X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/ThesisAli.git/blobdiff_plain/0a3bc115e20c81675323db7c2c8824ae419ce087..dd42ca97656c19804fa0624b8e9095293f58976f:/CHAPITRE_01.tex

diff --git a/CHAPITRE_01.tex b/CHAPITRE_01.tex
index ddbf3fd..495a608 100644
--- 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} 
-\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}
@@ -237,11 +237,11 @@ nodes in order to achieve their tasks efficiently.
 \begin{enumerate} [(i)]
 \item Neighbor-coordinated in which the wireless sensor node generates its own wake up schedule taking into consideration the wake up schedules of its neighbor sensor nodes. 
 %The protocols that used this approach like : S-MAC protocol, Timeout MAC (T-MAC), Pattern-MAC (PMAC), Dynamic S-MAC (DSMAC), and ESC; 
-\item Path-coordinated is suggested to allow the wireless sensor nodes along the path to collaborate to manage their wake up schedules so as to permit to packets passing on the path without delay. The sleep interval represents the main problem for the duty-cycling WSNs that participating in end-to-end delay. 
+\item Path-coordinated is suggested to allow the wireless sensor nodes along the path to collaborate to manage their wake up schedules so as to permit to packets passing on the path without delay.  
 %Some examples  used this approach~\cite{ref65,ref66,ref67}; 
 \item Network-coordinated: the wireless sensor nodes are cooperated in order to produce a global or per sensor node wake up schedule that achieves a specific objectives.  These schemes can be centralized in which one sensor node responsible of constructing the wakeup schedule for a subset or all nodes in WSN; or distributed in which every wireless sensor node in the network contributes in the production of their wakeup schedules.  
 %Instances that used this approach in~\cite{ref68,ref69}; 
-\item Non-collaborative: in this schemes, the wireless sensor node applies control theory, or other tools, which are based on local information inside the sensor node (such as queue length or duty cycle). i.e., It does not use the information from the other nodes in order to construct its own wake up schedule. 
+\item Non-collaborative: in these schemes, the wireless sensor node applies control theory, or other mechanisms, which are based on local information inside the sensor node (such as queue length or duty cycle). i.e., It does not use the information from the other nodes in order to construct its own wake up schedule. 
 %Some examples that used these schemes~\cite{ref70,ref71}.
 \end{enumerate}
 
@@ -250,18 +250,20 @@ nodes in order to achieve their tasks efficiently.
  
 \end{enumerate}
 
-\item \textbf{Asynchronous Schemes:} The time among the wireless sensor nodes do not needs synchronization. The wireless sensor node wake up to send packets with out taking into account whether the receiving sensor nodes are wake up and ready to receive. The major advantages received from these schemes in that they do not need time synchronization that lead to remove the energy consumption required by applying the periodic resynchronization of time among the sensor nodes~\cite{ref74}. Another benefit come from using the asynchronous schemes that they do not need high exploitation for wireless sensor resources (processing, memory, and radio) because there is no shared wake up schedules to be exchanged or saved in the memory. Therefore,  exchanging the packets among the wireless sensor nodes, which are not aware of each other's wake up schedules have been considered a major challenge in asynchronous schemes. These schemes can be categorized into three groups~\cite{ref57}: 
+\item \textbf{Asynchronous Schemes:} The time among the wireless sensor nodes does not need synchronization. The wireless sensor node wake up to send packets without taking into account whether the receiving sensor nodes are wake up and ready to receive. The major advantage is received by these schemes in that they do not need time synchronization that lead to remove the energy consumption is required to apply the periodic time resynchronization among the sensor nodes~\cite{ref74}. Another advantage of using the asynchronous schemes in that they do not need to exploit the limited resources (processing, memory, and radio) of the sensor nodes because there is no  shared wake up schedules to be exchanged or saved in the memory. Therefore,  exchanging the packets among the wireless sensor nodes, which are not aware of each other's wake up schedules have been considered a major challenge in asynchronous schemes. These schemes can be categorized into three groups~\cite{ref57}: 
  
