Update bt Ali

[ThesisAli.git] / CHAPITRE_01.tex

diff --git a/CHAPITRE_01.tex b/CHAPITRE_01.tex
index 64f492942926cfdb735e2d60ec3161e60d51d5b5..338d61c59736ee183ee7ede08d3dd43fec15818e 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}


@@ -146,31 +146,32 @@ 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}

-\indent 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 represent the main reason to propose a different solutions so as to face them as will be explained in next section~\ref{ch1:sec:06}.
+\indent 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 represent the main reason to propose a different solutions so as to face them 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 in order to extend the network lifetime.
 
 \item \textbf{Coverage:} One of the fundamental challenges in WSNs is the coverage preservation and the extension of the network lifetime continuously and effectively when monitoring a certain area (or region) of interest. The major objective is to choose the minimum number of sensor nodes in order to monitor the target sensing field without affecting on the application requirements in executing its tasks as long time as possible. 
 
 \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 in order to extend the network lifetime.
 
 \item \textbf{Coverage:} One of the fundamental challenges in WSNs is the coverage preservation and the extension of the network lifetime continuously and effectively when monitoring a certain area (or region) of interest. The major objective is to choose the minimum number of sensor nodes in order to monitor the target sensing field without affecting on the application requirements in executing its tasks as long time as possible. 
 

-\item \textbf{Routing:} Represents one of the important problems in WSNs that needs to be solved efficiently. The limited resources of WSNs and the impacts of wireless communication are led to a big challenge in ensuring energy-efficient routing. However, it is not enough to use the shortest path to route the packets among the sensor nodes toward the sink. It is necessary to design an energy-efficient routing protocol that considers the remaining energy of sensor node during taking the decision to route the packet to the next hop toward the destination. This can participates in energy conservation and balancing among the sensor node in WSNs. 
+\item \textbf{Routing:} Represents one of the important problems in WSNs that needs to be solved efficiently. The limited resources of WSNs and the impacts of wireless communication lead to a big challenge in ensuring energy-efficient routing. However, it is not enough to use the shortest path to route the packets among the sensor nodes toward the sink. It is necessary to design an energy-efficient routing protocol that considers the remaining energy of sensor node during taking the decision to route the packet to the next hop toward the destination. This participates in energy conservation and balancing among the sensor node in WSNs. 

 
 \item \textbf{Autonomous and Distributed Management:}
 
 \item \textbf{Autonomous and Distributed Management:}

-Since the nature of many WSN applications that need to be deployed in a remote or hostile environment, it is important that the wireless sensor nodes work in autonomous and distributed way to communicate and cooperate with other sensor nodes without human intervention because the maintenance or the repair maybe difficult. The distributed management consumes less energy because it is based on only local information from the neighboring sensor nodes; on the other hand, it does not give the optimal solution, so the main challenge is how to apply the a distributed management in WSNs and in the same time ensuring an optimal or near optimal solution.
+Since the nature of many WSN applications need to be deployed in a remote or hostile environment, it is important that the wireless sensor nodes work in autonomous and distributed way to communicate and cooperate with other sensor nodes without human intervention because the maintenance or the repair may be difficult. The distributed management consumes less energy because it is based on only local information from the neighboring sensor nodes, moreover, it does not give the optimal solution. So the main challenge is how to apply the a distributed management in WSNs and in the same time ensuring an optimal or near optimal solution.

    
    

-\item \textbf{Scalability:} Many physical phenomenons require to be deployed densely with a large number of sensor nodes for different reasons such as: the large sensed area,  reliability requirement, or network lifetime prolongation. It is necessary that the proposed protocols in WSNs be scalable for these large number of sensor
+\item \textbf{Scalability:} Many physical phenomenons require to be deployed densely with a large number of sensor nodes for different reasons such as: the large sensed area,  reliability requirement, or network lifetime prolongation. It is necessary that the proposed protocols in WSNs are scalable for these large number of sensor

 nodes in order to achieve their tasks efficiently.
 
