDiLCO}, the integer program is based on the model
 proposed by  \cite{ref156} with some modifications, where  the objective is


@@ -221,10 +265,10 @@ large compared to $W_{\theta}$.
  
 
 \section{Experimental Study and Analysis}
  
 
 \section{Experimental Study and Analysis}

-\label{ch5:sec:03}
+\label{ch5:sec:04}

 
 \subsection{Simulation Setup}
 
 \subsection{Simulation Setup}

-\label{ch5:sec:03:01}
+\label{ch5:sec:04:01}

 We  conducted  a  series of  simulations  to  evaluate  the efficiency  and  the
 relevance  of our  approach,  using  the  discrete   event  simulator  OMNeT++
 \cite{ref158}. The simulation  parameters are summarized in Table~\ref{table3}.  Each experiment  for  a network  is  run over  25~different random topologies and  the results presented hereafter are  the average of these
 We  conducted  a  series of  simulations  to  evaluate  the efficiency  and  the
 relevance  of our  approach,  using  the  discrete   event  simulator  OMNeT++
 \cite{ref158}. The simulation  parameters are summarized in Table~\ref{table3}.  Each experiment  for  a network  is  run over  25~different random topologies and  the results presented hereafter are  the average of these


@@ -293,7 +337,7 @@ We used the modeling language and the optimization solver which are mentioned in
 %The initial energy of each node  is randomly set in the interval $[500;700]$.  A sensor node  will not participate in the  next round if its  remaining energy is less than  $E_{R}=36~\mbox{Joules}$, the minimum  energy needed for the  node to stay alive  during one round.  This value has  been computed by  multiplying the energy consumed in  active state (9.72 mW) by the time in second  for one round (3600 seconds). According to the  interval of initial energy, a sensor may be alive during at most 20 rounds.
 
 \subsection{Metrics}
 %The initial energy of each node  is randomly set in the interval $[500;700]$.  A sensor node  will not participate in the  next round if its  remaining energy is less than  $E_{R}=36~\mbox{Joules}$, the minimum  energy needed for the  node to stay alive  during one round.  This value has  been computed by  multiplying the energy consumed in  active state (9.72 mW) by the time in second  for one round (3600 seconds). According to the  interval of initial energy, a sensor may be alive during at most 20 rounds.
 
 \subsection{Metrics}

-\label{ch5:sec:03:02}
+\label{ch5:sec:04:02}

 To evaluate our approach we consider the following performance metrics:
 
 \begin{enumerate}[i]
 To evaluate our approach we consider the following performance metrics:
 
 \begin{enumerate}[i]


@@ -359,7 +403,7 @@ indicate the energy consumed by the whole network in round $t$.
 
 
 \subsection{Results Analysis and Comparison }
 
 
 \subsection{Results Analysis and Comparison }

-\label{ch5:sec:03:02}
+\label{ch5:sec:04:02}

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


@@ -516,7 +560,7 @@ energy consumption,  since network lifetime and energy  consumption are directly
 
 
 \section{Conclusion}
 
 
 \section{Conclusion}

-\label{ch5:sec:04}
+\label{ch5:sec:05}

 
 We have addressed  the problem of the coverage and of the lifetime optimization in wireless  sensor networks.  This is  a key  issue as  sensor nodes  have limited resources in terms of memory, energy, and computational power. To cope with this problem,  the field  of sensing  is divided  into smaller  subregions  using the concept  of divide-and-conquer  method, and  then  we propose  a protocol  which optimizes coverage  and lifetime performances in each  subregion.  Our protocol,
 called MuDiLCO (Multiround  Distributed Lifetime Coverage Optimization) combines two  efficient   techniques:  network   leader  election  and   sensor  activity scheduling.
 
 We have addressed  the problem of the coverage and of the lifetime optimization in wireless  sensor networks.  This is  a key  issue as  sensor nodes  have limited resources in terms of memory, energy, and computational power. To cope with this problem,  the field  of sensing  is divided  into smaller  subregions  using the concept  of divide-and-conquer  method, and  then  we propose  a protocol  which optimizes coverage  and lifetime performances in each  subregion.  Our protocol,
 called MuDiLCO (Multiround  Distributed Lifetime Coverage Optimization) combines two  efficient   techniques:  network   leader  election  and   sensor  activity scheduling.