X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/ThesisAli.git/blobdiff_plain/fb2946597e20d8a0af9a7ecd5b81a3bb462ecea7..cf176cf7a6fb0534038168a6ed8fdfcb04d83ceb:/CHAPITRE_01.tex?ds=inline

diff --git a/CHAPITRE_01.tex b/CHAPITRE_01.tex
index 996d91d..aedf97f 100644
--- a/CHAPITRE_01.tex
+++ b/CHAPITRE_01.tex
@@ -458,26 +458,27 @@ The coverage protocols proposed in this dissertation use the binary disc sensing
 \begin{enumerate}[(i)]
 \item $\textbf{Coverage Type}$ Is the WSN dedicated to monitor a whole area, to observe a set of targets, or to look for a breach of a barrier?
  
-\item $\textbf{Deployment Method}$ refers to the way by which the wireless sensor nodes are deployed over the target sensing field in order to build the wireless sensor network. Generally, the sensor nodes can be placed either deterministically or randomly in the target sensing field~\cite{ref107}. The method of placement can be selected based on the type of sensors, application, and the environment. In the deterministic placement, the deployment can be achieved for a small number of sensor nodes and in friendly environment, whilst for a large number of sensor nodes or when the area of interest is inaccessible or hostile, a random placement is the choice. The sensor network can be either dense or sparse. The dense deployment is preferable when it is necessary to provide a security robustness in WSNs. On the other hand, the sparse deployment is used when the dense deployment is expensive or when the maximum coverage is performed by a less number of sensor nodes.
+\item $\textbf{Deployment Method}$ refers to the way by which the wireless sensor nodes are deployed over the target sensing field in order to build the wireless sensor network. Generally, the sensor nodes can be placed either deterministically or randomly in the target sensing field~\cite{ref107}. The method of placement can be selected based on the type of sensors, application, and the environment. In the deterministic placement, the deployment can be achieved for a small number of sensor nodes and in friendly environment, whilst for a large number of sensor nodes or when the area of interest is inaccessible or hostile, a random placement is the choice. The sensor network can be either dense or sparse. On the one hand, the dense deployment is preferable when it is necessary to provide a security robustness in WSNs. On the other hand, the sparse deployment is used when the dense deployment is expensive or when the maximum coverage is performed by a low number of sensor nodes.
 
 
 \item $\textbf{Coverage Degree}$ refers to how many sensor nodes are required to cover a target or an area. A point in the sensing field is said to be K-coverage by at least K sensor nodes. Some applications need a high reliability to achieve their tasks. Therefore, the sensing field is deployed densely so as to perform a K-coverage for this field. The simple coverage problem consists of a coverage degree equal to one (i.e., K=1), where every point in the sensing field is covered by at least one sensor.
 
-\item $\textbf{Coverage Ratio}$ is the percentage of the area of sensing field that fulfills the coverage degree of the application. If all the points in the sensing field are covered, the coverage ratio is $100\%$ and it can be called a complete coverage. Otherwise, it can be called as partial coverage.
+\item $\textbf{Coverage Ratio}$ is the percentage of the sensing field that fulfills the coverage degree of the application. If all the points in the sensing field are covered, the coverage ratio is $100\%$ and it can be called a complete coverage. Otherwise, it is said as partial coverage.
 
-\item $\textbf{Network Connectivity}$ is to ensure the existence of a path from any sensor node in WSN to the sink. The connected WSN refers to guarantee sending the sensed data from one sensor node to another sensor node directly toward the sink. 
+\item $\textbf{Network Connectivity}$ is to ensure the existence of a path from any sensor node in WSN to the sink. A connected WSN ensures the sending of the sending the sensed data from one sensor node to another sensor node directly toward the sink. 
 %It is necessary to consider the communication range of wireless sensor node is at least twice that of the sensing range ($R_c \geqslant 2R_s$) so as to imply connectivity among the sensor nodes during covering the sensing field~\cite{ref108}.
 
