X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/ThesisAli.git/blobdiff_plain/bb06163c8d122bfd6baf424927a264670a40b29e..375ced8f49cc67bebfe08000ab361ed616c55ad5:/CHAPITRE_01.tex?ds=sidebyside

diff --git a/CHAPITRE_01.tex b/CHAPITRE_01.tex
index 5ba6c61..f74170b 100644
--- a/CHAPITRE_01.tex
+++ b/CHAPITRE_01.tex
@@ -33,7 +33,7 @@ A WSN includes a large number of sensor nodes that can sense, process, and trans
 
 \section{Architecture} 
 \label{ch1:sec:02}
-A typical WSN architecture consists in a set of a huge number of wireless sensor nodes, which are capable of sensing the surrounded physical phenomenon such as fire in the forest (see~figure~\ref{wsn}), and then send the sensed data to a sink node. One or more sinks in WSN are responsible for collecting and processing the received sensed data, and making them available through the Internet to the end-user. 
+A typical WSN architecture consists in a set of a huge number of wireless sensor nodes, which are capable of sensing the surrounded physical phenomenon such as fire in the forest (see~Figure~\ref{wsn}), and then send the sensed data to a sink node. One or more sinks in WSN are responsible for collecting and processing the received sensed data, and making them available through the Internet to the end-user. 
 
 The basic element is a wireless sensor node that is composed of four major units~\cite{ref17,ref18}: sensing, computation, communication, and power. 
 %In addition, there are three optional units, which can be combined with the sensor node such as localization system, mobilizer, and power generator. 
@@ -57,7 +57,7 @@ Figure~\ref{twsn} shows the components of a typical wireless sensor node~\cite{r
 
 \end{enumerate}
 
-Furthermore, additional components can be incorporated into wireless sensor node according to the application requirements, such as a localization system, a power generator, and a mobilizer~\cite{ref17,ref19}. These components are showed by the dashed boxes in figure~\ref{twsn}.
+Furthermore, additional components can be incorporated into wireless sensor node according to the application requirements, such as a localization system, a power generator, and a mobilizer~\cite{ref17,ref19}. These components are showed by the dashed boxes in Figure~\ref{twsn}.
 
 \begin{enumerate} [(I)]         
 
@@ -75,7 +75,7 @@ Furthermore, additional components can be incorporated into wireless sensor node
 \label{wsn}
 \end{figure}
 
-Sensor nodes use a software layer called, Operating System (OS), which is logically 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.
+Sensor nodes use a software layer called, Operating System (OS), which is logically 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 is commonly 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} 
@@ -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:} 
-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. 
@@ -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 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}. 
 
 \begin{figure}[h!]
 \centering
@@ -131,11 +131,8 @@ In this section, we describe different academic and commercial applications. A W
 
 
 \item \textbf{ Environment and agriculture Applications}
-\indent Several WSNs applications have been developed for precision agriculture, cattle monitoring, and environmental monitoring.
-
-\indent Precision agriculture refers to the science of using innovative and modern technologies to improve the crop production. WSNs are the main technology for developing precision agriculture~\cite{ref29}. This technology contributes to increasing the agricultural yields, improving quality, and reducing costs whilst decreasing the damaging impact on the environment. The wireless sensors are distributed over the target field so as to monitor the main parameters such as soil moisture, atmospheric temperature, and create a decision support system \cite{ref22}. 
+\indent Several WSNs applications have been developed for precision agriculture, cattle monitoring, and environmental monitoring. Precision agriculture refers to the science of using innovative and modern technologies to improve the crop production. WSNs are the main technology for developing precision agriculture~\cite{ref29}. This technology contributes to increasing the agricultural yields, improving quality, and reducing costs whilst decreasing the damaging impact on the environment. The wireless sensors are distributed over the target field so as to monitor the main parameters such as soil moisture, atmospheric temperature, and create a decision support system \cite{ref22}. 
 The wireless sensors can be used in agricultural services like irrigation, fertilization,  pest control, animal and pastures monitoring, horticulture (e.g., greenhouse and viticulture)~\cite{ref30}. For instance, in cattle monitoring applications, the WSN is used to livestock control and monitoring such as virtual fencing for extensive grazing systems, animal behavior study, health monitoring, to detect disease breakouts, to localize them, and to control end-product quality (meat, milk).
-
 \indent Various WSN applications for environmental monitoring have been used in coastline erosion,  air quality monitoring, safe drinking water, and contamination control~\cite{ref30,ref22}.
 