 \begin{enumerate} [(A)]
 \item Transmitter-initiated: a special frame is sent by the transmitting sensor node to inform the receiving sensor node that it has a data frame to send.  If the receiving sensor node is hearing the the special frame during one of its wake up intervals, it waits for sending the data frame. The major advantages of these schemes represented by the low requirement of the memory and processing whilst the major disadvantages are low-duty cycle and the sleep latency is non-deterministic. 
 %Examples on these schemes in~\cite{ref75,ref76}.
-\item Receiver-initiated: during each wake up time interval of receiving wireless sensor node, it sends a special frame to inform the senders that it is wake up and ready to receive the data frames.  When the sensor node has a packet to send it wakes up and wait receiving special frame from neighboring sensor nodes. The waiting sensor node is sending its data frame at the moment of receiving the special frame from the neighbor sensor node. The main advantages are a  low processing and storage requirement whilst the disadvantage of these schemes are the low performance during low and high duty cycle as well as their sleep latency is stochastic. 
+
+\item Receiver-initiated: during each wake up time interval of receiving wireless sensor node, it sends a special frame to inform the senders, which are waked up and ready to receive the data frames.  When the sensor node has a packet to send it wakes up and wait receiving special frame from neighboring sensor nodes. The waiting sensor node is sending its data frame at the moment of receiving the special frame from the neighbor sensor node. The main advantages are a low processing and storage requirement whilst the disadvantage of these schemes are the low performance during low and high duty cycle as well as their sleep latency is stochastic. 
 %The works that proposed in~\cite{ref77,ref78} represents some instances for these approaches.
-\item Combinatorial or random: during the wake up duration, one or more data packets can be exchanged among the wireless sensor nodes. The data packets are exchanged among the wireless sensor nodes are increased as the wake up time increased. In this schemes, the special frames are removed and the energy consumption is decreased. 
+
+\item Combinatorial or random: during the wake up duration, one or more data packets can be exchanged among the wireless sensor nodes. The data packets are exchanged among the wireless sensor nodes are increased as the wake up time increased. In these schemes, the special frames are removed and the energy consumption is decreased. 
 %The proposed works in~\cite{ref81,ref82,ref83} give an instances for these schemes.
 \end{enumerate}
  
-\item \textbf{Hybrids schemes:} Some schemes need to use both time synchronous and Asynchronous methods. According to WSN circumstances, the wake up scheduling switches between synchronous and asynchronous modes, where the synchronous schemes work efficiently in the heavy load circumstances whilst in the light load circumstances, the asynchronous schemes are more efficient. 
+\item \textbf{Hybrids Schemes:} Some schemes need to use both time synchronous and asynchronous methods. According to WSN circumstances, the wake up scheduling switches between synchronous and asynchronous modes, where the synchronous schemes work efficiently in the heavy load circumstances whilst in the light load circumstances, the asynchronous schemes are more efficient. 
 %The protocols in~\cite{ref79,ref80} are an examples on these schemes.
 \end{enumerate}
 
@@ -269,25 +271,25 @@ nodes in order to achieve their tasks efficiently.
 
 \subsubsection{Topology Control Schemes:}
 
-\indent The topology control schemes are dealing with the redundancy in the WSNs. The WSN are always deploying with high density and in a random way, where a large number of wireless sensor nodes are usually throwing by the airplane over the area of interest. The purpose of deploying a dense WSN is to cope with the sensor failure during or after the WSN deployment and to maximize the network lifetime by means of exploiting the overlapping among the sensor nodes in the network by putting the redundant sensor nodes into sleep mode in order to benefit from it later. The major goal of topology control protocols is to dynamically adapt network topology based on requirements of application so as to minimize the number of active sensor nodes, achieve the tasks of the network, and prolong the network lifetime~\cite{ref56,ref22}. Many factors can be used to decide which sensor nodes should be turned on or off and when. The topology control schemes have been classified into two categories~\cite{ref56}: 
+\indent The topology control schemes deal with the redundancy in the WSNs. The WSN are always deploying with high density and in a random way, where a large number of wireless sensor nodes are usually throwing by the airplane over the area of interest. The purpose of deploying a dense WSN is to cope with the sensor failure during or after the WSN deployment and to maximize the network lifetime by means of exploiting the overlapping among the sensor nodes in the network by putting the redundant sensor nodes into sleep mode in order to benefit from it later. The major goal of topology control protocols is to dynamically adapt network topology based on requirements of application so as to minimize the number of active sensor nodes, achieve the tasks of the network, and prolong the network lifetime~\cite{ref56,ref22}. Many factors can be used to decide which sensor nodes should be turned on or off and when. The topology control schemes have been classified into two categories~\cite{ref56}: 
  
 \begin{enumerate} [(I)]
-\item \textbf{Location driven protocols} in which determining which wireless sensor node to turn on or off based on its location that should be known, such as Geographical Adaptive Fidelity (GAF) protocol~\cite{ref84}. These schemes are called network coverage that describing how the sensing field is monitored using minimum number of wireless sensor nodes in order to achieve application requirements and prolong the network lifetime~\cite{ref102}.
+\item \textbf{Location Driven Protocols} in which determining which wireless sensor node to turn on or off based on its location that should be known, for example, Geographical Adaptive Fidelity (GAF) protocol~\cite{ref84}. These schemes are called network coverage that describing how the sensing field is monitored using minimum number of wireless sensor nodes in order to achieve application requirements and prolong the network lifetime~\cite{ref102}.
 