   
 
 nodes in order to achieve their tasks efficiently.
 
   
 

-\item \textbf{Reliability:} Many applications require a high quality of coverage. These applications need to deploy a large number of a cheap sensor nodes so as to satisfy the requirement of application. These large number of the sensor nodes may be prone to the failure and this will affect on the quality of service provided by the application. However, it is important to build mechanisms inside the protocols so as to avoid the failure of some sensor nodes during the network operation and to increase the robustness of the proposed protocol in WSNs.
+\item \textbf{Reliability:} Many applications require a high quality of coverage. These applications need to deploy a large number of a cheap sensor nodes so as to satisfy their requirements. This large number of the sensor nodes may be prone to the failure and this will affect on the quality of service provided by the application. However, it is important to build mechanisms inside the protocols so as to avoid the failure of some sensor nodes during the network operation and to increase the robustness of the proposed protocol in WSNs.

 
 

-\item \textbf{Topology Control:} The maintenance and repair of the network topology is a challenging task because the large number of inaccessible sensor nodes that are prone to failure. Therefore, some schemes need to 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 because there is a 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 be reflect different tasks and can be integrated into one big network. Therefore, it is necessary to take the heterogeneity into consideration during constructing the protocols in WSNs. 
+\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 be reflect different tasks and can be integrated into one big network. Therefore, it is necessary to take the heterogeneity into consideration for constructing the protocols in WSNs. 

 
 

-\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 poses another challenge is how to find the more energy efficient route to transmit the information from the source to the destination, where the sensor nodes should be cooperated 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: how to find the more energy efficient route to transmit the information from the source to the destination, where the sensor nodes should be cooperated to find this route; and to serve as relays.

 
 

-\item \textbf{Data Management:} Represents one of the challenges that contributes in depleting the energy of the sensor nodes in WSNs. The main task of the WSN after deploying the sensor nodes in target environment that need to be monitored is to collect the sensed data from this physical environment and then transmit it to the base station. Since there are many sensor nodes in WSN; and since every sensor node want to transmit its sensed data to the base station; It will be a large amount of data that need to be managed, processed and routed, to the sink that represents a real challenge in WSNs.
+\item \textbf{Data Management:} Represents one of the challenges that contributes in depleting the energy of the sensor nodes in WSNs. The main task of the WSN after deploying the sensor nodes in target environment that need to be monitored is to collect the sensed data from this physical environment and then transmit it to the base station. Since there are many sensor nodes in WSN; and since every sensor node want to transmit its sensed data to the base station; there is a large amount of data that need to be managed, processed and routed, to the sink and it represents a real challenge in WSNs.

 
 \item \textbf{Security:} The sensitivity of the information collected by WSNs represents the final challenge that should be faced in WSNs. This information is susceptible to malicious intrusions and hacker attacks; however, it is necessary to provide energy efficient schemes by WSNs to protect this information during the operation of WSNs.
 
 
 \item \textbf{Security:} The sensitivity of the information collected by WSNs represents the final challenge that should be faced in WSNs. This information is susceptible to malicious intrusions and hacker attacks; however, it is necessary to provide energy efficient schemes by WSNs to protect this information during the operation of WSNs.
 


@@ -194,17 +195,17 @@ nodes in order to achieve their tasks efficiently.
 
 \subsubsection{Routing Metric based on Residual Energy:} Lifetime maximization can be achieved by using the residual power of wireless sensor node as a routing metric that should be taken into account during executing the routing protocol in WSNs. The routing protocols should be concentrated on the remaining power of sensor nodes during taking the decision to select the next hop toward the destination. They should not only depend on the shortest path solution.  They prioritize routes on the basis of an energy metric (sometimes with other routing metrics), therefore,  it is called energy-aware routing protocols.~\cite{ref45,ref46}.
 
