Update by Ali

[ThesisAli.git] / CHAPITRE_05.tex

diff --git a/CHAPITRE_05.tex b/CHAPITRE_05.tex
index 66eadc15bfb17db085d1095ce913c9eaa78cac74..d542a1460635d4f34505f9a2a6ef22e9d2b45cfd 100644 (file)
--- a/CHAPITRE_05.tex
+++ b/CHAPITRE_05.tex
@@ -4,7 +4,7 @@
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-\chapter{Multiround Distributed Lifetime Coverage Optimization Protocol in Wireless Sensor Networks}
+\chapter{Multiround Distributed Lifetime Coverage Optimization Protocol}

 \label{ch5}
 
 
 \label{ch5}
 
 


@@ -246,7 +246,7 @@ range.
 
 Our protocol  is declined into four versions: MuDiLCO-1,  MuDiLCO-3, MuDiLCO-5, and  MuDiLCO-7, corresponding  respectively to  $T=1,3,5,7$ ($T$  the  number of rounds in one sensing period).  In  the following, we will make comparisons with two other methods. DESK \cite{DESK} and GAF~\cite{GAF}.
 %Some preliminary experiments were performed in chapter 4 to study the choice of the number of subregions  which subdivides  the  sensing field,  considering different  network sizes. They show that as the number of subregions increases, so does the network lifetime. Moreover,  it makes  the MuDiLCO protocol  more robust  against random network  disconnection due  to node  failures.  However,  too  many subdivisions reduce the advantage  of the optimization. In fact, there  is a balance between the  benefit  from the  optimization  and the  execution  time  needed to  solve it. Therefore, 
 
 Our protocol  is declined into four versions: MuDiLCO-1,  MuDiLCO-3, MuDiLCO-5, and  MuDiLCO-7, corresponding  respectively to  $T=1,3,5,7$ ($T$  the  number of rounds in one sensing period).  In  the following, we will make comparisons with two other methods. DESK \cite{DESK} and GAF~\cite{GAF}.
 %Some preliminary experiments were performed in chapter 4 to study the choice of the number of subregions  which subdivides  the  sensing field,  considering different  network sizes. They show that as the number of subregions increases, so does the network lifetime. Moreover,  it makes  the MuDiLCO protocol  more robust  against random network  disconnection due  to node  failures.  However,  too  many subdivisions reduce the advantage  of the optimization. In fact, there  is a balance between the  benefit  from the  optimization  and the  execution  time  needed to  solve it. Therefore, 

-we set the number of subregions to 16 rather than 32 as explained in chapter 4, section ref{ch4:sec:04:05}. We use the modeling language and the optimization solver which are mentioned in chapter 4, section \ref{ch4:sec:04:02}. In addition, the energy consumption model is presented in chapter 4, section \ref{ch4:sec:04:03}. 
+We set the number of subregions to 16 rather than 32 as explained in chapter 4, section \ref{ch4:sec:04:05}. We use the modeling language and the optimization solver which are mentioned in chapter 4, section \ref{ch4:sec:04:02}. In addition, the energy consumption model is presented in chapter 4, section \ref{ch4:sec:04:03}. 

 
 \subsection{Metrics}
 \label{ch5:sec:04:02}
 
 \subsection{Metrics}
 \label{ch5:sec:04:02}


@@ -474,7 +474,7 @@ called MuDiLCO (Multiround  Distributed Lifetime Coverage Optimization) combines
 
 The activity  scheduling in each subregion  works in periods,  where each period consists of four  phases: (i) Information Exchange, (ii)  Leader Election, (iii) Decision Phase to plan the activity  of the sensors over $T$ rounds, (iv) Sensing Phase itself divided into T rounds.
 
 
 The activity  scheduling in each subregion  works in periods,  where each period consists of four  phases: (i) Information Exchange, (ii)  Leader Election, (iii) Decision Phase to plan the activity  of the sensors over $T$ rounds, (iv) Sensing Phase itself divided into T rounds.
 

-Simulations  results show the  relevance of  the proposed  protocol in  terms of lifetime, coverage  ratio, active  sensors ratio, energy  consumption, execution time. Indeed,  when dealing with  large wireless sensor networks,  a distributed approach, like  the one we  propose, allows to  reduce the difficulty of  a single global optimization problem by partitioning it into many smaller problems, one per subregion, that can be solved  more easily. Nevertheless, results also show that it is not possible to plan the activity of sensors over too many rounds because the resulting optimization problem leads to too high-resolution times and thus to an excessive energy consumption.
+Simulations  results show the  relevance of  the proposed  protocol in  terms of lifetime, coverage  ratio, active  sensors ratio, energy  consumption, execution time. Indeed,  when dealing with  large wireless sensor networks,  a distributed approach, like  the one we  propose, allows to  reduce the difficulty of  a single global optimization problem by partitioning it into many smaller problems, one per subregion, that can be solved  more easily. Nevertheless, results also show that it is not possible to plan the activity of sensors over too many rounds because the resulting optimization problem leads to too high-resolution times and thus to an excessive energy consumption.