Update by Ali

[ThesisAli.git] / CHAPITRE_01.tex

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@@ -115,15 +115,15 @@ A network consists of a low-cost wireless sensor nodes, which are equipped with
 
 \begin{enumerate}[(I)]
 
 
 \begin{enumerate}[(I)]
 

-\item \textbf{Health-care Applications:} There is increasing interest and extensive research in the health-care applications. Two types of health-care systems are recognized~\cite{ref22}: vital status monitoring and remote health-care surveillance. In vital status monitoring applications, sick persons are wearing the sensors in order to oversee the state of their health and to allow medical staff to monitor and control the patients status expeditiously. The most general used vital signs are ECG, pulse oximetry, body temperature, heart rate, blood pressure~\cite{ref27}. These applications include:  mass-casualty disaster monitoring, vital sign monitoring in hospitals, and sudden fall or epilepsy seizure detection. On the other hand, remote health-care surveillance refers to the health services that do not require continuous existence of health care. These applications include: elderly monitoring; providing support to a physically impaired person; gather clinically relevant information for rehabilitation supervision~\cite{ref28}; location tracking and medication intake monitoring~\cite{ref27}.
+\item \textbf{Health-care Applications:} There is increasing interest and extensive research in the health-care applications. Two types of health-care systems are recognized~\cite{ref22}: vital status monitoring and remote health-care surveillance. In vital status monitoring applications, sick persons are wearing the sensors in order to oversee the state of their health and to allow medical staff to monitor and control the patient's status expeditiously. The most general used vital signs are ECG, pulse oximetry, body temperature, heart rate, and blood pressure~\cite{ref27}. These applications include  mass-casualty disaster monitoring, vital sign monitoring in hospitals, and sudden fall or epilepsy seizure detection. On the other hand, remote health-care surveillance refers to the health services that do not require continuous existence of health care. These applications include elderly monitoring, providing support to a physically impaired person, gather clinically relevant information for rehabilitation supervision~\cite{ref28}, location tracking, and medication intake monitoring~\cite{ref27}.

 
 
 \item \textbf{ Environment and agriculture Applications:}
 
 
 \item \textbf{ Environment and agriculture Applications:}

-\indent Several WSNs applications have been developed to the precision agriculture, cattle monitoring, and environmental monitoring.
+\indent Several WSNs applications have been developed for the precision agriculture, cattle monitoring, and environmental monitoring.

 
 

-\indent Precision agriculture refers to the science of using the innovative and modern technology to improve the crop production, and the WSNs are the main technology for developing of precision agriculture~\cite{ref29}. This technology contributes in increasing the agricultural yields, improving quality, and reducing costs whilst decreasing the damaging impact for the environment. The wireless sensors are distributed over the target field so as to monitor the main parameters such as~\cite{ref22}: soil moisture, atmospheric temperature, creating a decision support system. The wireless sensors can be used in agricultural services like Irrigation, fertilization,  pest control, animal and pastures monitoring, horticulture(e.g., greenhouse and viticulture)~\cite{ref30}. 
+\indent Precision agriculture refers to the science of using the innovative and modern technology to improve the crop production. The WSNs are the main technology for developing of precision agriculture~\cite{ref29}. This technology contributes to increasing the agricultural yields, improving quality, and reducing costs whilst decreasing the damaging impact on the environment. The wireless sensors are distributed over the target field so as to monitor the main parameters such as soil moisture, atmospheric temperature, and creating a decision support system \cite{ref22}. The wireless sensors can be used in agricultural services like Irrigation, fertilization,  pest control, animal and pastures monitoring, horticulture(e.g., greenhouse and viticulture)~\cite{ref30}. 

 
 

-\indent In cattle monitoring applications, the WSN is used to livestock control and monitoring such as: virtual fencing for extensive grazing systems, animal behavior study, health monitoring, to detect disease breakouts, to localize them, and to control end-product quality (meat, milk).
+\indent In cattle monitoring applications, the WSN is used to livestock control and monitoring such as virtual fencing for extensive grazing systems, animal behavior study, health monitoring, to detect disease breakouts, to localize them, and to control end-product quality (meat, milk).  

 
 \indent Various WSN applications for environmental monitoring have been used in coastline erosion,  air quality monitoring, safe drinking water, and contamination control~\cite{ref22}.
 
 
 \indent Various WSN applications for environmental monitoring have been used in coastline erosion,  air quality monitoring, safe drinking water, and contamination control~\cite{ref22}.
 


@@ -131,15 +131,17 @@ A network consists of a low-cost wireless sensor nodes, which are equipped with
 The WSNs can be incorporated into military command, control, communications, computing, intelligence,
 surveillance, reconnaissance, and targeting systems. It estimates the unexpected events such as  natural disasters and threats as well as some of the military applications keep under surveillance friendly forces, equipment, and ammunition; battlefield surveillance;  reconnaissance of enemy forces; targeting; battle damage assessment; and nuclear, biological, and chemical attack detection and reconnaissance~\cite{ref19}. 
 
