Update by Ali 19-1-2015 at 01h49

[ThesisAli.git] / CHAPITRE_01.tex

diff --git a/CHAPITRE_01.tex b/CHAPITRE_01.tex
index e31b93a671edf636a2bf7350ceb44be8da7aa162..de7e0d338bb9ed45f79900f35b0449cef60a7f51 100644 (file)
--- a/CHAPITRE_01.tex
+++ b/CHAPITRE_01.tex
@@ -11,7 +11,7 @@
 
 \section{Introduction}
 \label{ch1:sec:01}
 
 \section{Introduction}
 \label{ch1:sec:01}

-The wireless networking has been receiving more attention and fast growth in the last decade. The growing demand for the use of wireless applications and emerging the wireless devices such as portable computers, cellular phones, and personal digital assistants (PDAs) have been led to develop different infrastructures of wireless networks. The wireless networks can be classified into two classes based on network architecture~\cite{ref154,ref155}: Infrastructure-based networks that consists of  a fixed network structure such as cellular networks and wireless local-area networks
+The wireless networking has been receiving more attention and fast growth in the last decade. The growing demand for the use of wireless applications and emerging the wireless devices such as portable computers, cellular phones, and personal digital assistants (PDAs) have been led to develop different infrastructures of wireless networks. The wireless networks can be classified into two classes based on network architecture~\cite{ref154,ref155}: Infrastructure-based networks that consists of a fixed network structure such as cellular networks and wireless local-area networks

 (WLANs); and Infrastructureless networks that constructed dynamically by the cooperation of the wireless nodes in the network, where each node capable of sending the packets and taking the decision based on the network status. Examples for such type of networks include mobile ad hoc networks and wireless sensor networks. Figure~\ref{WNT} shows the taxonomy of wireless networks.
 
 \begin{figure}[h!]
 (WLANs); and Infrastructureless networks that constructed dynamically by the cooperation of the wireless nodes in the network, where each node capable of sending the packets and taking the decision based on the network status. Examples for such type of networks include mobile ad hoc networks and wireless sensor networks. Figure~\ref{WNT} shows the taxonomy of wireless networks.
 
 \begin{figure}[h!]


@@ -27,7 +27,9 @@ In recent years, there is increasing interest in Wireless Sensor Networks (WSNs)
 
 \section{Wireless Sensor Network Architecture} 
 \label{ch1:sec:02}
 
 \section{Wireless Sensor Network Architecture} 
 \label{ch1:sec:02}

-In a typical WSN architecture, the basic element is a typical wireless sensor node that composed of four major units~\cite{ref17,ref18}: sensing unit, computation unit, communication unit, and power unit. In addition, there are three optional units, which can be combined with the sensor node such as: localization system, mobilizer, and power generator. Figure~\ref{twsn} shows the components of a typical wireless sensor node~\cite{ref17}.
+A typical WSN architecture consists of a set of a typical wireless sensor nodes, which are capable of sensing the physical phenomenon around it such as fire in the forest (see~figure~\ref{wsn}), and then send the sensed data to a controller node called a sink. One or more sink in WSN are responsible for collecting and processing the sensed data by the wireless sensors, and then send it through the Internet to the end user. 
+
+In those WSN architecture, the basic element is a typical wireless sensor node that composed of four major units~\cite{ref17,ref18}: sensing unit, computation unit, communication unit, and power unit. In addition, there are three optional units, which can be combined with the sensor node such as: localization system, mobilizer, and power generator. Figure~\ref{twsn} shows the components of a typical wireless sensor node~\cite{ref17}.

 
 \begin{figure}[h!]
 \centering
 
 \begin{figure}[h!]
 \centering


@@ -55,9 +57,6 @@ Furthermore, additional components can be incorporated into wireless sensor node
 \item \textbf{Power Generator:} Several WSN applications need to operate for a longer time, so it is essential to equip the wireless sensor node with additional power source in order to prolong the network lifetime. The better energy source to generate the power for outdoor applications is a solar cells. An another power harvesting mechanisims~\cite{ref20,ref21}  for thermal, motion, vibration, micro water flow, Biological, pressure gradients, and electromagnetic radiation energy harvesting can be used that yield increasing power output to extend the network lifetime. 
 \end{enumerate}
 