  
-\item $\textbf{Activity based Scheduling}$ is to schedule the activation and deactivation of sensor nodes during the network lifetime. The basic objective is to decide which sensors are in what states (active or sleeping mode) and for how long, so that the application coverage requirement can be guaranteed and the network lifetime can be prolonged. Various  centralized, distributed, and localized approaches have been proposed for activity scheduling. In distributed algorithms, each node in the network autonomously makes decisions on whether to turn on or turn off itself only using local neighbor information. In centralized algorithms, a central controller (a node or base station) informs every sensor of the time intervals to be activated.
-This dissertation deals with activity based scheduling to ensure the best coverage in WSNs.
+\item $\textbf{Activity based Scheduling}$ is to schedule the activation and deactivation of sensor nodes during the network lifetime. The basic objective is to decide which sensors are in what states (active or sleeping mode) and for how long, so that the application coverage requirement can be guaranteed and the network lifetime can be prolonged. Various  centralized, distributed, and localized approaches have been proposed for activity scheduling. In distributed algorithms, each node in the network autonomously makes decisions on whether to turn on or turn off itself, using only local neighbor information. In centralized algorithms, a central controller (a node or base station) informs every sensor of the time intervals to be activated.
+This dissertation deals with activity based scheduling to ensure the best coverage.
 
 \end{enumerate}
 
 \section{Energy Consumption Modeling} 
 \label{ch1:sec:9}
 %\indent The WSNs have been received a lot of interests because the low energy consumption of the sensor nodes.  
-One of the most critical issues in WSNs is to reduce the energy consumption of the limited power battery of the sensor nodes so as to prolong the network lifetime as long as possible. In order to model the energy consumption, four states for a sensor node are used~\cite{ref140}:  transmission, reception, listening, and sleeping. In addition, two states should be taken into account: computation and sensed data acquisition. The main tasks of each of these states include:
+%One of the most critical issues in WSNs is to reduce the energy consumption of the limited power battery of the sensor nodes so as to prolong the network lifetime as long as possible. 
+In order to model the energy consumption, four states for a sensor node are used~\cite{ref140}:  transmission, reception, listening, and sleeping. In addition, two states should be taken into account: computation and data acquisition. The main tasks of each of these states include:
 
 \begin{enumerate}[(i)]
 
@@ -491,7 +492,7 @@ One of the most critical issues in WSNs is to reduce the energy consumption of t
 
 \item Data Acquisition: sensing, processing sensed data, analog to digital conversion, preprocessing, and maybe storing.
 
-\item Sleeping: a low power to the sensor node to stay alive.
+\item Sleeping: a low power level allowing the sensor node to stay alive.
 
 
 
@@ -510,7 +511,7 @@ One of the most critical issues in WSNs is to reduce the energy consumption of t
 \label{RDM}
 \end{figure}
 
-\indent In this model, the radio consumes energy to execute the transmitter and the power amplifier. The receiver circuitry consumes energy to run the radio electronics, as described in figure~\ref{RDM}.  The channel model can be either free space ($d^2$ power loss) or multipath fading ($d^4$ power loss), based on the distance between the transmitter and receiver. This power loss can be controlled by setting the power amplifier so that if the distance is less than a threshold ($d_0$), the free space ($\varepsilon_{fs}$) model is used (i.e., $\varepsilon_{amp}$ = $\varepsilon_{fs}$); Otherwise, the multipath ($\varepsilon_{mp}$) model is used (i.e., $\varepsilon_{amp}$ = $\varepsilon_{mp}$). Therefore, to transmit a K-bit packet with a distance d, the radio expends
+\indent In this model, the radio consumes energy to execute the transmitter and the power amplifier. The receiver circuitry consumes energy to run the radio electronics, as described in figure~\ref{RDM}.  The channel model can be either free space ($d^2$ power loss) or multipath fading ($d^4$ power loss), based on the distance between the transmitter and receiver. This power loss can be controlled by setting the power amplifier so that if the distance is less than a threshold ($d_0$), the free space ($\varepsilon_{fs}$) model is used (i.e., $\varepsilon_{amp}$ = $\varepsilon_{fs}$). Otherwise, the multipath ($\varepsilon_{mp}$) model is used (i.e., $\varepsilon_{amp}$ = $\varepsilon_{mp}$). Therefore, to transmit a K-bit packet across a distance d, the radio expends
 
