Update by Ali

diff --git a/CHAPITRE_01.tex b/CHAPITRE_01.tex
index 64f492942926cfdb735e2d60ec3161e60d51d5b5..f7f9eaad1d42ad7c7e384f8c161a82331a7f5de9 100644 (file)
--- a/CHAPITRE_01.tex
+++ b/CHAPITRE_01.tex
@@ -17,7 +17,7 @@ The wireless networking has been receiving more attention and fast growth in the
 \begin{figure}[h!]
 \centering
 %\includegraphics[scale=0.4]{Figures/ch1/WNT.eps} 
 \begin{figure}[h!]
 \centering
 %\includegraphics[scale=0.4]{Figures/ch1/WNT.eps} 

-\includegraphics[scale=0.5]{Figures/ch1/WSNT.jpg} 
+\includegraphics[scale=0.7]{Figures/ch1/WSNT.jpg} 

 \caption{ The taxonomy of wireless networks.}
 \label{WNT}
 \end{figure}
 \caption{ The taxonomy of wireless networks.}
 \label{WNT}
 \end{figure}


@@ -374,7 +374,7 @@ where $d(s_i,P) = \sqrt{(x_i - x)^2 + (y_i - y)^2}$, denotes the Euclidean dista
 
 
 \item \textbf{The Probabilistic Sensing Model}
 
 
 \item \textbf{The Probabilistic Sensing Model}

-In reality, the event detection by sensor node is imprecise; therefore, the coverage $C_{xy}$ requires to be represented in probabilistic manner. The probabilistic sensing model is more practical which can used as an extension for the binary disc sensing model. The equation \ref{eq2-ch1} shows the probabilistic sensing model that expresses the coverage $C_{xy}$ of the point P by sensor node $s_i$.
+In reality, the event detection by sensor node is imprecise; therefore, the coverage $C_{xy}$ requires to be represented in probabilistic manner. The probabilistic sensing model is more practical, which can be used as an extension for the binary disc sensing model. The equation \ref{eq2-ch1} shows the probabilistic sensing model that expresses the coverage $C_{xy}$ of the point P by sensor node $s_i$.

 
 \begin{equation}
 C_{xy}\left(s_i \right)  = \left \{ 
 
 \begin{equation}
 C_{xy}\left(s_i \right)  = \left \{ 


@@ -390,7 +390,7 @@ where $R_u$ is a measure of the uncertainty in sensor detection, $\alpha = d(s_i
 
 \end{enumerate}
 
 
 \end{enumerate}
 

-The coverage protocols that proposed in this dissertation have been used the binary disc sensing model.
+The coverage protocols that proposed in this dissertation have been used the binary disc sensing model as a sensing coverage model for each wireless sensor node in WSN.

 
 
 \section{Design Issues for Coverage Problems:}
 
 
 \section{Design Issues for Coverage Problems:}


@@ -400,34 +400,32 @@ The coverage protocols that proposed in this dissertation have been used the bin
 \begin{enumerate}[(i)]
 \item $\textbf{Coverage Type}$ refers to determining what is it exactly that you are trying to cover. Typically, it may be required to monitor a whole area, observe a set of targets, or look for a breach among a barrier. 
   
 \begin{enumerate}[(i)]
 \item $\textbf{Coverage Type}$ refers to determining what is it exactly that you are trying to cover. Typically, it may be required to monitor a whole area, observe a set of targets, or look for a breach among 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 so as to construct the wireless sensor network~\cite{ref107}. The method of placing the sensor nodes can be selected based on the type of sensors, application, and the environment, which the wireless sensor nodes will work in it. In the deterministic placing, the deployment can be achieved in case of small number of sensor nodes and in friendly environment, whilst for a large number of sensor nodes or the area of interest is Inaccessible or hostile, a random placing is the choice. The sensor network can be either dense or sparse. the dense deployment is preferred when it is important to detect the event or when it is required that the area covered by more than one sensor node. 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 so as to construct the wireless sensor network~\cite{ref107}. The method of placing the sensor nodes can be selected based on the type of sensors, application, and the environment in which the wireless sensor nodes will work. In the deterministic placing, the deployment can be achieved in case of small number of sensor nodes and in friendly environment, whilst for a large number of sensor nodes; or the area of interest is Inaccessible or hostile, a random placing is the choice. The sensor network can be either dense or sparse. the dense deployment is preferred when it is important to detect the event or when it is required that the area covered by more than one sensor node. 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 $\textbf{Coverage Degree}$ refers to how many sensor nodes required it to cover a target or an area. This can be described as K-coverage in which the point in the sensing field is covered by at least K sensor nodes. There are some applications that need a high reliability to achieve their tasks, so the sensing field have been 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 only one sensor.
+\item $\textbf{Coverage Degree}$ refers to how many sensor nodes are required to cover a target or an area. This can be described as K-coverage in which the point in the sensing field 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 only one sensor.