-\item \textbf{Connectivity driven protocols} in which the wireless sensor nodes are activated or deactivated so that the sensing coverage and WSN connectivity are assured, such as Span protocol~\cite{ref85}.
+\item \textbf{Connectivity Driven Protocols} in which the wireless sensor nodes are activated or deactivated so that the sensing coverage and connectivity of WSN are assured, such as Span protocol~\cite{ref85}.
 \end{enumerate}
 
 
 \subsection{Data-Driven Schemes:}
 
-\indent Data driven approaches aim to decrease the amount of data sent to the sink whilst maintaining the accuracy of sensing within acceptable level. So, removing unwanted data during the transmission and restriction the sensing tasks during data acquisition can be participating in reduce the energy consumption in WSNs. 
+\indent Data driven approaches aim to decrease the amount of data sent to the sink whilst maintaining the accuracy of sensing within acceptable level. Therefore, removing unwanted data during the transmission and restriction the sensing tasks during data acquisition can be participating in reduce the energy consumption in WSNs. 
 %Several data-driven schemes have been proposed in~\cite{ref86,ref87,ref88,ref89,ref90}.
-Data driven schemes classified into two main approaches~\cite{ref59,ref22}:
+Data driven schemes are classified into two main approaches~\cite{ref59,ref22}:
  
 %\begin{enumerate} [(I)]
-\subsubsection{Data Reduction Schemes} that deal with reducing the amount of data need to be transmitted to sink. They can be divided into stochastic approaches, time series forecasting and algorithmic approaches. In stochastic approaches, the physical phenomena are transformed using stochastic characterization. the aggregating by these protocols require high processing so it is feasible to work on a powerful sensor nodes with a big battery. In time series forecasting, the old values of periodic sampling can be used to forecast a future value in the same series. In algorithmic approaches, sensed phenomena are demonstrated using heuristic or state transition model. 
+\subsubsection{Data Reduction Schemes} deal with reducing the amount of data need to be transmitted to sink. They can be divided into stochastic approaches, time series forecasting, and algorithmic approaches. In stochastic approaches, the physical phenomena is transformed using stochastic characterization. The aggregation by these protocols requires high processing, therefore it is feasible to work on a powerful sensor nodes with a big battery. In time series forecasting, the old values of periodic sampling can be used to forecast a future value in the same series. In algorithmic approaches, sensed phenomena is demonstrated using heuristic or state transition model. 
 
-\subsubsection{Energy Efficient Data Acquisition Schemes} are concentrated on the energy consumption reduction in the sensing unit. These schemes are divided into adaptive sampling, hierarchical sampling and model based active sampling. In adaptive sampling, the amount of data that acquired from the transducer can be reduced by spatial or temporal correlation between data. These approaches are more efficient to be used in centralized fusion but it consumes a high energy due to requiring a high processing. In hierarchical sampling, are more efficient when there are different types of sensor are installed on the nodes. These approaches are more energy efficient and application specific. The model based approaches are similar to data prediction schemes. These approaches aim to decrease the data samples by using computed models and conserve the energy by means of data acquisition. 
+\subsubsection{Energy Efficient Data Acquisition Schemes} are concentrated on the energy consumption reduction in the sensing unit. These schemes are divided into adaptive sampling, hierarchical sampling, and model based active sampling. In adaptive sampling, the amount of data that acquired from the transducer can be reduced by spatial or temporal correlation between data. These approaches are more efficient to be used in centralized fusion, but it consumes more energy due to requiring a high processing. In hierarchical sampling, are more efficient when there are different types of sensor are installed on the nodes. These approaches are more energy efficient and application specific. The model based approaches are similar to data prediction schemes. These approaches aim to decrease the data samples by using computed models and conserve the energy by means of data acquisition. 
 %\end{enumerate} 
 
 
@@ -310,10 +312,10 @@ 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:} 
-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.
+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.
 