 \item \textbf{Power Generator:} Several WSN applications need to operate for a longer time, so it is essential to equip the wireless sensor node with additional power source in order to prolong the network lifetime. The better energy source to generate the power for outdoor applications is a solar cells. An another power harvesting mechanisims~\cite{ref20,ref21}  for thermal, motion, vibration, micro water flow, Biological, pressure gradients, and electromagnetic radiation energy harvesting can be used that yield increasing power output to extend the network lifetime. 
 \end{enumerate}
 

-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.
-
-A typical WSN architecture consists of a set of a typical wireless sensor nodes, which are capable of sensing the phenomenon of interest around it such as fire in the forest (see~figure~\ref{wsn}) and then send the sensed data to a controller node called a sink. One or more sink in WSN are responsible for collecting and processing the sensed data by the wireless sensors, and then send it through the Internet to the end user. 

 \begin{figure}[h!]
 \centering
 \includegraphics[scale=0.9]{Figures/ch1/wsn.jpg} 
 \begin{figure}[h!]
 \centering
 \includegraphics[scale=0.9]{Figures/ch1/wsn.jpg} 


@@ -65,10 +64,12 @@ A typical WSN architecture consists of a set of a typical wireless sensor nodes,
 \label{wsn}
 \end{figure}
 
 \label{wsn}
 \end{figure}
 

+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.
+

 
 \section{Types of Wireless Sensor Networks} 
 \label{ch1:sec:03}
 
 \section{Types of Wireless Sensor Networks} 
 \label{ch1:sec:03}

-According to the physical phenomena for which the WSN is developed, several WSNs are deployed on the ground, underground and underwater, which suffer from different conditions and challenges. WSNs can be classified into six types, where five types of them presented in~\cite{ref4,ref5} and we added the sixth type. Figure~\ref{wsnt} gives an examples for WSNs types.
+According to the physical phenomena for which the WSN is developed, several WSNs are deployed on the ground, underground and underwater, which suffer from different conditions and challenges. WSNs can be classified into six types, where five types of them presented in~\cite{ref4,ref5} and we added the sixth type. This dissertation is used the terrestrial WSN. Figure~\ref{wsnt} gives an examples for WSNs types.

 \begin{figure}[h!]
 \centering
 \includegraphics[scale=0.5]{Figures/ch1/typesWSN.pdf} 
 \begin{figure}[h!]
 \centering
 \includegraphics[scale=0.5]{Figures/ch1/typesWSN.pdf} 


@@ -102,7 +103,7 @@ A network consists of a low cost wireless sensor nodes, which are equipped with
 
 \section{Wireless Sensor Network Applications}
 \label{ch1:sec:04}
 
 \section{Wireless Sensor Network Applications}
 \label{ch1:sec:04}

-The high development in WSNs led to extensive study on different characteristics of it. However, the WSN has been applied with concentrating on various applications. In this section, we demonstrated a different academic and commercial applications that developed for WSNs. The WSN composed of various types of sensors such as~\cite{ref17,ref19}: thermal, seismic, magnetic, visual, infrared, acoustic, and radar, which are capable of observing a different physical conditions such as: temperature, humidity, pressure, speed, direction, movement, light, soil makeup, noise levels, the presence or absence of certain kinds of objects, and mechanical stress levels on attached objects. So, There are a wide range of WSN applications and these applications can be classified into five classes~\cite{ref22}. Figure~\ref{WSNAP} shows classification of WSN applications. 
+The fast development in WSNs has been led to extensive study on different characteristics of it. However, the WSN has been applied with concentrating on various applications. In this section, we demonstrated a different academic and commercial applications that developed for WSNs. The WSN composed of various types of sensors such as~\cite{ref17,ref19}: thermal, seismic, magnetic, visual, infrared, acoustic, and radar, which are capable of observing a different physical conditions such as: temperature, humidity, pressure, speed, direction, movement, light, soil makeup, noise levels, the presence or absence of certain kinds of objects, and mechanical stress levels on attached objects. So, There are a wide range of WSN applications and these applications can be classified into five classes~\cite{ref22}. Figure~\ref{WSNAP} shows classification of WSN applications. 

 
 \begin{figure}[h!]
 \centering
 
 \begin{figure}[h!]
 \centering


@@ -126,7 +127,7 @@ In cattle monitoring applications, the WSN used to livestock control and monitor
 Various WSN applications for environmental monitoring have been used in coastline erosion,  air quality monitoring, safe drinking water and contamination control~\cite{ref22}.
 