 
 \item $\textbf{Coverage Ratio}$ is the percentage of the area of sensing field that fulfill 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 area of sensing field that fulfill 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 $\textbf{Network Connectivity}$ is to ensure the existence 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 toward directly to 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 $\textbf{Network Connectivity}$ is to ensure the existence 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. 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. 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 approaches, including centralized, distributed, and localized algorithms, 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 sensors 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 approaches, including centralized, distributed, and localized algorithms, 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 sensors of the time intervals to be activated.

 
 \end{enumerate}
 
 \section{Energy Consumption Models:} 
 \label{ch1:sec:9}
 
 \end{enumerate}
 
 \section{Energy Consumption Models:} 
 \label{ch1:sec:9}

-\indent The WSNs have been received a lot of interest because their low energy consumption sensor nodes.  Since the sensor node has a limited power battery; so, one of the most critical issues in WSNs is how to reduce the energy consumption of 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 have been used~\cite{ref140}:  transmission, reception, listening, and sleeping; and we can add another two states that should be taken into account: computation and sensed data acquisition. The main tasks of each of these states include:
+\indent The WSNs have been received a lot of interest because their low energy consumption sensor nodes.  Since the sensor node has a limited power battery; therefore, one of the most critical issues in WSNs is how to reduce the energy consumption of 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 have been used~\cite{ref140}:  transmission, reception, listening, and sleeping; in addition, two states that should be taken into account: computation and sensed data acquisition. The main tasks of each of these states include:

 
 \begin{enumerate}[(i)]
 
 
 \begin{enumerate}[(i)]
 

-\item Computation: processing needed for clustering and executing any algorithm inside the sensor node.  The processing that required to  physical communication and networking protocols is included in reception and transmission.
+\item Computation: processing needed for clustering and executing any algorithm inside the sensor node.  The processing that required to physical communication and networking protocols is included in reception and transmission.

 
 

-\item  Transmission:  processing for address determination, packetization, encoding, framing, and maybe queuing; supply for the baseband and RF circuitry.
+\item Transmission:  processing for address determination, packetization, encoding, framing, and maybe queuing; supply for the baseband and RF circuitry.

 
 

-\item  Reception:  Low-noise amplifier, downconverter oscillator, filtering, detection, decoding, error detection, and address check; reception even if a node is not the intended receiver
+\item Reception:  Low-noise amplifier, downconverter oscillator, filtering, detection, decoding, error detection, and address check; reception even if a node is not the intended receiver.

 
 

-\item  Listening: Similar to reception except that the signal processing chain stops at the detection.
+\item Listening: Similar to reception except that the signal processing chain stops at the detection.

 
 \item Data Acquisition: sensing, processing sensed data, A/D conversion, preprocessing, and maybe storing.
 
 
 \item Data Acquisition: sensing, processing sensed data, A/D conversion, preprocessing, and maybe storing.
 


@@ -440,7 +438,7 @@ In this section, two energy consumption models are explained. The first model ca
 
 \subsection{Radio Energy Dissipation Model:}
 \label{ch1:sec9:subsec1}
 
 \subsection{Radio Energy Dissipation Model:}
 \label{ch1:sec9:subsec1}

-\indent Since the communication unit is the most energy-consuming part inside the sensor node, and accordingly there are many authors used the radio energy dissipation model that proposed in~\cite{ref109,ref110} as energy consumption model during the simulation and evaluation of their works in WSNs. Figure~\ref{RDM} shows the radio energy dissipation model.
+\indent Since the communication unit is the most energy-consuming part inside the sensor node, therefore, many authors are used the radio energy dissipation model that proposed in~\cite{ref109,ref110} as energy consumption model during the simulation and evaluation of their works in WSNs. Figure~\ref{RDM} shows the radio energy dissipation model.

 \begin{figure}[h!]
 \centering
 \includegraphics[scale=0.4]{Figures/ch1/RDM.eps} 
 \begin{figure}[h!]
 \centering
 \includegraphics[scale=0.4]{Figures/ch1/RDM.eps} 


@@ -468,7 +466,7 @@ As well as to receive an k-bit packet, the radio expends
 
 The typical parameters are set as: $E_{elec}$ = 50 nJ/bit, $\varepsilon_{fs}$ = 10 pJ/bit/$m^2$, $\varepsilon_{fs}$ = 0.0013 pJ/bit/$m^4$. In addition, the energy for data aggregation is set as $E_{DA}$ = 5 nJ/bit.
 
