X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/ThesisAli.git/blobdiff_plain/946555d85197e77a0b8f06b8ee4e4d2b6c788f3d..dd42ca97656c19804fa0624b8e9095293f58976f:/CHAPITRE_03.tex

diff --git a/CHAPITRE_03.tex b/CHAPITRE_03.tex
index ecf1b35..68b15be 100644
--- a/CHAPITRE_03.tex
+++ b/CHAPITRE_03.tex
@@ -1,6 +1,6 @@
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 %%                          %%
-%%       CHAPITRE 03        %%
+%%       CHAPTER 03        %%
 %%                          %%
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
@@ -380,13 +380,57 @@ 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
 high coverage ratio.
 
-We chose as energy consumption model the one described in chapter 1, section \ref{ch1:sec9:subsec2}. Each node  has an initial  energy level, in  Joules, which is randomly  drawn in $[500-700]$.   If its  energy  provision  reaches a  value  below the  threshold $E_{th}=36$~Joules, the minimum  energy needed for a node  to stay active during
-one  period, it  will  no longer  take part  in  the coverage  task. This  value corresponds to the  energy needed by the sensing  phase, obtained by multiplying the energy  consumed in active state  (9.72 mW) by  the time in seconds  for one period  (3,600 seconds),  and  adding  the energy  for  the pre-sensing  phases.
-According to  the interval of initial energy,  a sensor may be  active during at most 20 periods.
+
+\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}
+\label{ch3:sec:04:03}
+
+\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}.
+
+\begin{table}[ht]
+\caption{The Energy Consumption Model}
+% title of Table
+\centering
+% used for centering table
+\begin{tabular}{|c|c|c|c|c|}
+% centered columns (4 columns)
+      \hline
+%inserts double horizontal lines
+Sensor status & MCU & Radio & Sensing & Power (mW) \\ [0.5ex]
+\hline
+% inserts single horizontal line
+LISTENING & on & on & on & 20.05 \\
+% inserting body of the table
+\hline
+ACTIVE & on & off & on & 9.72 \\
+\hline
+SLEEP & off & off & off & 0.02 \\
+\hline
+COMPUTATION & on & on & on & 26.83 \\
+%\hline
+%\multicolumn{4}{|c|}{Energy needed to send/receive a 1-bit} & 0.2575\\
+ \hline
+\end{tabular}
+
+\label{table1}
+% is used to refer this table in the text
+\end{table}
+
+\indent For the sake of simplicity we ignore  the energy needed to turn on the radio, to start up the sensor node, to move from one status to another, etc. Thus, when a sensor becomes active (i.e., it has already chosen its status), it can turn  its radio  off to  save battery. The value of energy spent to send a 1-bit-content message is  obtained by using  the equation in ~\cite{ref112} to calculate  the energy cost for transmitting  messages and  we propose  the same value for receiving the packets. The energy  needed to send or receive a 1-bit packet is equal to $0.2575~mW$.
+
+
+%We have used an energy consumption model, which is presented in chapter 1, section \ref{ch1:sec9:subsec2}. 
+
+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 $E_{th}=36~\mbox{Joules}$, 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), and  adding  the energy  for  the pre-sensing  phases. According to the  interval of initial energy, a sensor may be alive during at most 20 rounds.
 
 
 \subsection{Performance Metrics}
-\label{ch3:sec:04:02}  
+\label{ch3:sec:04:04}  
 In the simulations,  we introduce the following performance metrics to evaluate
 the efficiency of our approach:
 
@@ -455,7 +499,7 @@ Where: $A_r$ is the number of active sensors in the subregion $r$ during current
 
 
 \subsection{Performance Analysis for Different Subregions}
-\label{ch3:sec:04:03}
+\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.
@@ -558,7 +602,7 @@ Comparison shows that DiLCO-16 protocol, which uses 16 leaders, is the best one
 
 
 \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. 
 
@@ -658,7 +702,7 @@ Comparison shows that the Model~1, which uses less number of primary points, is
 
 
 \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} 