 
 \subsubsection{Routing Metric based on Residual Energy:} Lifetime maximization can be achieved by using the residual power of wireless sensor node as a routing metric that should be taken into account during executing the routing protocol in WSNs. The routing protocols should be concentrated on the remaining power of sensor nodes during taking the decision to select the next hop toward the destination. They should not only depend on the shortest path solution.  They prioritize routes on the basis of an energy metric (sometimes with other routing metrics), therefore,  it is called energy-aware routing protocols.~\cite{ref45,ref46}.
 

-\subsubsection{Multipath Routing:} Efficient strategy that 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. Many multipath routing protocol are summarized in~\cite{ref50,ref51}.
+\subsubsection{Multipath Routing:} Efficient strategy that 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. Many multipath routing protocol summarized in~\cite{ref50,ref51}.

 
 
 \subsection{Cluster Architectures:}
 
 
 
 \subsection{Cluster Architectures:}
 

-\indent 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}:
+\indent 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 managing the activities of the wireless sensor nodes with 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 the wireless sensor nodes into clusters is 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 is 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.

-\item Limiting the number of communications (transmitting and receiving) due to the fusion operation carried out by 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 residual energy is led to balance the energy consumption among wireless sensor nodes inside the cluster.
 \item Some nodes can be turned-off within the same cluster whilst the cluster head manage the responsibilities.
 \end{enumerate}
 \item The continuous changing of cluster head according to residual energy is led to balance the energy consumption among wireless sensor nodes inside the cluster.
 \item Some nodes can be turned-off within the same cluster whilst the cluster head manage the responsibilities.
 \end{enumerate}


@@ -215,7 +216,7 @@ nodes in order to achieve their tasks efficiently.
 
 \subsection{Scheduling Schemes:}
 
 
 \subsection{Scheduling Schemes:}
 

-\indent 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 chosen to be awake from a given sensor nodes in WSN. 
+\indent Many scheduling schemes have been suggested so as to decrease the energy depletion and improve the lifetime of WSNs~\cite{ref58,ref59}. These schemes deal 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 chosen to be awake from a given sensor nodes in WSN. 

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


@@ -226,7 +227,7 @@ nodes in order to achieve their tasks efficiently.
 
 \subsubsection{Wake up Scheduling Schemes:}
 
 
 \subsubsection{Wake up Scheduling Schemes:}
 

-\indent This section demonstrates the scheduling schemes from 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 so as to sends or receives the packets. Whilst, the sleep interval refers to a period of time at which the radio unit is turned off so as to retain the energy of wireless sensor node. Some schemes divide the time into equal length durations of time that called slotted schemes; on the other hand, the other schemes works with the time in continuous way that 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 multiples of slots. The wake up schedule represents a set of a wake up and sleep intervals, which are produced for one period. This schedule replicates to each period and it can be changed by the wake up scheduling scheme during the different periods of time. The final goal of this wake up schedule is to permit to exchange the data among the wireless sensor nodes in WSN during the wake up interval. As shown in figure~\ref{wsns}, the requirement for synchronization has been categorized the wake up scheduling into three categories~\cite{ref57}:
+\indent This section demonstrates the scheduling schemes from 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 so as to send or receive the packets. Whilst, the sleep interval refers to a period of time at which the radio unit is turned off so as to retain the energy of wireless sensor node. Some schemes divide the time into equal length durations of time and are called slotted schemes; on the other hand, the other schemes work with the time in 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. The wake up schedule represents a set of a wake up and sleep intervals, which are produced for one period. This schedule replicates to each period and it can be changed by the wake up scheduling scheme during the different periods of time. The final goal of this wake up schedule is to permit to exchange the data among the wireless sensor nodes in WSN during the wake up interval. As shown in figure~\ref{wsns}, the requirement for synchronization has been categorized the wake up scheduling into three categories~\cite{ref57}:

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


@@ -295,7 +296,7 @@ Data driven schemes are classified into two main approaches~\cite{ref59,ref22}:
 
 \subsection{Battery Repletion:}
 
 
 \subsection{Battery Repletion:}
 

-\indent 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}.
+\indent In the last years, extensive researches have been focused on energy harvesting and wireless charging techniques. These solutions represent alternate energy sources to recharge wireless sensor batteries without human intervention~\cite{ref91,ref59}.