 \item \textbf{Public safety and military systems Applications}
 Various WSN applications for environmental monitoring have been used in coastline erosion,  air quality monitoring, safe drinking water and contamination control~\cite{ref22}.
 
 \item \textbf{Public safety and military systems Applications}

-The WSNs can be incorporated into  military command, control, communications, computing, intelligence,
+The WSNs can be incorporated into military command, control, communications, computing, intelligence,

 surveillance, reconnaissance, and targeting systems. It estimates the unpredictable events such as  natural disasters and threats as well as some of the military WSN applications keep under surveillance friendly forces, equipment, and ammunition; battlefield surveillance; reconnaissance of opposing forces and terrain; targeting; battle damage assessment; and nuclear, biological, and chemical (NBC) attack detection and reconnaissance~\cite{ref19}. According to figure~\ref{WSNAP}, the public safety and military applications are categorized into active intervention and passive supervision~\cite{ref22}. In active intervention systems, the wireless sensors are portable with the agents and is devoted to the security of the team activities. During the work of the team, the leader will monitor the agents situation and the environmental impact factors. The main applications includes: emergency rescue teams, miners and soldiers. In  passive supervision systems, the wireless static sensors are scattered over a large field for monitoring a civil area or nuclear site for a longer time. These applications includes: 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}
 surveillance, reconnaissance, and targeting systems. It estimates the unpredictable events such as  natural disasters and threats as well as some of the military WSN applications keep under surveillance friendly forces, equipment, and ammunition; battlefield surveillance; reconnaissance of opposing forces and terrain; targeting; battle damage assessment; and nuclear, biological, and chemical (NBC) attack detection and reconnaissance~\cite{ref19}. According to figure~\ref{WSNAP}, the public safety and military applications are categorized into active intervention and passive supervision~\cite{ref22}. In active intervention systems, the wireless sensors are portable with the agents and is devoted to the security of the team activities. During the work of the team, the leader will monitor the agents situation and the environmental impact factors. The main applications includes: emergency rescue teams, miners and soldiers. In  passive supervision systems, the wireless static sensors are scattered over a large field for monitoring a civil area or nuclear site for a longer time. These applications includes: 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}


@@ -143,7 +144,7 @@ The most significant goal for many companies is the automation of controlling an
 
 \section{The Main Challenges in Wireless Sensor Networks}
 \label{ch1:sec:05}
 
 \section{The Main Challenges in Wireless Sensor Networks}
 \label{ch1:sec:05}

-There are many challenges need to be faced in WSNs, which are received increasing attention by a large number of researchers during the last few years. These challenges were the reason in proposing different solutions so as to face these challenges (see section~\ref{ch1:sec:06}).
+There are many challenges need to be faced in WSNs, which are received increasing attention by a large number of researchers during the last few years. These challenges were the reason in proposing different solutions so as to face these challenges as will be explained in next section~\ref{ch1:sec:06}.

 \begin{enumerate} [(I)]
 \item \textbf{Extended Network Lifetime:} one fundamental issue in WSNs is how to prolong the network lifetime as long as possible. Since sensor battery has a limited power; and since it is difficult to recharge or replace it especially in remote or hostile environment; It is necessary to reduce the energy consumption by using energy-efficient methods so as to extend the network lifetime.
 
 \begin{enumerate} [(I)]
 \item \textbf{Extended Network Lifetime:} one fundamental issue in WSNs is how to prolong the network lifetime as long as possible. Since sensor battery has a limited power; and since it is difficult to recharge or replace it especially in remote or hostile environment; It is necessary to reduce the energy consumption by using energy-efficient methods so as to extend the network lifetime.
 


@@ -177,7 +178,7 @@ Since the nature of many WSN applications that need to be deployed in a remote o
 The energy limited nature of wireless sensor nodes need to use energy efficient mechanisms to prolong network lifetime. The energy efficient mechanisms can be classified into five categories~\cite{ref22}. Figure~\ref{emwsn} summarizes the energy-efficient mechanisms in WSNs.
 \begin{figure}[h!]
 \centering
 The energy limited nature of wireless sensor nodes need to use energy efficient mechanisms to prolong network lifetime. The energy efficient mechanisms can be classified into five categories~\cite{ref22}. Figure~\ref{emwsn} summarizes the energy-efficient mechanisms in WSNs.
 \begin{figure}[h!]
 \centering

-\includegraphics[scale=0.5]{Figures/ch1/WSN-M.pdf} 
+\includegraphics[scale=0.4]{Figures/ch1/WSN-M.eps} 

