Update by Ali

[ThesisAli.git] / CHAPITRE_01.tex

diff --git a/CHAPITRE_01.tex b/CHAPITRE_01.tex
index ba0cb78872f69b9578ed4065cd29d1528a9f4a2f..f74170b39eecf173cc9c00d8a11b1467336af65c 100644 (file)
--- a/CHAPITRE_01.tex
+++ b/CHAPITRE_01.tex
@@ -98,7 +98,7 @@ Wireless sensor nodes are deployed over the land constructing a network of hundr
 Nodes are deployed over caves, mines, or underground and communicate through soil~\cite{ref9,ref10}. The most important applications in underground WSNs are structural monitoring, agriculture monitoring, landscape management, underground environment monitoring of soil, water or mineral and military border monitoring. The essential challenges of underground WSNs are the high levels of attenuation and signal loss in communication. Therefore, it needs a certain type of devices able to provide a robust wireless underground communication. The risk on these devices comes from unsuitable underground conditions, replacing or recharging the battery seems to be impossible, and the WSN deployment is expensive. 
 
 \item \textbf{Underwater WSNs:} 
 Nodes are deployed over caves, mines, or underground and communicate through soil~\cite{ref9,ref10}. The most important applications in underground WSNs are structural monitoring, agriculture monitoring, landscape management, underground environment monitoring of soil, water or mineral and military border monitoring. The essential challenges of underground WSNs are the high levels of attenuation and signal loss in communication. Therefore, it needs a certain type of devices able to provide a robust wireless underground communication. The risk on these devices comes from unsuitable underground conditions, replacing or recharging the battery seems to be impossible, and the WSN deployment is expensive. 
 
 \item \textbf{Underwater WSNs:} 

-This type of WSNs is composed of wireless sensor nodes deployed in the water such as the ocean~\cite{ref11,ref12}. Many challenges must be faced in this type of WSN such as the high cost of the underwater sensor devices; underwater wireless communication with limited bandwidth, high latency, signal fading, and long propagation delay problems; sparse deployment in which the wireless sensors should be able to self-organized to adapt to various condition of the ocean environment; the limited power of the node battery, and the difficulty to replace or recharge it. These challenges led to look for energy efficient underwater wireless communication mechanisms. The main underwater WSNs applications are seismic monitoring, disaster prevention monitoring, underwater robotics, pollution monitoring, equipment monitoring, and undersea surveillance and exploration. \\
+This type of WSNs is composed of wireless sensor nodes deployed in the water such as the ocean~\cite{ref11,ref12}. Many challenges must be faced in this type of WSN such as the high cost of the underwater sensor devices; underwater wireless communication with limited bandwidth, high latency, signal fading, and long propagation delay problems; sparse deployment in which the wireless sensors should be able to self-organized to adapt to various condition of the ocean environment; the limited power of the node battery, and the difficulty to replace or recharge it. These challenges lead to look for energy efficient underwater wireless communication mechanisms. The main underwater WSNs applications are seismic monitoring, disaster prevention monitoring, underwater robotics, pollution monitoring, equipment monitoring, and undersea surveillance and exploration. \\

 
 \item \textbf{Multimedia WSNs:} 
 They consist of inexpensive wireless sensor nodes supplied with CMOS (Complementary Metal-Oxide-Silicon) cameras or  microphones devices. The nodes are deployed in a pre-guided way to ensure the coverage. Multimedia WSN is capable of retrieving and storing audio, video, and image contents from the physical environment~\cite{ref13,ref14,ref15}.  Multimedia WSN contributed in improving some existing WSN applications such as tracking and monitoring.  The main challenges in multimedia WSN include:  the processing, filtering, and compressing of multimedia data; the requested bandwidth and high energy consumption; Quality-of-Service provisioning is very difficult because of the link capacity and delays; it should combine different wireless techniques; energy-efficient cross-layer design; it needs flexible architecture to support various applications; and the deployment is based on the multimedia devices coverage. 
 