 \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 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}.
+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}.
 
 %\end{enumerate}
 
@@ -324,9 +326,11 @@ In WSNs that included a static sink, the wireless sensor nodes, which are near t
 \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 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 $\alpha$.~\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 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 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, there are 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 } 
@@ -334,12 +338,12 @@ In WSNs that included a static sink, the wireless sensor nodes, which are near t
 \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
+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
+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
 unpractical environments)  or cost reasons.   Therefore, it is desired  that the
 WSNs are deployed  with high densities so as to  exploit the overlapping sensing
@@ -370,7 +374,7 @@ where $d(s_i,P) = \sqrt{(x_i - x)^2 + (y_i - y)^2}$, denotes the Euclidean dista
 
 
 \item \textbf{The Probabilistic Sensing Model}
-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 \{ 
@@ -386,7 +390,7 @@ where $R_u$ is a measure of the uncertainty in sensor detection, $\alpha = d(s_i
 
 \end{enumerate}
 
-The coverage protocols that proposed in this dissertation have been used the binary disc sensing model.
+The coverage protocols that proposed in this dissertation have been used the binary disc sensing model as a sensing coverage model for each wireless sensor node in WSN.
 
 
 \section{Design Issues for Coverage Problems:}
@@ -396,34 +400,32 @@ The coverage protocols that proposed in this dissertation have been used the bin
 \begin{enumerate}[(i)]
 \item $\textbf{Coverage Type}$ refers to determining what is it exactly that you are trying to cover. Typically, it may be required to monitor a whole area, observe a set of targets, or look for a breach among a barrier. 
   
-\item $\textbf{Deployment Method}$ refers to the way by which the wireless sensor nodes are deployed over the target sensing field in order to build the wireless sensor network. Generally, the sensor nodes can be placed either deterministically or randomly in the target sensing field so as to construct the wireless sensor network~\cite{ref107}. The method of placing the sensor nodes can be selected based on the type of sensors, application, and the environment, which the wireless sensor nodes will work in it. In the deterministic placing, the deployment can be achieved in case of small number of sensor nodes and in friendly environment, whilst for a large number of sensor nodes or the area of interest is Inaccessible or hostile, a random placing is the choice. The sensor network can be either dense or sparse. the dense deployment is preferred when it is important to detect the event or when it is required that the area covered by more than one sensor node. On the other hand, the sparse deployment is used when the dense deployment is expensive or when the maximum coverage is performed by a less number of sensor nodes.
+\item $\textbf{Deployment Method}$ refers to the way by which the wireless sensor nodes are deployed over the target sensing field in order to build the wireless sensor network. Generally, the sensor nodes can be placed either deterministically or randomly in the target sensing field so as to construct the wireless sensor network~\cite{ref107}. The method of placing the sensor nodes can be selected based on the type of sensors, application, and the environment in which the wireless sensor nodes will work. In the deterministic placing, the deployment can be achieved in case of small number of sensor nodes and in friendly environment, whilst for a large number of sensor nodes; or the area of interest is Inaccessible or hostile, a random placing is the choice. The sensor network can be either dense or sparse. the dense deployment is preferred when it is important to detect the event or when it is required that the area covered by more than one sensor node. On the other hand, the sparse deployment is used when the dense deployment is expensive or when the maximum coverage is performed by a less number of sensor nodes.
  
-\item $\textbf{Coverage Degree}$ refers to how many sensor nodes required it to cover a target or an area. This can be described as K-coverage in which the point in the sensing field is covered by at least K sensor nodes. There are some applications that need a high reliability to achieve their tasks, so the sensing field have been deployed densely so as to perform a K-coverage for this field. The simple coverage problem consists of a coverage degree equal to one (i.e., K=1), where every point in the sensing field is covered by only one sensor.
+\item $\textbf{Coverage Degree}$ refers to how many sensor nodes are required to cover a target or an area. This can be described as K-coverage in which the point in the sensing field is covered by at least K sensor nodes. Some applications need a high reliability to achieve their tasks, therefore, the sensing field is deployed densely so as to perform a K-coverage for this field. The simple coverage problem consists of a coverage degree equal to one (i.e., K=1), where every point in the sensing field is covered by only one sensor.
 
 \item $\textbf{Coverage Ratio}$ is the percentage of the area of sensing field that fulfill the coverage degree of the application. If all the points in the sensing field are covered, the coverage ratio is $100\%$ and it can be called a complete coverage, otherwise it can be called as partial coverage.
 