 
 \begin{equation}
@@ -522,17 +523,17 @@ E_{tx}\left(K,d \right)  = \left \{
 \label{eq3-ch1} 
 \end{equation}
 
-As well as to receive a K-bit packet, the radio expends
+while to receive a K-bit packet, the radio expends
 
 \begin{equation}
       E_{rx}\left(k,d \right)  =   \emph{ KE_{elec} }.   
 \label{eq4-ch1} 
 \end{equation}
 
-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 as $E_{DA}$ = 5 nJ/bit.
+\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 as $E_{DA}$ = 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 unit and sensing unit. 
-In this dissertation, we developed another energy consumption model that proposed by~\cite{DESK} and based on \cite{ref112}.
+\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. 
+In this dissertation, we developed another energy consumption model that based on \cite{ref112}.
 %\subsection{Our Energy Consumption Model:}
 %\label{ch1:sec9:subsec2}
 
@@ -541,4 +542,6 @@ In this dissertation, we developed another energy consumption model that propose
 \section{Conclusion}
 \label{ch1:sec:10}
 
-\indent In this chapter, an overview of the wireless sensor networks has been presented. Unlike traditional ad-hoc networks, WSNs are collaborative and very oriented toward a specific application domain. The structure of the typical wireless sensor network and the main components of the sensor nodes have been detailed. Several types of wireless sensor networks are described. Various fields of applications covering a wide spectrum for a WSNs including health, home, environmental, military, and industrial applications have been presented.  As shown, since sensor nodes have limited battery life; since it is impossible to replace batteries, especially in remote and hostile environments; the limited power of a battery represents the critical challenge in WSNs. The main challenges in WSNs have explained. Energy efficiency is the primary challenge to increase the network lifetime. Therefore, the energy efficient solutions have proposed in order to handle that challenge. Many energy efficient mechanisms have been illustrated, which are aimed to reduce the energy consumption of the different parts of the wireless sensor nodes in WSNs. The definition of the network lifetime has been presented and in different contexts.  The problem of the coverage in WSNs is explained.  One of the main scientific research challenges in WSNs is how to build energy efficient coverage protocols. This chapter highlights the main design issues for the coverage problems that need to be considered in designing a coverage protocol for WSNs. In addition, the energy consumption modeling have been illustrated. 
\ No newline at end of file
+\indent In this chapter an overview of the wireless sensor networks has been presented. Unlike traditional ad-hoc networks, WSNs are collaborative and very oriented toward a specific application domain. The structure of the typical wireless sensor network and the main components of the sensor nodes have been detailed. Several types of wireless sensor networks are described. Various fields of applications covering a wide spectrum including health, home, environmental, military, and industrial applications have been presented.  As shown, since sensor nodes have limited battery life; since it is impossible to replace batteries, especially in remote and hostile environments; the limited power of a battery represents the critical challenge in WSNs. The main challenges in WSNs have been explained. Energy efficiency is the primary challenge to increase the network lifetime. Therefore, energy efficient solutions have been proposed in order to handle that challenge. Many energy efficient mechanisms have been illustrated, which are aimed to reduce the energy consumption of the different parts of the wireless sensor nodes. The definition of the network lifetime has been presented in different contexts.  The problem of the coverage in WSNs is also explained.  
+%One of the main scientific research challenges in WSNs is how to build energy efficient coverage protocols. 
+This chapter highlights the main design issues that need to be considered when designing an energy efficient coverage protocol for WSNs. In addition, energy consumption models have been discussed. 
\ No newline at end of file