 \item \textbf{Multimedia WSNs:} 
 They consist of inexpensive wireless sensor nodes supplied with CMOS (Complementary Metal-Oxide-Silicon) cameras or  microphones devices. The nodes are deployed in a pre-guided way to ensure the coverage. Multimedia WSN is capable of retrieving and storing audio, video, and image contents from the physical environment~\cite{ref13,ref14,ref15}.  Multimedia WSN contributed in improving some existing WSN applications such as tracking and monitoring.  The main challenges in multimedia WSN include:  the processing, filtering, and compressing of multimedia data; the requested bandwidth and high energy consumption; Quality-of-Service provisioning is very difficult because of the link capacity and delays; it should combine different wireless techniques; energy-efficient cross-layer design; it needs flexible architecture to support various applications; and the deployment is based on the multimedia devices coverage. 


@@ -143,7 +143,7 @@ The wireless sensors can be used in agricultural services like irrigation, ferti
 WSNs can be incorporated into military command, control, communication, computing, intelligence,
 surveillance, reconnaissance, and targeting systems. It permits to estimate the unexpected events such as  natural disasters and threats; military surveillance to the battlefield, enemy forces, battle damage, and targeting; and nuclear, biological, and chemical attack detection and reconnaissance~\cite{ref19}. 
 
 WSNs can be incorporated into military command, control, communication, computing, intelligence,
 surveillance, reconnaissance, and targeting systems. It permits to estimate the unexpected events such as  natural disasters and threats; military surveillance to the battlefield, enemy forces, battle damage, and targeting; and nuclear, biological, and chemical attack detection and reconnaissance~\cite{ref19}. 
 

-\indent According to Figure~\ref{WSNAP}, the public safety and military applications can be categorized into active intervention and passive supervision~\cite{ref22}. In active intervention systems, the wireless sensors are wore by the agents and the WSN devoted to the security of the team activities. During the work of the team, the leader will observe the agent's situation and the environmental factors. The main applications include emergency rescue teams, miners, and soldiers. In passive supervision systems, wireless static sensors are scattered over a large field in order to monitor a civil area or nuclear site for a longer time. These applications include surveillance and target tracking; emergency navigation; fire detection in a building; structural health monitoring; and natural disaster prevention such as in the case of tsunamis, eruptions or flooding.
+\indent According to Figure~\ref{WSNAP}, the public safety and military applications can be categorized into active intervention and passive supervision~\cite{ref22}. In active intervention systems, the wireless sensors are worn by the agents and the WSN devoted to the security of the team activities. During the work of the team, the leader will observe the agent's situation and the environmental factors. The main applications include emergency rescue teams, miners, and soldiers. In passive supervision systems, wireless static sensors are scattered over a large field in order to monitor a civil area or nuclear site for a longer time. These applications include surveillance and target tracking; emergency navigation; fire detection in a building; structural health monitoring; and natural disaster prevention such as in the case of tsunamis, eruptions or flooding.

 
 
 \item \textbf{Transportation Systems Applications:}
 
 
 \item \textbf{Transportation Systems Applications:}


@@ -222,7 +222,7 @@ The main task of a WSN after deploying the sensor nodes in the target environmen
 \indent In this strategy, the wireless sensor nodes are grouped into several groups called clusters. Each group of wireless sensor nodes is managed by a single sensor node, which is called cluster head. The cluster head takes the responsibility for managing the activities of the wireless sensor nodes in the cluster and it communicates and coordinates with other cluster heads or with the base station in the WSN. This mechanism conserves the energy in WSNs by means of~\cite{ref43,ref22}:
 
 \begin{enumerate}[(a)]
 \indent In this strategy, the wireless sensor nodes are grouped into several groups called clusters. Each group of wireless sensor nodes is managed by a single sensor node, which is called cluster head. The cluster head takes the responsibility for managing the activities of the wireless sensor nodes in the cluster and it communicates and coordinates with other cluster heads or with the base station in the WSN. This mechanism conserves the energy in WSNs by means of~\cite{ref43,ref22}:
 
 \begin{enumerate}[(a)]

-\item Grouping wireless sensor nodes into clusters led to decrease the communication range within the cluster. Therefore, the energy needed for communication among the nodes inside the cluster is minimized.
+\item Grouping wireless sensor nodes into clusters lead to decrease the communication range within the cluster. Therefore, the energy needed for communication among the nodes inside the cluster is minimized.