 
 \subsubsection{Energy Harvesting:} 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}. 
 
 
 \subsubsection{Energy Harvesting:} 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}. 
 


@@ -312,10 +313,10 @@ direction; and cognitive radio and Cooperative communications schemes~\cite{ref2
 \indent The relay nodes placement and the mobility of the sink can be considered as energy-efficient strategies, which are used to minimize the consumption of the energy and extend the lifetime of WSNs.
 %\begin{enumerate} [(I)]
 \subsubsection{Relay node placement:} 
 \indent The relay nodes placement and the mobility of the sink can be considered as energy-efficient strategies, which are used to minimize the consumption of the energy and extend the lifetime of WSNs.
 %\begin{enumerate} [(I)]
 \subsubsection{Relay node placement:} 

-In WSN, some wireless sensor nodes in a certain region may be died and this will lead 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.
+In WSN, some wireless sensor nodes in a certain region may be died and this creates 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.

 
 \subsubsection{Sink Mobility:} 
 
 \subsubsection{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 lead 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}.
+In WSNs includes a static sink, the wireless sensor nodes, which are near the sink drain their power more rapidly compared with other sensor nodes that lead to WSN disconnection and limited network lifetime~\cite{ref53}. Sending all the data in WSN to the sink 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 to the base station, extending the lifetime of WSN~\cite{ref54,ref55}.

 
 %\end{enumerate}
 
 
 %\end{enumerate}
 


@@ -324,27 +325,39 @@ In WSNs that included a static sink, the wireless sensor nodes, which are near t
 
 \section{Network Lifetime in Wireless Sensor Networks}  
 \label{ch1:sec:07}
 
 \section{Network Lifetime in Wireless Sensor Networks}  
 \label{ch1:sec:07}

-\indent 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. 
-
-\indent 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 are 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 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 $\alpha$.~\textbf{(vii)} the working time spent by the system before either the coverage ratio or delivery ratio become less than a predetermined threshold.~\textbf{(viii)} the number of the successful data gathering trips.~\textbf{(ix)} the number of sent packets.~\textbf{(x)} the percentage of wireless sensor nodes that have a route to the sink.~\textbf{(xi)} the prediction of the total period of time during which the probability of ensuring the connectivity and k-coverage concurrently is at least $\alpha$.~\textbf{(xii)} the time spent by WSN until loosing the connectivity or the coverage.~\textbf{(xiii)} the time spent by WSN until acceptable event detection ratio is not acceptable in the network.~\textbf{(xiv)} the time spent by WSN and the application requirement has been met.
+\indent The limited resources in WSNs have been addressed, and one of the main challenges in WSNs is the limited power resource. For this reason, 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. 
+
+\indent The authors have 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 are exist for the lifetime of a sensor network~\cite{ref92,ref93} such as: 
+%~\textbf{(i)}
+\begin{enumerate} [i.]
+
+\item 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~\cite{ref162,ref163}.
+\item The time spent by WSN and has at least a specific set $\beta$ of alive sensor nodes in WSN~\cite{ref164,ref165}.
+\item The time spent by WSN until the death of all wireless sensor nodes in WSN because they have been depleted their energy~\cite{ref166}.
+\item For  k-coverage, is the time spent by WSN in covering the area of interest by at least $k$ sensor nodes~\cite{DESK}.
+\item For 100 $\%$ coverage is the time spent by WSN in covering each target or the whole area by at least one sensor node~\cite{ref167}.
+\item For $\alpha$-coverage: the total time by which at least $\alpha$ part of the sensing field is covered by at least one node~\cite{ref168}; or is the time spent by WSN until the coverage ratio becomes less than a predetermined threshold $\alpha$~\cite{ref169}.
+\item The working time spent by the system before either the coverage ratio or delivery ratio become less than a predetermined threshold~\cite{ref170}.
+\item The number of the successful data gathering trips~\cite{ref173}.
+\item The number of sent packets~\cite{ref174}.
+\item The percentage of wireless sensor nodes that have a route to the sink~\cite{ref170}.
+\item The prediction of the total period of time during which the probability of ensuring the connectivity and k-coverage concurrently is at least $\alpha$~\cite{ref175}.
+\item The time spent by WSN until loosing the connectivity or the coverage~\cite{ref171}.
+\item The time spent by WSN until acceptable event detection ratio is not acceptable in the network~\cite{ref166}.
+\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 reflects 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 reflects 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.

