update by Ali

[ThesisAli.git] / CHAPITRE_01.tex

diff --git a/CHAPITRE_01.tex b/CHAPITRE_01.tex
index 58e5aff2619ac1b3f4feb358cdeabaad78566c80..479138935469052d8b6b6f5d6cb08e8f910fe1c8 100644 (file)
--- a/CHAPITRE_01.tex
+++ b/CHAPITRE_01.tex
@@ -64,7 +64,7 @@ Furthermore, additional components can be incorporated into wireless sensor node
 
 \begin{figure}[h!]
 \centering
 
 \begin{figure}[h!]
 \centering

-\includegraphics[scale=0.9]{Figures/ch1/wsn.jpg} 
+\includegraphics[scale=1.2]{Figures/ch1/wsn.jpg} 

 \caption{ Wireless sensor network architecture.}
 \label{wsn}
 \end{figure}
 \caption{ Wireless sensor network architecture.}
 \label{wsn}
 \end{figure}


@@ -176,7 +176,7 @@ nodes in order to achieve their tasks efficiently.
 
 \item \textbf{Topology Control:} The maintenance and repair of the network topology is a challenging task due to the large number of inaccessible sensor nodes that are prone to failure. Therefore, some schemes need to be used to deal with the dynamic changing of topology and the failure of some sensor nodes due to energy depletion or malfunction.
 
 
 \item \textbf{Topology Control:} The maintenance and repair of the network topology is a challenging task due to the large number of inaccessible sensor nodes that are prone to failure. Therefore, some schemes need to be used to deal with the dynamic changing of topology and the failure of some sensor nodes due to energy depletion or malfunction.
 

-\item \textbf{Heterogeneity:} One essential challenge is to provide a WSN protocol that deals with different sensor node capabilities such as communication, processing, sensing, and energy. The future of WSNs will be heterogeneous with a large number of sensor nodes. These WSNs may reflect different tasks and can be integrated into one big network. Therefore, it is necessary to take the heterogeneity into consideration during the design stage of  WSNs protocols. 
+\item \textbf{Heterogeneity:} One essential challenge is to provide a WSN protocol that deals with different sensor node capabilities such as communication, processing, sensing, and energy. The future networks will be heterogeneous with a large number of sensor nodes. These WSNs may reflect different tasks and can be integrated into one big network. Therefore, it is necessary to take the heterogeneity into consideration during the design stage of  WSNs protocols. 

 
 \item \textbf{Wireless Networking:} The networking and wireless communication represent another important challenge in WSNs. The communication range of the signals can be attenuated or faded during the signal propagation across the communication media or during passing through obstacles. The increasing distance between the sensor nodes and the sink requires increased transmission power. However, the long distance can be divided into several small distances using multi-hop communication. The multi-hop communication generates another challenge that is how to find the more energy efficient route to transmit the information from the source to the destination. The sensor nodes should cooperate to find this route and to serve as relays.
 
 
 \item \textbf{Wireless Networking:} The networking and wireless communication represent another important challenge in WSNs. The communication range of the signals can be attenuated or faded during the signal propagation across the communication media or during passing through obstacles. The increasing distance between the sensor nodes and the sink requires increased transmission power. However, the long distance can be divided into several small distances using multi-hop communication. The multi-hop communication generates another challenge that is how to find the more energy efficient route to transmit the information from the source to the destination. The sensor nodes should cooperate to find this route and to serve as relays.
 


@@ -273,10 +273,10 @@ The majority of synchronous schemes work in periodic (cyclic) way by preparing t
 \item \textbf{Asynchronous Schemes:} The time among the wireless sensor nodes does not need synchronization. The wireless sensor node wakes up to send packets without taking into account whether the receiving sensor nodes are waked up and ready to receive. These schemes do not need time synchronization which consumes energy~\cite{ref74}. They do not need to exploit the limited resources (processing, memory, and radio) of the sensor nodes because there are no  shared wake up schedules to be exchanged or saved in the memory. Therefore,  exchanging the packets among the wireless sensor nodes, which are not aware of each other's wake-up schedules have considered as a major challenge in asynchronous schemes. These schemes can been categorized into three groups~\cite{ref57}: 
  
 \begin{enumerate} [(A)]
 \item \textbf{Asynchronous Schemes:} The time among the wireless sensor nodes does not need synchronization. The wireless sensor node wakes up to send packets without taking into account whether the receiving sensor nodes are waked up and ready to receive. These schemes do not need time synchronization which consumes energy~\cite{ref74}. They do not need to exploit the limited resources (processing, memory, and radio) of the sensor nodes because there are no  shared wake up schedules to be exchanged or saved in the memory. Therefore,  exchanging the packets among the wireless sensor nodes, which are not aware of each other's wake-up schedules have considered as a major challenge in asynchronous schemes. These schemes can been categorized into three groups~\cite{ref57}: 
  
 \begin{enumerate} [(A)]