 %\indent In cattle monitoring applications, the WSN is used to livestock control and monitoring such as virtual fencing for extensive grazing systems, animal behavior study, health monitoring, to detect disease breakouts, to localize them, and to control end-product quality (meat, milk).  
@@ -146,7 +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}. 
 
-\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:}
@@ -203,7 +200,7 @@ The main task of a WSN after deploying the sensor nodes in the target environmen
 
 \section{Energy-Efficient Mechanisms of a working WSN}
 \label{ch1:sec:06}
-\indent The strong constraint on limiting wireless sensor nodes energy usage requires energy efficient mechanisms to prolong network lifetime. The energy efficient mechanisms can be classified into five categories~\cite{ref22}, as summarized in figure~\ref{emwsn}.
+\indent The strong constraint on limiting wireless sensor nodes energy usage requires energy efficient mechanisms to prolong network lifetime. The energy efficient mechanisms can be classified into five categories~\cite{ref22}, as summarized in Figure~\ref{emwsn}.
 \begin{figure}[h!]
 \centering
 \includegraphics[scale=0.4]{Figures/ch1/WSN-M.eps} 
@@ -225,7 +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)]
-\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.
@@ -258,7 +255,7 @@ The main task of a WSN after deploying the sensor nodes in the target environmen
 
 \subsubsection{Wake up Scheduling Schemes}
 
-\indent This section describes the scheduling schemes from the point of view of schedule composition process and the framework of the wake-up schedule. In these scheduling schemes, the wake-up interval refers to the period of time at which the radio unit is turned on to send or receive packets. On the other hand, the sleep interval refers to a period of time at which the radio unit is turned off so as to save the energy of node. Some schemes divide the time into equal length durations of time and are called slotted schemes. Other schemes work with the time in a continuous way and are called unslotted schemes. The sleep and wake up intervals are defined for the unslotted schemes, whilst for the slotted schemes, these intervals are represented as multiple slots. A wake-up schedule represents a set of a wake-up and sleep intervals which are produced for one period. This schedule is replicated for each period and it can be changed by the wake-up scheduling scheme during the different periods of time. The final goal is to permit to exchange data among the wireless sensor nodes during the wake-up interval. As shown in figure~\ref{wsns}, the requirement for synchronization categorizes the wake-up scheduling into three categories~\cite{ref57}:
+\indent This section describes the scheduling schemes from the point of view of schedule composition process and the framework of the wake-up schedule. In these scheduling schemes, the wake-up interval refers to the period of time at which the radio unit is turned on to send or receive packets. On the other hand, the sleep interval refers to a period of time at which the radio unit is turned off so as to save the energy of node. Some schemes divide the time into equal length durations of time and are called slotted schemes. Other schemes work with the time in a continuous way and are called unslotted schemes. The sleep and wake up intervals are defined for the unslotted schemes, whilst for the slotted schemes, these intervals are represented as multiple slots. A wake-up schedule represents a set of a wake-up and sleep intervals which are produced for one period. This schedule is replicated for each period and it can be changed by the wake-up scheduling scheme during the different periods of time. The final goal is to permit to exchange data among the wireless sensor nodes during the wake-up interval. As shown in Figure~\ref{wsns}, the requirement for synchronization categorizes the wake-up scheduling into three categories~\cite{ref57}:
 
 \begin{enumerate} [(I)]
 
@@ -416,7 +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}
@@ -445,7 +442,7 @@ where $R_u$ is a measure of the uncertainty in sensor detection, $\alpha = d(s_i
 
 \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.
@@ -463,7 +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 $\textbf{Coverage Degree}$ 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.
 
@@ -472,7 +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.
  
-\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}
@@ -514,7 +511,7 @@ In order to model the energy consumption, four states for a sensor node are used
 \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 across a distance d, the radio is
+\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 is
 
 
 \begin{equation}
@@ -536,7 +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 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}
 