 
 The typical parameters are set as: $E_{elec}$ = 50 nJ/bit, $\varepsilon_{fs}$ = 10 pJ/bit/$m^2$, $\varepsilon_{fs}$ = 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 have been considered only the energy consumed by the communication part inside the sensor node; however, in order to achieve a more accurate model, it is necessary to take into account the energy consumed by the other parts inside the sensor node such as: computation unit and sensing unit. 
+\indent The radio energy dissipation model have been considered only the energy, which is consumed by the communication part inside the sensor node; however, in order to achieve a more accurate model, it is necessary to take into account the energy is consumed by the other parts inside the sensor node such as: computation unit and sensing unit. 

 
 
 \subsection{Our Energy Consumption Model:}
 
 
 \subsection{Our Energy Consumption Model:}


@@ -512,4 +510,4 @@ COMPUTATION & on & on & on & 26.83 \\
 \section{Conclusion}
 \label{ch1:sec:10}
 
 \section{Conclusion}
 \label{ch1:sec:10}
 

-\indent In this chapter, an overview about the wireless sensor networks have been presented that represent our focus in this dissertation.  The structure of the the typical wireless sensor network and the main components of the sensor nodes have been demonstrated. Several types of wireless sensor networks are described. There are several fields of application covering a wide spectrum for a WSN have been presented, including health, home, environmental, military, and industrial applications.  As demonstrated, 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; on the other hand, the energy efficient solutions have been proposed in order to handle these challenges Through energy conservation to prolong the network lifetime. There are many energy efficient mechanisms have been illustrated that  aiming to reduce the energy consumption by the different units 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 is explained, where constructing energy efficient coverage protocols one of the main scientific research challenges in WSNs. This chapter highlights the main design issues for the coverage problems that need to be considered during designing coverage protocol for WSNs. In additional, some energy consumption models have been demonstrated. 
+\indent In this chapter, an overview about the wireless sensor networks have been presented that represent our focus in this dissertation.  The structure of the the typical wireless sensor network and the main components of the sensor nodes have been demonstrated. Several types of wireless sensor networks are described. Various fields of applications covering a wide spectrum for a WSNs have been presented, including health, home, environmental, military, and industrial applications.  As demonstrated, 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; on the other hand, the energy efficient solutions have been proposed in order to handle these challenges through energy conservation to prolong the network lifetime. Many energy efficient mechanisms have been illustrated, which are aimed to reduce the energy consumption by the different units 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 is explained, where constructing energy efficient coverage protocols one of the main scientific research challenges in WSNs. This chapter highlights the main design issues for the coverage problems that need to be considered during designing a coverage protocol for WSNs. In addition, some energy consumption models have been demonstrated. 

diff --git a/Thesis.toc b/Thesis.toc
index bd9d8acf17e605ae717d093933ae4bbbb5eec165..e76a9aa45dac1607ca93c6608b7ad0ea53a3f928 100644 (file)
--- a/Thesis.toc
+++ b/Thesis.toc
@@ -18,10 +18,10 @@
 \contentsline {section}{\numberline {1.5}The Main Challenges in Wireless Sensor Networks}{9}{section.1.5}
 \contentsline {section}{\numberline {1.6}Energy-Efficient Mechanisms in Wireless Sensor Networks}{11}{section.1.6}
 \contentsline {subsection}{\numberline {1.6.1}Energy-Efficient Routing:}{11}{subsection.1.6.1}
 \contentsline {section}{\numberline {1.5}The Main Challenges in Wireless Sensor Networks}{9}{section.1.5}
 \contentsline {section}{\numberline {1.6}Energy-Efficient Mechanisms in Wireless Sensor Networks}{11}{section.1.6}
 \contentsline {subsection}{\numberline {1.6.1}Energy-Efficient Routing:}{11}{subsection.1.6.1}

-\contentsline {subsubsection}{\numberline {1.6.1.1}Routing Metric based on Residual Energy:}{11}{subsubsection.1.6.1.1}
+\contentsline {subsubsection}{\numberline {1.6.1.1}Routing Metric based on Residual Energy:}{12}{subsubsection.1.6.1.1}

 \contentsline {subsubsection}{\numberline {1.6.1.2}Multipath Routing:}{12}{subsubsection.1.6.1.2}
 \contentsline {subsection}{\numberline {1.6.2}Cluster Architectures:}{12}{subsection.1.6.2}
 \contentsline {subsubsection}{\numberline {1.6.1.2}Multipath Routing:}{12}{subsubsection.1.6.1.2}
 \contentsline {subsection}{\numberline {1.6.2}Cluster Architectures:}{12}{subsection.1.6.2}