 
 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 $\alpha$.
 
 
 \section{Coverage in Wireless Sensor Networks } 
 \label{ch1:sec:8}
 
 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 $\alpha$.
 
 
 \section{Coverage in Wireless Sensor Networks } 
 \label{ch1:sec:8}

-\indent Energy efficiency is  a crucial issue in wireless sensor networks since sensory
-consumption, in  order to  maximize the network lifetime, represents  the major
-difficulty when designing WSNs. As a consequence, one of the scientific research
-challenges in  WSNs, which has  been addressed by a large amount of literature
-during the  last few  years, is  the design of  energy efficient  approaches for
-coverage and connectivity~\cite{ref94,ref101}. Coverage reflects how well a
-sensor  field is  monitored. On the one hand, we want to monitor the  area of
-interest in the most efficient way~\cite{ref95}.  On the other hand, we want to
-use as little energy as possible. Sensor nodes are battery-powered  with no
-means  of recharging  or replacing,  usually  due to  environmental (hostile  or
+%\indent Energy efficiency is  a crucial issue in wireless sensor networks since sensory consumption, in  order to  maximize the network lifetime, represents  the major difficulty when designing WSNs. As a consequence, 
+
+One of the scientific research challenges in  WSNs, which has  been addressed by a large amount of literature during the  last few  years, is  the design of  energy efficient  approaches for coverage and connectivity~\cite{ref94,ref101}. Coverage reflects how well a sensor field is  monitored. On the one hand, we want to monitor the  area of interest in the most efficient way~\cite{ref95}.  On the other hand, we want to use few energy as possible. Sensor nodes are battery-powered  with no
+mean  of recharging  or replacing,  usually  due to  environmental (hostile  or

 unpractical environments)  or cost reasons.   Therefore, it is desired  that the
 WSNs are deployed  with high densities so as to  exploit the overlapping sensing
 regions of some sensor  nodes to save energy by turning off  some of them during
 unpractical environments)  or cost reasons.   Therefore, it is desired  that the
 WSNs are deployed  with high densities so as to  exploit the overlapping sensing
 regions of some sensor  nodes to save energy by turning off  some of them during


@@ -356,7 +369,7 @@ The most discussed coverage problems in literature can be classified into three
 \item \textbf{Barrier coverage}~\cite{ref99,ref100} to prevent intruders from entering into the region of interest. 
 \end{enumerate}
 
 \item \textbf{Barrier coverage}~\cite{ref99,ref100} to prevent intruders from entering into the region of interest. 
 \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 an obstacles in the environment that affect on the sensing range~\cite{ref104}, so there are 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, There are two common sensing coverage models have been used for simulating the performance of wireless sensors~\cite{ref104,ref105,ref106}:
+\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 an obstacles in the environment that affect on the sensing range~\cite{ref104}, so 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{The Binary Disc Sensing Model:}
 
 \begin{enumerate}[(A)] 
 \item \textbf{The Binary Disc Sensing Model:}


@@ -374,7 +387,7 @@ 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 +403,9 @@ 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 proposed in this dissertation use 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 +415,33 @@ 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 placement the sensor nodes can be selected based on the type of sensors, application, and the environment. 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 where 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 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 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 K-coverage mean 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 at least 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 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 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 of 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}
 