 \caption{Energy-Efficient Mechanisms in Wireless Sensor Networks}
 \label{emwsn}
 \end{figure}
 \caption{Energy-Efficient Mechanisms in Wireless Sensor Networks}
 \label{emwsn}
 \end{figure}


@@ -187,7 +188,16 @@ The energy-efficient routing is a significant factor to the design of WSN protoc
 
 \begin{enumerate} [(I)]
 
 
 \begin{enumerate} [(I)]
 

-\item \textbf{Cluster architectures:} in this strategy, the wireless sensor nodes are grouped into several groups that called clusters, each group of wireless sensor nodes are managed by a single sensor node, which is called cluster head. The cluster head takes the responsibility of manging the activities of the wireless sensor nodes with the cluster and it communicates and coordinates with other cluster heads or the base station in the WSN. This mechanism conserves the energy in WSNs by means of~\cite{ref43,ref22}:
+\item \textbf{Energy as a routing metric:} lifetime maximization can be achieved by using the residual power of wireless sensor node as a routing metric and take it into account during executing the routing protocol in WSNs. So, the routing protocols should concentrate on the remaining power of sensor nodes during taking the decision to select the next hop toward the destination and not depend on the shortest path solution. It prioritizes routes on the basis of an energy metric (sometimes with other routing metrics) so it is called energy-aware routing protocols~\cite{ref45,ref46}.
+
+\item \textbf{Multipath routing:} efficient strategy that can provides reliability, security and load balancing  in order to forward packets in a limited energy and constrained resources(computation, communication, and storage) networks like WSNs~\cite{ref50}. The single path routing is simple and scalable but it is not efficient for energy constrained networks such as WSNs . There are many multipath routing protocol are summarized in~\cite{ref50,ref51}.
+
+\end{enumerate} 
+
+
+\subsection{Cluster architectures}
+In this strategy, the wireless sensor nodes are grouped into several groups that called clusters, each group of wireless sensor nodes are managed by a single sensor node, which is called cluster head. The cluster head takes the responsibility of manging the activities of the wireless sensor nodes with the cluster and it communicates and coordinates with other cluster heads or the base station in the WSN. This mechanism conserves the energy in WSNs by means of~\cite{ref43,ref22}:
+

 \begin{enumerate}[(a)]
 \item Grouping the wireless sensor nodes into clusters led to decrease the communication range within the cluster and therefore minimize the energy needed to communication among the nodes inside the cluster.
 \item Minimizing the energy hungry operations such as collaboration and aggregation to the cluster head.
 \begin{enumerate}[(a)]
 \item Grouping the wireless sensor nodes into clusters led to decrease the communication range within the cluster and therefore minimize the energy needed to communication among the nodes inside the cluster.
 \item Minimizing the energy hungry operations such as collaboration and aggregation to the cluster head.


@@ -197,23 +207,10 @@ The energy-efficient routing is a significant factor to the design of WSN protoc
 \end{enumerate}
 In addition, the clustering supports network scalability in WSNs~\cite{ref43,ref44}.
 
 \end{enumerate}
 In addition, the clustering supports network scalability in WSNs~\cite{ref43,ref44}.
 

-\item \textbf{Energy as a routing metric:} lifetime maximization can be achieved by using the residual power of wireless sensor node as a routing metric and take it into account during executing the routing protocol in WSNs. So, the routing protocols should concentrate on the remaining power of sensor nodes during taking the decision to select the next hop toward the destination and not depend on the shortest path solution. It prioritizes routes on the basis of an energy metric (sometimes with other routing metrics) so it is called energy-aware routing protocols~\cite{ref45,ref46}.
-
-\item \textbf{Multipath routing:} efficient strategy that can provides reliability, security and load balancing  in order to forward packets in a limited energy and constrained resources(computation, communication, and storage) networks like WSNs~\cite{ref50}. The single path routing is simple and scalable but it is not efficient for energy constrained networks such as WSNs . There are many multipath routing protocol are summarized in~\cite{ref50,ref51}.
-
-\item \textbf{Relay node placement:} in WSN, some wireless sensor nodes in a certain region may be died and this will leads to create a hole in the WSN. This problem can be solved by placing the wireless sensor nodes in sensing field using optimal distribution or by deploying a small number of relay wireless sensor nodes with a powerful capabilities whose major goal is the communication with other wireless sensor nodes or relay nodes~\cite{ref52}. his solution can enhance the power balancing and avoiding the overloaded wireless sensor nodes in a particular region in WSN.

