X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/ThesisAli.git/blobdiff_plain/e2fea957f33c77ba4585e64d92c0c22d80a90d9a..585c6ba024142990d0e8889792b49ca87292c3ee:/CHAPITRE_01.tex?ds=sidebyside

diff --git a/CHAPITRE_01.tex b/CHAPITRE_01.tex
index a49ca9d..ec27e65 100644
--- a/CHAPITRE_01.tex
+++ b/CHAPITRE_01.tex
@@ -13,7 +13,7 @@
 \label{ch1:sec:01}
 %The wireless networking has received more attention and fast growth in the last decade.
 In the last decade, wireless networking has became a major component of the global network infrastructure.
-More precisely, the growing demand for the use of wireless applications and the continuous arrival of wireless devices such as portable computers, cellular phones, and personal digital assistants (PDAs) have led to develop different infrastructures of wireless networks. The wireless networks can be classified into two classes based on the network architecture~\cite{ref154,ref155}: Infrastructure-based networks that consist of a fixed network structure such as cellular networks and wireless local-area networks
+More precisely, the growing demand for the use of wireless applications and the continuous arrival of wireless devices such as portable computers, cellular phones, and Personal Digital Assistants (PDAs) have led to develop different infrastructures of wireless networks. The wireless networks can be classified into two classes based on the network architecture~\cite{ref154,ref155}: Infrastructure-based networks that consist of a fixed network structure such as cellular networks and Wireless Local-Area Networks
 (WLANs); and Infrastructureless networks that are constructed dynamically by the cooperation of the wireless nodes in the network, where each node is capable of sending packets and taking decisions based on the network status. Examples of such type of networks include mobile ad hoc networks and wireless sensor networks. Figure~\ref{WNT} shows the taxonomy of wireless networks.
 
 \begin{figure}[h!]
@@ -41,7 +41,7 @@ Figure~\ref{twsn} shows the components of a typical wireless sensor node~\cite{r
 
 \begin{figure}[h!]
 \centering
-\includegraphics[scale=0.5]{Figures/ch1/twsn2.pdf} 
+\includegraphics[scale=0.52]{Figures/ch1/twsn2.pdf} 
 \caption{ Components of a typical wireless sensor node.}
 \label{twsn}
 \end{figure}
@@ -51,7 +51,7 @@ Figure~\ref{twsn} shows the components of a typical wireless sensor node~\cite{r
 
 \item \textbf{Computation Unit:} The main purpose of this unit is to manage and manipulate the instructions that are related to sensing, communication, and self-organization. This allows the sensor node to cooperate with other sensor nodes in order to perform the allocated sensing tasks. It is composed of a processor chip, an active short-term memory for storing the sensed data, an internal flash memory for storing program instructions, and an internal timer.
 
-\item \textbf{Communication Unit:} It is responsible for all data transmission and reception done by the sensor node, which are performed by the transceiver circuitry. A transceiver circuit is composed of a mixer, frequency synthesizer, voltage-controlled oscillator (VCO), phase-locked loop (PLL), demodulator, and power amplifiers. All these components consume valuable power~\cite{ref19}.
+\item \textbf{Communication Unit:} It is responsible for all data transmission and reception done by the sensor node, which are performed by the transceiver circuitry. A transceiver circuit is composed of a mixer, frequency synthesizer, Voltage-Controlled Oscillator (VCO), Phase-Locked Loop (PLL), demodulator, and power amplifiers. All these components consume valuable power~\cite{ref19}.
 
 \item \textbf{Power Unit:} This unit represents the most significant part of a sensor node.  It supplies the other units by the needed power. 
 