 
 \end{enumerate}
 

-\section{Energy Consumption Models:} 
+\section{Energy Consumption Modeling:} 

 \label{ch1:sec:9}
 \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 sensor node has a limited power battery, therefore, one of the most critical issues in WSNs is 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 are used~\cite{ref140}:  transmission, reception, listening, and sleeping; in addition, two states 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.
 


@@ -435,12 +449,12 @@ central controller (a node or base station) informs every sensors of the time in
 
 \end{enumerate}
 
 
 \end{enumerate}
 

-In this section, two energy consumption models are explained. The first model called radio energy dissipation model and the second model represent our energy consumption model, which has been used by the proposed protocols in this dissertation.
+%In this section, two energy consumption models are explained. The first model called radio energy dissipation model and the second model represent our energy consumption model, which has been used by the proposed protocols in this dissertation.

 
 
 
 

-\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.
+%\subsection{Radio Energy Dissipation Model:}
+%\label{ch1:sec9:subsec1}
+\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} 


@@ -448,7 +462,8 @@ In this section, two energy consumption models are explained. The first model ca
 \label{RDM}
 \end{figure}
 
 \label{RDM}
 \end{figure}
 

-\indent In this model, the radio consumes an energy to execute the transmitter and the power amplifier, and receiver circuitry consumes an energy to run the radio electronics, as described in figure~\ref{RDM}.  The channel model can be either free space ( power loss) or multipath fading ( 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; otherwise, the multipath ($C$) model is used. Therefore, to transmit an k-bit packet with a distance d, the radio expends:
+\indent In this model, the radio consumes an energy to execute the transmitter and the power amplifier, and receiver circuitry consumes an 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 an k-bit packet with a distance d, the radio expends:
+

 
 \begin{equation}
 E_{tx}\left(k,d \right)  = \left \{ 
 
 \begin{equation}
 E_{tx}\left(k,d \right)  = \left \{ 


@@ -466,50 +481,16 @@ As well as to receive an k-bit packet, the radio expends
 \label{eq4-ch1} 
 \end{equation}
 
 \label{eq4-ch1} 
 \end{equation}
 

-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. 
+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 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:}
-\label{ch1:sec9:subsec2}
-\indent 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.

 
 

-\indent 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 (expressed in  milliWatt per second) for  the different status of  the sensor is summarized in Table~\ref{table1}.
-
-\begin{table}[ht]
-\caption{The Energy Consumption Model}
-% title of Table
-\centering
-% used for centering table
-\begin{tabular}{|c|c|c|c|c|}
-% centered columns (4 columns)
-      \hline
-%inserts double horizontal lines
-Sensor status & MCU & Radio & Sensing & Power (mW) \\ [0.5ex]
-\hline
-% inserts single horizontal line
-LISTENING & on & on & on & 20.05 \\
-% inserting body of the table
-\hline
-ACTIVE & on & off & on & 9.72 \\
-\hline
-SLEEP & off & off & off & 0.02 \\
-\hline
-COMPUTATION & on & on & on & 26.83 \\
-%\hline
-%\multicolumn{4}{|c|}{Energy needed to send/receive a 1-bit} & 0.2575\\
- \hline
-\end{tabular}
-
-\label{table1}
-% is used to refer this table in the text
-\end{table}
-
-\indent 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 value for receiving the packets. The energy  needed to send or receive a 1-bit packet is equal to $0.2575~mW$.
+%\subsection{Our Energy Consumption Model:}
+%\label{ch1:sec9:subsec2}

 
 
 \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, the energy consumption Modeling have been demonstrated.