Update by Ali

[ThesisAli.git] / CHAPITRE_05.tex

diff --git a/CHAPITRE_05.tex b/CHAPITRE_05.tex
index 85655683a31d321137951dc729695ba77c9ad427..918ec9055139bbe4671dd34a326ba3b58b7266ac 100644 (file)
--- a/CHAPITRE_05.tex
+++ b/CHAPITRE_05.tex
@@ -92,7 +92,7 @@ The difference with MuDiLCO in that the elected leader in each subregion is for
 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
 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. \textcolor{blue}{The flowchart of MuDiLCO protocol executed in each sensor node is presented in Figure \ref{flow5}.} 
+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. The flowchart of MuDiLCO protocol executed in each sensor node is presented in Figure \ref{flow5}.

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


@@ -106,11 +106,6 @@ period  after  Information~Exchange  and  Leader~Election phases,  in  order to
 
 %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.
 
 
 %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.
 

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 \section{Primary Points based Multiround Coverage Problem Formulation}
 \label{ch5:sec:03}
 
 \section{Primary Points based Multiround Coverage Problem Formulation}
 \label{ch5:sec:03}
 


@@ -121,6 +116,8 @@ period  after  Information~Exchange  and  Leader~Election phases,  in  order to
 
 We extend the mathematical formulation given in section \ref{ch4:sec:03} to take into account multiple rounds.
 
 
 We extend the mathematical formulation given in section \ref{ch4:sec:03} to take into account multiple rounds.
 

+\newpage
+

 For a  primary point  $p$, let $\alpha_{j,p}$  denote the indicator  function of
 whether the point $p$ is covered, that is
 \begin{equation}
 For a  primary point  $p$, let $\alpha_{j,p}$  denote the indicator  function of
 whether the point $p$ is covered, that is
 \begin{equation}


@@ -439,4 +436,4 @@ In this chapter, we have presented a protocol, called MuDiLCO (Multiround  Distr
 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. Compared with DiLCO, it is clear that MuDiLCO improves the network lifetime especially for the dense network, but it is  less robust than DiLCO under sensor nodes failures. Therefore, choosing the number of rounds $T$ depends on the type of application the WSN is deployed for. 
 
 
 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. Compared with DiLCO, it is clear that MuDiLCO improves the network lifetime especially for the dense network, but it is  less robust than DiLCO under sensor nodes failures. Therefore, choosing the number of rounds $T$ depends on the type of application the WSN is deployed for. 
 
 

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