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[ThesisAli.git] / CHAPITRE_03.tex

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-%%       CHAPITRE 03        %%
+%%       CHAPTER 03        %%

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@@ -381,11 +381,14 @@ interest of $(50 \times 25)~m^2 $ in such a way that they cover the field with a
 high coverage ratio.
 
 
 high coverage ratio.
 
 

+\subsection{Modeling  Language and Optimization Solver}
+\label{ch3:sec:04:02}
+The modeling  language for Mathematical Programming (AMPL)~\cite{AMPL} is  employed to generate the integer program instance  in a  standard format, which  is then read  and solved  by the optimization solver  GLPK (GNU  linear Programming Kit  available in  the public domain) \cite{glpk} through a Branch-and-Bound method.

 
 \subsection{Energy Consumption Model}
 
 \subsection{Energy Consumption Model}

-\label{ch3:sec:04:02}
+\label{ch3:sec:04:03}

 
 

-\indent In this dissertation, we have used an energy consumption model proposed by~\cite{ref111} and based on \cite{ref112} with slight  modifications.  The energy consumption for  sending/receiving the packets is added, whereas the  part related to the sensing range is removed because we consider a fixed sensing range.
+\indent In this dissertation, we used an energy consumption model proposed by~\cite{ref111} and based on \cite{ref112} with slight  modifications.  The energy consumption for  sending/receiving the packets is added, whereas the  part related to the sensing range is removed because we consider a fixed sensing range.

 
 \indent For our energy consumption model, we refer to the sensor node Medusa~II which uses an Atmels  AVR ATmega103L microcontroller~\cite{ref112}. The typical architecture  of a  sensor  is composed  of four  subsystems: the  MCU subsystem which is capable of computation, communication subsystem (radio) which is responsible  for transmitting/receiving messages, the  sensing subsystem that collects  data, and  the  power supply  which  powers the  complete sensor  node \cite{ref112}. Each  of the first three subsystems  can be turned on or  off depending on  the current status  of the sensor.   Energy consumption (expressed in  milliWatt per second) for  the different status of  the sensor is summarized in Table~\ref{table1}.
 
 
 \indent For our energy consumption model, we refer to the sensor node Medusa~II which uses an Atmels  AVR ATmega103L microcontroller~\cite{ref112}. The typical architecture  of a  sensor  is composed  of four  subsystems: the  MCU subsystem which is capable of computation, communication subsystem (radio) which is responsible  for transmitting/receiving messages, the  sensing subsystem that collects  data, and  the  power supply  which  powers the  complete sensor  node \cite{ref112}. Each  of the first three subsystems  can be turned on or  off depending on  the current status  of the sensor.   Energy consumption (expressed in  milliWatt per second) for  the different status of  the sensor is summarized in Table~\ref{table1}.
 


@@ -427,7 +430,7 @@ The initial energy of each node  is randomly set in the interval $[500;700]$.  A
 
 
 \subsection{Performance Metrics}
 
 
 \subsection{Performance Metrics}

-\label{ch3:sec:04:03}  
+\label{ch3:sec:04:04}  

 In the simulations,  we introduce the following performance metrics to evaluate
 the efficiency of our approach:
 
 In the simulations,  we introduce the following performance metrics to evaluate
 the efficiency of our approach:
 


@@ -496,7 +499,7 @@ Where: $A_r$ is the number of active sensors in the subregion $r$ during current
 
 
 \subsection{Performance Analysis for Different Subregions}
 
 
 \subsection{Performance Analysis for Different Subregions}

-\label{ch3:sec:04:04}
+\label{ch3:sec:04:05}

   
 In this subsection, we are studied the performance of our DiLCO protocol for a different number of subregions (Leaders).
 The DiLCO-1 protocol is a centralized approach on all the area of the interest, while  DiLCO-2, DiLCO-4, DiLCO-8, DiLCO-16 and DiLCO-32 are distributed on two, four, eight, sixteen, and thirty-two subregions respectively. We did not take the DiLCO-1 protocol in our simulation results because it need high execution time to give the decision leading to consume all it's energy before producing the solution for optimization problem.
   
 In this subsection, we are studied the performance of our DiLCO protocol for a different number of subregions (Leaders).
 The DiLCO-1 protocol is a centralized approach on all the area of the interest, while  DiLCO-2, DiLCO-4, DiLCO-8, DiLCO-16 and DiLCO-32 are distributed on two, four, eight, sixteen, and thirty-two subregions respectively. We did not take the DiLCO-1 protocol in our simulation results because it need high execution time to give the decision leading to consume all it's energy before producing the solution for optimization problem.


@@ -599,7 +602,7 @@ Comparison shows that DiLCO-16 protocol, which uses 16 leaders, is the best one
 
 
 \subsection{Performance Analysis for Primary Point Models}
 
 
 \subsection{Performance Analysis for Primary Point Models}

-\label{ch3:sec:04:03}
+\label{ch3:sec:04:06}

 
 In this section, we are studied the performance of DiLCO~16 approach for a different primary point models. The objective of this comparison is to select the suitable primary point model to be used by DiLCO protocol. 
 
 
 In this section, we are studied the performance of DiLCO~16 approach for a different primary point models. The objective of this comparison is to select the suitable primary point model to be used by DiLCO protocol. 
 


@@ -699,7 +702,7 @@ Comparison shows that the Model~1, which uses less number of primary points, is
 
 
 \subsection{Performance Comparison with other Approaches}
 
 
 \subsection{Performance Comparison with other Approaches}

-\label{ch3:sec:04:04}
+\label{ch3:sec:04:07}

 Based on the results, which are conducted from previous two subsections, \ref{ch3:sec:04:02} and \ref{ch3:sec:04:03}, we have found that DiLCO-16 protocol and DiLCO-32 protocol with Model~2 are the best candidates to be compared with other two approaches. The first approach, called DESK that proposed by ~\cite{DESK}, which is a full distributed coverage algorithm. The second approach, called GAF~\cite{GAF}, consists in dividing the region into fixed squares.   During the decision phase, in each square, one sensor is chosen to remain on during the sensing phase time. 
 
 \subsubsection{Coverage Ratio} 
 Based on the results, which are conducted from previous two subsections, \ref{ch3:sec:04:02} and \ref{ch3:sec:04:03}, we have found that DiLCO-16 protocol and DiLCO-32 protocol with Model~2 are the best candidates to be compared with other two approaches. The first approach, called DESK that proposed by ~\cite{DESK}, which is a full distributed coverage algorithm. The second approach, called GAF~\cite{GAF}, consists in dividing the region into fixed squares.   During the decision phase, in each square, one sensor is chosen to remain on during the sensing phase time. 
 
 \subsubsection{Coverage Ratio}