 \item Minimizing the energy-hungry operations such as collaboration and aggregation to the cluster head.
 %\item Limiting the number of communications (transmitting and receiving) due to the fusion operation carried out by the cluster head.
 \item The continuous changing of cluster head according to the residual energy led to balance the energy consumption among wireless sensor nodes inside the cluster.
 \item Minimizing the energy-hungry operations such as collaboration and aggregation to the cluster head.
 %\item Limiting the number of communications (transmitting and receiving) due to the fusion operation carried out by the cluster head.
 \item The continuous changing of cluster head according to the residual energy led to balance the energy consumption among wireless sensor nodes inside the cluster.


@@ -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)] 
 \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}
 \begin{equation}
 C_{xy}\left(s_i \right)  = \left \{ 
 \begin{array}{l l}


@@ -442,7 +442,7 @@ where $R_u$ is a measure of the uncertainty in sensor detection, $\alpha = d(s_i
 
 \end{enumerate}
 
 
 \end{enumerate}
 

-The coverage protocols proposed in this dissertation use the binary disc sensing model for each wireless sensor node in a WSN because it is widely used in the literature. Moreover, it is easy to formulate the linear programs with it, whereas the probabilistic model is more complex and it is difficult to use it to create integer programs.
+The coverage protocols proposed in this dissertation use the binary disc sensing model for each wireless sensor node in a WSN because it is widely used in the literature. Moreover, it is easy to formulate linear programs with it, whereas the probabilistic model is more complex. % and it is difficult to use it to create integer programs.

 
 
 %The coverage protocols have proposed in this dissertation use the binary disc sensing model as a sensing coverage model for each wireless sensor node in WSN.
 
 
 %The coverage protocols have proposed in this dissertation use the binary disc sensing model as a sensing coverage model for each wireless sensor node in WSN.


@@ -460,7 +460,7 @@ The coverage protocols proposed in this dissertation use the binary disc sensing
 \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 in a friendly environment with a small number of sensor nodes. The random placement is preferred for a large number of sensor nodes or when the area of interest is inaccessible or hostile. 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 $\textbf{Deployment Method}$ 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 in a friendly environment with a small number of sensor nodes. The random placement is preferred for a large number of sensor nodes or when the area of interest is inaccessible or hostile. 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 if it is covered 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 $\textbf{Coverage Degree}$ 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-covered if it is covered 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 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 $\textbf{Coverage Ratio}$ 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.
 


@@ -469,7 +469,7 @@ The coverage protocols proposed in this dissertation use the binary disc sensing
 
 %Activity based Scheduling schedules the activation and deactivation of sensor nodes during the network lifetime.
  
 
 %Activity based Scheduling schedules the activation and deactivation of sensor nodes during the network lifetime.
  

-\item $\textbf{Activity based Scheduling}$ schedules 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.
+\item $\textbf{Activity based Scheduling}$ schedules the activation and deactivation of sensor nodes during the network lifetime. The basic objective is to decide which sensors are in which 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.

 \textbf{This dissertation deals with activity based scheduling to ensure the best coverage}.
 
 \end{enumerate}
 \textbf{This dissertation deals with activity based scheduling to ensure the best coverage}.
 
 \end{enumerate}


@@ -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. 
 \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 developed another energy consumption model that based on \cite{ref112}}.
+\textbf{In this dissertation, we have based  the energy consumption model on \cite{ref112}}.

 %\subsection{Our Energy Consumption Model:}
 %\label{ch1:sec9:subsec2}
 
 %\subsection{Our Energy Consumption Model:}
 %\label{ch1:sec9:subsec2}