@@ -75,7 +75,7 @@ Furthermore, additional components can be incorporated into wireless sensor node
 \label{wsn}
 \end{figure}
 
-The sensor node use software layer that logically locates between the node's hardware and the application  called, An operating system (OS)~\cite{ref18}. OS enables the applications to interact with hardware resources, to schedule and prioritize tasks, memory management, power management, file management, networking, and to arbitrate between contending applications and services that attempt to reserve resources. The TinyOS has been used as an operating system in wireless sensor node. It is developed by the university of California, Berkeley and designed to work on platforms with limited storage and processing power.
+The sensor node use a software layer called, Operating System (OS), is logically locates between the node's hardware and the application layer~\cite{ref18}. The OS enables the applications to interact with hardware resources, to schedule and prioritize tasks, memory management, power management, file management, networking, and to arbitrate between contending applications and services that attempt to reserve resources. The TinyOS has been used as an operating system in wireless sensor node. It is developed by the university of California, Berkeley and designed to work on platforms with limited storage and processing power.
 
 
 \section{Types of Wireless Sensor Networks} 
@@ -342,9 +342,9 @@ They concentrate on the energy consumption reduction in the sensing unit. These
 \indent In the last years, extensive researches have been focused on energy harvesting and wireless charging techniques. These solutions represent alternate energy sources to recharge wireless sensor batteries without human intervention~\cite{ref91,ref59}.
 
 \begin{enumerate} [i)]
-\item{Energy Harvesting} In energy harvesting, several sources of environmental energy have been developed so as to enable the wireless sensors to acquire energy from the surrounding environment. These energy sources are solar, wind energy, vibration based energy harvesting, radio signals for scavenging RF power, thermoelectric generators, and shoe-mounted piezoelectric generator to power artificial organs~\cite{ref59}. 
+\item{Energy Harvesting:} In energy harvesting, several sources of environmental energy have been developed so as to enable the wireless sensors to acquire energy from the surrounding environment. These energy sources are solar, wind energy, vibration based energy harvesting, radio signals for scavenging RF power, thermoelectric generators, and shoe-mounted piezoelectric generator to power artificial organs~\cite{ref59}. 
 
-\item{Wireless Charging}In wireless charging, the power can be transmitted between the devices without requiring a connection between the transmitter and the receiver. These techniques participate in increasing the availability of WSNs and prolonging the network lifetime. Wireless charging in WSNs can be performed in two ways: magnetic resonant coupling and electromagnetic radiation~\cite{ref22}.
+\item{Wireless Charging:} In wireless charging, the power can be transmitted between the devices without requiring a connection between the transmitter and the receiver. These techniques participate in increasing the availability of WSNs and prolonging the network lifetime. Wireless charging in WSNs can be performed in two ways: magnetic resonant coupling and electromagnetic radiation~\cite{ref22}.
 
 \end{enumerate}
 
@@ -416,8 +416,7 @@ 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}
@@ -430,8 +429,7 @@ C_{xy}\left(s_i \right)  = \left \{
 where $d(s_i,P) = \sqrt{(x_i - x)^2 + (y_i - y)^2}$ denotes the Euclidean distance between sensor node $s_i$ and P. 
 
 
-\item \textbf{Probabilistic Sensing Model}
-In reality, an event detection by a sensor node is imprecise. Hence, the coverage $C_{xy}$ requires to be represented in a probabilistic way. The probabilistic sensing model is more practical and can be used as an extension of the binary disc sensing model. Equation \ref{eq2-ch1} shows the probabilistic sensing model that expresses the coverage $C_{xy}$ of the point P by the sensor node $s_i$ as follow
+\item \textbf{Probabilistic Sensing Model:} In reality, an event detection by a sensor node is imprecise. Hence, the coverage $C_{xy}$ requires to be represented in a probabilistic way. The probabilistic sensing model is more practical and can be used as an extension of the binary disc sensing model. Equation \ref{eq2-ch1} shows the probabilistic sensing model that expresses the coverage $C_{xy}$ of the point P by the sensor node $s_i$ as follow
 
 \begin{equation}
 C_{xy}\left(s_i \right)  = \left \{ 