 
 

-\item \textbf{Sink Mobility:} in WSNs that included a static sink, the wireless sensor nodes, which are near the sink drain their power more rapidly compared with other sensor nodes that leads to WSN disconnection and limited network lifetime~\cite{ref53}. This is happening due to sending all the data in WSN to the sink that maximizes the overload on the wireless sensor nodes close to sink. In order to overcome this problem and prolong the network lifetime; it is necessary to use a mobile sink to move within the area of WSN so as to collect the sensory data from the static sensor nodes over a single hop communication.  The mobile sink avoids the multi-hop communication and conserves the energy at the static sensor nodes close the base station, extending the lifetime of WSN~\cite{ref54,ref55}.
-
-\end{enumerate} 

 
 
 
 
 
 

-\subsection{Radio Optimization}
-In wireless sensor node, the radio is the most energy-consuming unit for draining the battery power. Extensive researches have been focused on decreasing the power depletion due to wireless communication by means of optimizing the radio parameters such as: coding and modulation schemes; transmission Power and antenna
-direction; and cognitive radio and Cooperative communications schemes~\cite{ref22}.
-
-

 \subsection{Scheduling Schemes}
 There are many scheduling schemes have been suggested so as to decrease the energy depletion and improve the lifetime of WSNs~\cite{ref58,ref59}. These schemes have dealt with scheduling the states of wireless sensor nodes and putting the idle sensor nodes into sleep mode (i.e, turn off the radio unit) to save the energy. Figure~\ref{wsns} summarizes the Scheduling Schemes in WSNs. In this figure, the scheduling schemes are classified into two main branches~\cite{ref56,ref57}: (i) wake up scheduling aims to manage the wireless sensor node states (sleep/wake up) in WSN by selecting a set of time intervals for a sensor nodes to be awake from continuous time duration. and (ii) topology control in which a set of a wireless sensor nodes are chose to be awake from a given sensor nodes in WSN. 
 \begin{figure}[h!]
 \subsection{Scheduling Schemes}
 There are many scheduling schemes have been suggested so as to decrease the energy depletion and improve the lifetime of WSNs~\cite{ref58,ref59}. These schemes have dealt with scheduling the states of wireless sensor nodes and putting the idle sensor nodes into sleep mode (i.e, turn off the radio unit) to save the energy. Figure~\ref{wsns} summarizes the Scheduling Schemes in WSNs. In this figure, the scheduling schemes are classified into two main branches~\cite{ref56,ref57}: (i) wake up scheduling aims to manage the wireless sensor node states (sleep/wake up) in WSN by selecting a set of time intervals for a sensor nodes to be awake from continuous time duration. and (ii) topology control in which a set of a wireless sensor nodes are chose to be awake from a given sensor nodes in WSN. 
 \begin{figure}[h!]


@@ -293,6 +290,22 @@ Data driven schemes classified into two main approaches~\cite{ref59,ref22}:
 In the last years, extensive researches have been focused on energy harvesting and wireless charging techniques. These solutions are representing alternate energy sources to recharge wireless sensor batteries without human intervention and instead of depending on the limited power supplied by a typical batteries~\cite{ref91,ref59}. In energy harvesting, several sources of environmental energy have been developed so as to enable the wireless sensors to acquire energy from the surrounding environment like solar, wind energy, vibration based energy harvesting, radio signals for scavenging RF power, Thermoelectric generators, and shoe-mounted piezoelectric generator to power artificial organs~\cite{ref59}. In wireless charging,  the wireless power can be transmitted between the devices without requiring to the connection between the transmitter and the receiver. These techniques are participating in increasing the the availability of WSNs and prolonging the network lifetime. Wireless charging in WSNs can be performed by using two manners: magnetic resonant coupling and electromagnetic radiation~\cite{ref22}.
 
 
 In the last years, extensive researches have been focused on energy harvesting and wireless charging techniques. These solutions are representing alternate energy sources to recharge wireless sensor batteries without human intervention and instead of depending on the limited power supplied by a typical batteries~\cite{ref91,ref59}. In energy harvesting, several sources of environmental energy have been developed so as to enable the wireless sensors to acquire energy from the surrounding environment like solar, wind energy, vibration based energy harvesting, radio signals for scavenging RF power, Thermoelectric generators, and shoe-mounted piezoelectric generator to power artificial organs~\cite{ref59}. In wireless charging,  the wireless power can be transmitted between the devices without requiring to the connection between the transmitter and the receiver. These techniques are participating in increasing the the availability of WSNs and prolonging the network lifetime. Wireless charging in WSNs can be performed by using two manners: magnetic resonant coupling and electromagnetic radiation~\cite{ref22}.
 