-\item Transmitter-initiated: a special frame is sent by the transmitting sensor node to inform the receiving sensor node that it has a data frame to send.  If the receiving sensor node is hearing the special frame during one of its wake up intervals, it waits for sending the data frame. The major advantage of these schemes is the low requirement of the memory and processing whilst the major disadvantages are low-duty-cycle and the non-deterministic sleep latency. 
+\item Transmitter-initiated: a special frame is sent by the transmitting sensor node to inform the receiving sensor node that it has a data frame to send.  If the receiving sensor node is hearing the special frame during one of its wake up intervals, the receiving node waits for sending the data frame to receive it. The major advantage of these schemes is the low requirement of the memory and processing whilst the major disadvantages are low-duty-cycle and the non-deterministic sleep latency. 

 %Examples on these schemes in~\cite{ref75,ref76}.
 
 %Examples on these schemes in~\cite{ref75,ref76}.
 

-\item Receiver-initiated: during each wake-up time interval of receiving wireless sensor node, it sends a special frame to inform the senders, which are waked up and ready to receive the data frames.  When the sensor node has a packet to send, it wakes up and wait receiving special frame from neighboring sensor nodes. The waiting sensor node is sending its data frame at the moment of receiving the special frame from the neighbor sensor node. The main advantages are a low processing and storage requirement whilst the disadvantage of these schemes are the low performance during low and high duty cycle, as well as their stochastic sleep latency. 
+\item Receiver-initiated: during each wake-up time interval of receiving wireless sensor node, it sends a special frame to inform the senders, which of senders are waked up and ready to receive the data frames.  When the sensor node has a packet to send, it wakes up and wait receiving special frame from neighboring sensor nodes. The waiting sensor node is sending its data frame at the moment of receiving the special frame from the neighbor sensor node. The main advantages are a low processing and storage requirement whilst the disadvantage of these schemes are the low performance during low and high duty cycle, as well as their stochastic sleep latency. 

 %The works that proposed in~\cite{ref77,ref78} represents some instances for these approaches.
 
 \item Combinatorial or random: during the wake-up duration, one or more data packets can be exchanged among the wireless sensor nodes. The exchanged data packets among the wireless sensor nodes are increased as the wake-up time increased. In these schemes, the special frames are removed and the energy consumption is decreased. A common disadvantage of these schemes is the non-deterministic sleep
 %The works that proposed in~\cite{ref77,ref78} represents some instances for these approaches.
 
 \item Combinatorial or random: during the wake-up duration, one or more data packets can be exchanged among the wireless sensor nodes. The exchanged data packets among the wireless sensor nodes are increased as the wake-up time increased. In these schemes, the special frames are removed and the energy consumption is decreased. A common disadvantage of these schemes is the non-deterministic sleep


@@ -304,7 +304,7 @@ latency.
 
 \subsection{Data-Driven Schemes}
 
 
 \subsection{Data-Driven Schemes}
 

-\indent Data-driven approaches aim to decrease the amount of data sent to the sink whilst maintaining the accuracy of sensing within an acceptable level. Therefore, removing unwanted data during the transmission and restriction the sensing tasks during data acquisition can be participating in reducing the energy consumption in WSNs. 
+\indent Data-driven approaches aim to decrease the amount of data sent to the sink whilst maintaining the accuracy of sensing within an acceptable level. Therefore, removing unwanted data during the transmission and restricting the sensing tasks during data acquisition can be participating in reducing the energy consumption in WSNs. 

 %Several data-driven schemes have been proposed in~\cite{ref86,ref87,ref88,ref89,ref90}.
 Data driven schemes are classified into two main approaches~\cite{ref59,ref22}:
  
 %Several data-driven schemes have been proposed in~\cite{ref86,ref87,ref88,ref89,ref90}.
 Data driven schemes are classified into two main approaches~\cite{ref59,ref22}:
  


@@ -370,7 +370,9 @@ In the WSNs including a static sink, the wireless sensor nodes, which are near t
 \item The time during which the application requirement is satisfied~\cite{ref172}.
 \end{enumerate}
 
 \item The time during which the application requirement is satisfied~\cite{ref172}.
 \end{enumerate}
 