-\contentsline {subsection}{\numberline {1.6.3}Scheduling Schemes:}{12}{subsection.1.6.3}
+\contentsline {subsection}{\numberline {1.6.3}Scheduling Schemes:}{13}{subsection.1.6.3}

 \contentsline {subsubsection}{\numberline {1.6.3.1}Wake up Scheduling Schemes:}{13}{subsubsection.1.6.3.1}
 \contentsline {subsubsection}{\numberline {1.6.3.2}Topology Control Schemes:}{15}{subsubsection.1.6.3.2}
 \contentsline {subsection}{\numberline {1.6.4}Data-Driven Schemes:}{16}{subsection.1.6.4}
 \contentsline {subsubsection}{\numberline {1.6.3.1}Wake up Scheduling Schemes:}{13}{subsubsection.1.6.3.1}
 \contentsline {subsubsection}{\numberline {1.6.3.2}Topology Control Schemes:}{15}{subsubsection.1.6.3.2}
 \contentsline {subsection}{\numberline {1.6.4}Data-Driven Schemes:}{16}{subsection.1.6.4}


@@ -32,7 +32,7 @@
 \contentsline {subsubsection}{\numberline {1.6.5.2}Wireless Charging:}{17}{subsubsection.1.6.5.2}
 \contentsline {subsection}{\numberline {1.6.6}Radio Optimization:}{17}{subsection.1.6.6}
 \contentsline {subsection}{\numberline {1.6.7}Relay nodes and Sink Mobility:}{17}{subsection.1.6.7}
 \contentsline {subsubsection}{\numberline {1.6.5.2}Wireless Charging:}{17}{subsubsection.1.6.5.2}
 \contentsline {subsection}{\numberline {1.6.6}Radio Optimization:}{17}{subsection.1.6.6}
 \contentsline {subsection}{\numberline {1.6.7}Relay nodes and Sink Mobility:}{17}{subsection.1.6.7}

-\contentsline {subsubsection}{\numberline {1.6.7.1}Relay node placement:}{17}{subsubsection.1.6.7.1}
+\contentsline {subsubsection}{\numberline {1.6.7.1}Relay node placement:}{18}{subsubsection.1.6.7.1}

 \contentsline {subsubsection}{\numberline {1.6.7.2}Sink Mobility:}{18}{subsubsection.1.6.7.2}
 \contentsline {section}{\numberline {1.7}Network Lifetime in Wireless Sensor Networks}{18}{section.1.7}
 \contentsline {section}{\numberline {1.8}Coverage in Wireless Sensor Networks }{19}{section.1.8}
 \contentsline {subsubsection}{\numberline {1.6.7.2}Sink Mobility:}{18}{subsubsection.1.6.7.2}
 \contentsline {section}{\numberline {1.7}Network Lifetime in Wireless Sensor Networks}{18}{section.1.7}
 \contentsline {section}{\numberline {1.8}Coverage in Wireless Sensor Networks }{19}{section.1.8}


@@ -40,7 +40,7 @@
 \contentsline {section}{\numberline {1.10}Energy Consumption Models:}{21}{section.1.10}
 \contentsline {subsection}{\numberline {1.10.1}Radio Energy Dissipation Model:}{22}{subsection.1.10.1}
 \contentsline {subsection}{\numberline {1.10.2}Our Energy Consumption Model:}{23}{subsection.1.10.2}
 \contentsline {section}{\numberline {1.10}Energy Consumption Models:}{21}{section.1.10}
 \contentsline {subsection}{\numberline {1.10.1}Radio Energy Dissipation Model:}{22}{subsection.1.10.1}
 \contentsline {subsection}{\numberline {1.10.2}Our Energy Consumption Model:}{23}{subsection.1.10.2}

-\contentsline {section}{\numberline {1.11}Conclusion}{23}{section.1.11}
+\contentsline {section}{\numberline {1.11}Conclusion}{24}{section.1.11}

 \contentsline {chapter}{\numberline {2}Related Literatures}{25}{chapter.2}
 \contentsline {section}{\numberline {2.1}Introduction}{25}{section.2.1}
 \contentsline {section}{\numberline {2.2}Coverage Algorithms}{26}{section.2.2}
 \contentsline {chapter}{\numberline {2}Related Literatures}{25}{chapter.2}
 \contentsline {section}{\numberline {2.1}Introduction}{25}{section.2.1}
 \contentsline {section}{\numberline {2.2}Coverage Algorithms}{26}{section.2.2}