-\item $\textbf{Network Connectivity}$ is to ensure the existence a path from any sensor node in WSN to the sink. The connected WSN refers to guarantee sending the sensed data from one sensor node to another sensor node toward directly to the sink. It is necessary to consider the communication range of wireless sensor node is at least twice that of the sensing range ($R_c \geqslant 2R_s$) so as to imply connectivity among the sensor nodes during covering the sensing field~\cite{ref108}.
+\item $\textbf{Network Connectivity}$ is to ensure the existence a path from any sensor node in WSN to the sink. The connected WSN refers to guarantee sending the sensed data from one sensor node to another sensor node directly toward the sink. It is necessary to consider the communication range of wireless sensor node is at least twice that of the sensing range ($R_c \geqslant 2R_s$) so as to imply connectivity among the sensor nodes during covering the sensing field~\cite{ref108}.
 
-\item $\textbf{Activity based Scheduling}$ is to schedule the activation and deactivation of sensor nodes. The basic objective is to decide which sensors are in what states (active or sleeping mode) and for how long, so that the application coverage requirement can be
-guaranteed and the network lifetime can be prolonged. Various approaches, including centralized, distributed, and localized algorithms, have been proposed for activity scheduling. In
-distributed algorithms, each node in the network autonomously makes decisions on whether to turn on or turn off itself only using local neighbor information. In centralized algorithms, a
-central controller (a node or base station) informs every sensors of the time intervals to be activated.
+\item $\textbf{Activity based Scheduling}$ is to schedule the activation and deactivation of sensor nodes during the network lifetime. The basic objective is to decide which sensors are in what states (active or sleeping mode) and for how long, so that the application coverage requirement can be
+guaranteed and the network lifetime can be prolonged. Various approaches, including centralized, distributed, and localized algorithms, have been proposed for activity scheduling. In distributed algorithms, each node in the network autonomously makes decisions on whether to turn on or turn off itself only using local neighbor information. In centralized algorithms, a central controller (a node or base station) informs every sensors of the time intervals to be activated.
 
 \end{enumerate}
 
-\section{Energy Consumption Models:} 
+\section{Energy Consumption Modeling:} 
 \label{ch1:sec:9}
-\indent The WSNs have been received a lot of interest because their low energy consumption sensor nodes.  Since the sensor node has a limited power battery; so, one of the most critical issues in WSNs is how to reduce the energy consumption of sensor nodes so as to prolong the network lifetime as long as possible. In order to model the energy consumption, four states for a sensor node have been used~\cite{ref140}:  transmission, reception, listening, and sleeping; and we can add another two states that should be taken into account: computation and sensed data acquisition. The main tasks of each of these states include:
+\indent The WSNs have been received a lot of interest because their low energy consumption sensor nodes.  Since the sensor node has a limited power battery; therefore, one of the most critical issues in WSNs is how to reduce the energy consumption of sensor nodes so as to prolong the network lifetime as long as possible. In order to model the energy consumption, four states for a sensor node have been used~\cite{ref140}:  transmission, reception, listening, and sleeping; in addition, two states that should be taken into account: computation and sensed data acquisition. The main tasks of each of these states include:
 
 \begin{enumerate}[(i)]
 
-\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.
 
@@ -431,12 +433,12 @@ central controller (a node or base station) informs every sensors of the time in
 
 \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} 
@@ -464,48 +466,14 @@ As well as to receive an k-bit packet, the radio expends
 
 The typical parameters are set as: $E_{elec}$ = 50 nJ/bit, $\varepsilon_{fs}$ = 10 pJ/bit/$m^2$, $\varepsilon_{fs}$ = 0.0013 pJ/bit/$m^4$. In addition, the energy for data aggregation is set as $E_{DA}$ = 5 nJ/bit.
 
-\indent The radio energy dissipation model have been considered only the energy consumed by the communication part inside the sensor node; however, in order to achieve a more accurate model, it is necessary to take into account the energy consumed by the other parts inside the sensor node such as: computation unit and sensing unit. 
+\indent The radio energy dissipation model have been considered only the energy, which is consumed by the communication part inside the sensor node; however, in order to achieve a more accurate model, it is necessary to take into account the energy is consumed by the other parts inside the sensor node such as: computation unit and sensing unit. 
 
 
-\subsection{Our Energy Consumption Model:}
-\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}
 
-\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. 