 

+\subsection{Radio Optimization}
+In wireless sensor node, the radio is the most energy-consuming unit for draining the battery power. Extensive researches have been focused on decreasing the power depletion due to wireless communication by means of optimizing the radio parameters such as: coding and modulation schemes; transmission Power and antenna
+direction; and cognitive radio and Cooperative communications schemes~\cite{ref22}.
+
+\subsection{Relay nodes and Sink Mobility}
+
+\begin{enumerate} [(I)]
+\item \textbf{Relay node placement:} in WSN, some wireless sensor nodes in a certain region may be died and this will leads to create a hole in the WSN. This problem can be solved by placing the wireless sensor nodes in sensing field using optimal distribution or by deploying a small number of relay wireless sensor nodes with a powerful capabilities whose major goal is the communication with other wireless sensor nodes or relay nodes~\cite{ref52}. This solution can enhance the power balancing and avoiding the overloaded wireless sensor nodes in a particular region in WSN.
+
+\item \textbf{Sink Mobility:} in WSNs that included a static sink, the wireless sensor nodes, which are near the sink drain their power more rapidly compared with other sensor nodes that leads to WSN disconnection and limited network lifetime~\cite{ref53}. This is happening due to sending all the data in WSN to the sink that maximizes the overload on the wireless sensor nodes close to sink. In order to overcome this problem and prolong the network lifetime; it is necessary to use a mobile sink to move within the area of WSN so as to collect the sensory data from the static sensor nodes over a single hop communication.  The mobile sink avoids the multi-hop communication and conserves the energy at the static sensor nodes close the base station, extending the lifetime of WSN~\cite{ref54,ref55}.
+
+\end{enumerate}
+
+
+
+

 \section{Network Lifetime in Wireless Sensor Networks}  
 \label{ch1:sec:07}
 The limited resources in WSNs have been addressed, and one of the main challenges in WSNs is the limited power resource. For this reason, there are extensive researches have been proposed in order to prolong the network lifetime by means of designing and implementing energy-efficient protocols. The reason for these large number of proposed protocols to maximize the network lifetime is the difficulty and sometime impossibility to replace or recharge the batteries of wireless sensor nodes especially in the large WSN and hostile environment. The authors have been defined the network lifetime in different contexts and use it as a metric to evaluate the performance of their protocols. Based on the previous proposed works in prolonging the network lifetime;Various definitions exist for the lifetime of a sensor network~\cite{ref92,ref93} such as:~\textbf{(i)} is the time spent by WSN until the death of the first wireless sensor node ( or cluster head ) in the network due to its energy depletion.~\textbf{(ii)} is the time spent by WSN and has at least a specific set $\beta$ of alive sensor nodes in WSN.~\textbf{(iii)} is the time spent by WSN until the death of all wireless sensor nodes in WSN because they have been depleted of their energy.~\textbf{(iv)} for  k-coverage is the time spent by WSN in covering the area of interest by at least $k$ sensor nodes.~\textbf{(v)} for 100 $\%$ coverage is the time spent by WSN in covering each target or the whole area by at least one sensor node.~\textbf{(vi)} for $\alpha$-coverage: the total time by which at least $\alpha$ part of the sensing field is covered by at least one node; or is the time spent by WSN until the coverage ratio becomes less than a predetermined threshold $\alpha$.
 \section{Network Lifetime in Wireless Sensor Networks}  
 \label{ch1:sec:07}
 The limited resources in WSNs have been addressed, and one of the main challenges in WSNs is the limited power resource. For this reason, there are extensive researches have been proposed in order to prolong the network lifetime by means of designing and implementing energy-efficient protocols. The reason for these large number of proposed protocols to maximize the network lifetime is the difficulty and sometime impossibility to replace or recharge the batteries of wireless sensor nodes especially in the large WSN and hostile environment. The authors have been defined the network lifetime in different contexts and use it as a metric to evaluate the performance of their protocols. Based on the previous proposed works in prolonging the network lifetime;Various definitions exist for the lifetime of a sensor network~\cite{ref92,ref93} such as:~\textbf{(i)} is the time spent by WSN until the death of the first wireless sensor node ( or cluster head ) in the network due to its energy depletion.~\textbf{(ii)} is the time spent by WSN and has at least a specific set $\beta$ of alive sensor nodes in WSN.~\textbf{(iii)} is the time spent by WSN until the death of all wireless sensor nodes in WSN because they have been depleted of their energy.~\textbf{(iv)} for  k-coverage is the time spent by WSN in covering the area of interest by at least $k$ sensor nodes.~\textbf{(v)} for 100 $\%$ coverage is the time spent by WSN in covering each target or the whole area by at least one sensor node.~\textbf{(vi)} for $\alpha$-coverage: the total time by which at least $\alpha$ part of the sensing field is covered by at least one node; or is the time spent by WSN until the coverage ratio becomes less than a predetermined threshold $\alpha$.