-\indent According to the above definitions for network lifetime, there is no universal definition to reflect the requirements of each application and the effects of the environment. In real WSN, the network lifetime reflects a set of a particular circumstances of the environment. Accordingly, the current definitions are applicable for the WSNs that meet a particular conditions. However, many more parameters, which are affecting on the network lifetime of WSN such as~\cite{ref92}:  heterogeneity, node mobility, topology changes, application characteristics, quality of service, and completeness.
+\indent According to the above definitions for network lifetime, there is no universal definition to reflect the requirements of each application and the effects of the environment. In real WSN, the network lifetime reflects a set of a particular circumstances of the environment. 
+%Accordingly, the current definitions are applicable for the WSNs that meet a particular conditions. 
+Many parameters are affecting the network lifetime should be taken into account~\cite{ref92}:  heterogeneity, node mobility, topology changes, application characteristics, quality of service, and completeness.

 
 The network lifetime has been defined in this dissertation as the time spent by WSN until the coverage ratio becomes less than a predetermined threshold $\alpha$.
 
 
 The network lifetime has been defined in this dissertation as the time spent by WSN until the coverage ratio becomes less than a predetermined threshold $\alpha$.
 


@@ -383,7 +385,7 @@ One of the scientific research challenges in  WSNs, which has  been addressed by
 \begin{enumerate}[(i)] 
 \item \textbf{Area coverage}~\cite{ref97,ref101} where every point inside an area has to be monitored. The work in this dissertation deals with this type of coverage.
 \item \textbf{Target coverage}~\cite{ref98,ref101} where the main objective is to cover only a finite number of discrete points called targets.
 \begin{enumerate}[(i)] 
 \item \textbf{Area coverage}~\cite{ref97,ref101} where every point inside an area has to be monitored. The work in this dissertation deals with this type of coverage.
 \item \textbf{Target coverage}~\cite{ref98,ref101} where the main objective is to cover only a finite number of discrete points called targets.

-\item \textbf{Barrier coverage}~\cite{ref99,ref100} to prevent intruders from entering into the region of interest. 
+\item \textbf{Barrier coverage}~\cite{ref99,ref100} where the main goal is to detect targets as they cross a barrier, which is usually a long belt region such as intrusion detection and border surveillance applications.  

 \end{enumerate}
 
 \indent The sensing quality and capability can be assessed by a sensing coverage models due to discovering the 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}:
 \end{enumerate}
 
 \indent The sensing quality and capability can be assessed by a sensing coverage models due to discovering the 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}:


@@ -420,7 +422,8 @@ 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 as a sensing coverage model for each wireless sensor node in WSN because we consider for each sensor a set of primary points and we assume that the sensing disk defined by a sensor is covered if all the primary points of this sensor are covered.
+The coverage protocols proposed in this dissertation use the binary disc sensing model as a sensing coverage model for each wireless sensor node in WSN because it is a widely used in the literatures, as well as it is easy to formulate the linear programs with it. While 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.
 


@@ -446,6 +449,7 @@ The coverage protocols proposed in this dissertation use the binary disc sensing
 
  
 \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.
 
  
 \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.

 
 \end{enumerate}
 
 
 \end{enumerate}
 


@@ -485,33 +489,34 @@ One of the most critical issues in WSNs is to reduce the energy consumption of t
 \label{RDM}
 \end{figure}
 
 \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, 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 with a distance d, the radio expends

 
 
 \begin{equation}
 
 
 \begin{equation}

-E_{tx}\left(k,d \right)  = \left \{ 
+E_{tx}\left(K,d \right)  = \left \{ 

 \begin{array}{l l}
 \begin{array}{l l}

-  \emph{ kE_{elec} + k \varepsilon_{fs}d^2}  & \mbox{if $d < d_0$} \\
-  \emph{ kE_{elec} + k \varepsilon_{mp}d^4}  &  \mbox{if $d \geqslant d_0$}\\
+  \emph{ KE_{elec} + K \varepsilon_{fs}d^2}  & \mbox{if $d < d_0$} \\
+  \emph{ KE_{elec} + K \varepsilon_{mp}d^4}  &  \mbox{if $d \geqslant d_0$}\\

 \end{array} \right.
 \label{eq3-ch1} 
 \end{equation}
 
 \end{array} \right.
 \label{eq3-ch1} 
 \end{equation}
 

-As well as to receive a k-bit packet, the radio expends
+As well as to receive a K-bit packet, the radio expends

 
 \begin{equation}
 
 \begin{equation}

-      E_{rx}\left(k,d \right)  =   \emph{ kE_{elec} }.   
+      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.
 
 \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. 
 \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.
 
 \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}.

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

+

 \section{Conclusion}
 \label{ch1:sec:10}
 
 \section{Conclusion}
 \label{ch1:sec:10}