X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/ThesisAli.git/blobdiff_plain/0a4831505b7eaf36f78bc512ac5c62f033fe0d59..df0ded57ac60ab03ead82d37326459a25812634a:/CHAPITRE_01.tex

diff --git a/CHAPITRE_01.tex b/CHAPITRE_01.tex
index f74170b..ddc551c 100644
--- a/CHAPITRE_01.tex
+++ b/CHAPITRE_01.tex
@@ -116,7 +116,7 @@ This kind of WSN consists of low-cost wireless sensor nodes, which are embedded
 \section{Applications}
 \label{ch1:sec:04}
 %\indent The fast development in WSNs has been led to study  their different characteristics extensively. However, the WSN is concentrated on various applications. 
-In this section, we describe different academic and commercial applications. A WSN can use various types of sensors such as \cite{ref17,ref19}: thermal, seismic, magnetic, visual, infrared, acoustic, and radar. These sensors are capable of observing a different physical conditions such as: temperature, humidity, pressure, speed, direction, movement, light, soil makeup, noise levels, presence or absence of certain kinds of objects, and mechanical stress levels on attached objects. Consequently, a wide range of WSN applications can be classified into five classes~\cite{ref22}, as shown in Figure~\ref{WSNAP}. 
+In this section, we describe different academic and commercial applications. A WSN can use various types of sensors such as \cite{ref17,ref19}: thermal, seismic, magnetic, visual, infrared, acoustic, and radar. These sensors are capable of observing  different physical conditions such as: temperature, humidity, pressure, speed, direction, movement, light, soil makeup, noise levels, presence or absence of certain kinds of objects, and mechanical stress levels on attached objects. Consequently, a wide range of WSN applications can be classified into five classes~\cite{ref22}, as shown in Figure~\ref{WSNAP}. 
 
 \begin{figure}[h!]
 \centering
@@ -413,7 +413,7 @@ A major research challenge in  WSNs, which has  been addressed by a large amount
 \indent The sensing quality and capability can be assessed by a sensing coverage model obtained through the identification of a mathematical relationship between the point and the sensor node in the sensing field. In the real world, there are sometimes obstacles in the environment that affect the sensing range \cite{ref104}. Therefore, several sensing coverage models have been suggested according to application requirements and physical working environment such as~\cite{ref103}: boolean sector coverage, boolean disk coverage, attenuated disk coverage, truncated attenuated disk, detection coverage, and estimation coverage models. However, two main sensing coverage models have been used for simulating the performance of wireless sensors~\cite{ref104,ref105,ref106}:
 
 \begin{enumerate}[(A)] 
-\item \textbf{Binary Disc Sensing Model:} It is the simplest sensing coverage model in which every point in the sensing field can be sensed if it is within the sensing range of the wireless sensor node. Otherwise, the sensor node is not able to detect any point that is outside its sensing range. The sensing range in this model can be viewed as a circular disk with a radius equal to $R_s$. Assume that a sensor node $s_i$ is deployed at the position $(x_i,y_i)$. For any point P at the position $(x,y)$, Equation \ref{eq1-ch1} shows the binary sensor model that expresses the coverage $C_{xy}$ of the point P by sensor node $s_i$ as follow
+\item \textbf{Binary Disc Sensing Model:} It is the simplest sensing coverage model in which every point in the sensing field can be sensed if it is within the sensing range of the wireless sensor node. Otherwise, the sensor node is not able to detect any point that is outside its sensing range. The sensing range in this model can be viewed as a circular disk with a radius equal to $R_s$. Assume that a sensor node $s_i$ is deployed at the position $(x_i,y_i)$. For any point P at the position $(x,y)$, equation \ref{eq1-ch1} shows the binary sensor model that expresses the coverage $C_{xy}$ of the point P by sensor node $s_i$ as follow
 \begin{equation}
 C_{xy}\left(s_i \right)  = \left \{ 
 \begin{array}{l l}
@@ -533,7 +533,7 @@ while to receive a K-bit packet, the radio is
 \noindent The typical parameters are set as: $E_{elec}$ = 50 nJ/bit, $\varepsilon_{fs}$ = 10 pJ/bit/$m^2$, $\varepsilon_{mp}$ = 0.0013 pJ/bit/$m^4$. In addition, the energy for data aggregation is set to $E_{DA}$ = 5 nJ/bit.
 
 \indent The radio energy dissipation model considers only the energy consumed by the communication part of the sensor node. However, in order to achieve a more accurate model, it is necessary to take into account the energy consumed by other parts inside the sensor node such as computation and sensing units. 
-\textbf{In this dissertation, we have based  the energy consumption model on \cite{ref112}}.
+\textbf{In this dissertation, we have based  the energy consumption model on \cite{ref112}. This model will be detailed in Section \ref{ch4:sec:04:03}}.
 %\subsection{Our Energy Consumption Model:}
 %\label{ch1:sec9:subsec2}