@@ -407,6 +420,7 @@ In this section, two energy consumption models are explained. The first model ca
 
 
 \subsection{Radio Energy Dissipation Model}
 
 
 \subsection{Radio Energy Dissipation Model}

+\label{ch1:sec9:subsec1}

 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.
 \begin{figure}[h!]
 \centering
 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.
 \begin{figure}[h!]
 \centering


@@ -441,6 +455,7 @@ The radio energy dissipation model have been considered only the energy consumed
 
 
 \subsection{Our Energy Consumption Model}
 
 
 \subsection{Our Energy Consumption Model}

+\label{ch1:sec9:subsec2}

 In this dissertation, the coverage protocols have been used an energy consumption model proposed by~\cite{ref111} and based on \cite{ref112} with slight  modifications.  The energy consumption for  sending/receiving the packets is added, whereas the  part related to the sensing range is removed because we consider a fixed sensing range.
 For our energy consumption model, we refer to the sensor node Medusa~II which uses an Atmels  AVR ATmega103L microcontroller~\cite{ref112}. The typical architecture  of a  sensor  is composed  of  four  subsystems: the  MCU subsystem which is capable of computation, communication subsystem (radio) which is responsible  for transmitting/receiving messages, the  sensing subsystem that collects  data, and  the  power supply  which  powers the  complete sensor  node
 \cite{ref112}. Each  of the first three subsystems  can be turned on or  off depending on  the current status  of the sensor.   Energy consumption
 In this dissertation, the coverage protocols have been used an energy consumption model proposed by~\cite{ref111} and based on \cite{ref112} with slight  modifications.  The energy consumption for  sending/receiving the packets is added, whereas the  part related to the sensing range is removed because we consider a fixed sensing range.
 For our energy consumption model, we refer to the sensor node Medusa~II which uses an Atmels  AVR ATmega103L microcontroller~\cite{ref112}. The typical architecture  of a  sensor  is composed  of  four  subsystems: the  MCU subsystem which is capable of computation, communication subsystem (radio) which is responsible  for transmitting/receiving messages, the  sensing subsystem that collects  data, and  the  power supply  which  powers the  complete sensor  node
 \cite{ref112}. Each  of the first three subsystems  can be turned on or  off depending on  the current status  of the sensor.   Energy consumption


@@ -476,7 +491,7 @@ COMPUTATION & on & on & on & 26.83 \\
 \end{table}
 
 For the sake of simplicity we ignore  the energy needed to turn on the radio, to start up the sensor node, to move from one status to another, etc.
 \end{table}
 
 For the sake of simplicity we ignore  the energy needed to turn on the radio, to start up the sensor node, to move from one status to another, etc.

-Thus, when a sensor becomes active (i.e., it has already chosen its status), it can turn  its radio  off to  save battery. The value of energy spent to send a 1-bit-content message is  obtained by using  the equation in  ~\cite{ref112} to calculate  the energy cost  for transmitting  messages and  we propose  the same
+Thus, when a sensor becomes active (i.e., it has already chosen its status), it can turn  its radio  off to  save battery. The value of energy spent to send a 1-bit-content message is  obtained by using  the equation in  ~\cite{ref112} to calculate  the energy cost for transmitting  messages and  we propose  the same

 value for receiving the packets. The energy  needed to send or receive a 1-bit packet is equal to $0.2575~mW$.
 
 
 value for receiving the packets. The energy  needed to send or receive a 1-bit packet is equal to $0.2575~mW$.