 The WSNs can be incorporated into military command, control, communications, computing, intelligence,
 surveillance, reconnaissance, and targeting systems. It estimates the unexpected events such as  natural disasters and threats as well as some of the military applications keep under surveillance friendly forces, equipment, and ammunition; battlefield surveillance;  reconnaissance of enemy forces; targeting; battle damage assessment; and nuclear, biological, and chemical attack detection and reconnaissance~\cite{ref19}. 
 

-\indent According to figure~\ref{WSNAP}, the public safety and military applications are categorized into active intervention and passive supervision~\cite{ref22}. In active intervention systems, the wireless sensors are portable with the agents and is devoted to the security of the team activities. During the work of the team, the leader will observe the agents situation and the environmental factors. The main applications include: emergency rescue teams, miners and soldiers. In passive supervision systems, the wireless static sensors are scattered over a large field in order to monitor a civil area or nuclear site for a longer time. These applications include: surveillance and target tracking; emergency navigation; fire detection in a building; structural health monitoring; and natural disaster prevention such as in the case of tsunamis, eruptions or flooding.
+\indent According to figure~\ref{WSNAP}, the public safety and military applications are categorized into active intervention and passive supervision~\cite{ref22}. In active intervention systems, the wireless sensors are portable with the agents and is devoted to the security of the team activities. During the work of the team, the leader will observe the agent's situation and the environmental factors. The main applications include emergency rescue teams, miners, and soldiers. In passive supervision systems, the wireless static sensors are scattered over a large field in order to monitor a civil area or nuclear site for a longer time. These applications include surveillance and target tracking; emergency navigation; fire detection in a building; structural health monitoring; and natural disaster prevention such as in the case of tsunamis, eruptions or flooding.
+

 
 \item \textbf{Transportation Systems Applications:}
 The fast development in the domain of Intelligent Transport Systems (ITS) ranging from flight transport and traffic management to in-vehicle services like driver alert or traffic monitoring. As a result, the transportation data collection and communication represent a major role in the ITS~\cite{ref37}.
 
 
 \item \textbf{Transportation Systems Applications:}
 The fast development in the domain of Intelligent Transport Systems (ITS) ranging from flight transport and traffic management to in-vehicle services like driver alert or traffic monitoring. As a result, the transportation data collection and communication represent a major role in the ITS~\cite{ref37}.
 

-\indent The WSNs can be integrated with the transportation systems such as traffic monitoring, real-time safety systems, and commercial services~\cite{ref22}. In traffic-monitoring systems, the wireless sensors are embedded within or across the pavement or intersections, and some sensors are installed above or on the side of roads so as to collect the informations related to the traffic~\cite{ref36}. These WSN traffic systems are used to detect the vehicles, vehicles count, and classification. In safety applications, the wireless sensors are used to deal with many cases such as: driving safety~\cite{ref41}, vehicle safety~\cite{ref38},  where many wireless sensors are scattered on roads or vehicles, collaborating through Vehicle-to-Vehicle, Vehicle-to-Roadside, and Vehicle-to-Infrastructure communications. Extensive research in these domains are concentrated on preventing the collisions among vehicles by Vehicle-to-Vehicle communications~\cite{ref40}. In addition, commercial applications can be given by service providers. They include route guidance to avoid rush-hour jams, smart high-speed tolling, assistance in finding a parking space, and automobile journey statistics collection~\cite{ref22}.
+\indent The WSNs can be integrated with the transportation systems such as traffic monitoring, real-time safety systems, and commercial services~\cite{ref22}. In traffic-monitoring systems, the wireless sensors are embedded within or across the pavement or intersections, and some sensors are installed above or on the side of roads so as to collect the information related to the traffic~\cite{ref36}. These WSN traffic systems are used to detect the vehicles, vehicles count, and classification. In safety applications, the wireless sensors are used to deal with many cases such as driving safety \cite{ref41}, vehicle safety~\cite{ref38},  where many wireless sensors are scattered on roads or vehicles, collaborating through Vehicle-to-Vehicle, Vehicle-to-Roadside, and Vehicle-to-Infrastructure communications. Extensive research in these domains is concentrated on preventing the collisions among vehicles by Vehicle-to-Vehicle communications~\cite{ref40}. In addition, commercial applications can be given by service providers. They include route guidance to avoid rush-hour jams, smart high-speed tolling, assistance in finding a parking space, and automobile journey statistics collection~\cite{ref22}.
+

 
 \item \textbf{Industry Applications: Manufacturing and Smart Grids:}
 
 \item \textbf{Industry Applications: Manufacturing and Smart Grids:}

-The most significant goal for many companies is the automation of controlling and monitoring systems in many applications such as: manufacturing, water treatment, electrical power distribution, and oil and gas refining. The WSNs is incorporated in Supervisory Control and Data Acquisition (SCADA) systems and smart grids~\cite{ref22}. SCADA systems are a computer softwares by which the industrial processes in factories are controlled and supervised. The wireless sensors are used with actuators to control the factory,  detection of liquid/gas leakages, and inventory management. These applications are needed for precise monitoring of temperature, shock, and noise factors in remote locations such as tanks, turbine engines, or pipelines. In Smart Grids, the goal is to supervise the power supply and  depletion operation. The main applications in smart grid include: sensing the relevant parameters affecting power output (pressure, humidity, wind orientation, radiation, etc.); control of turbines, motors and underground cables; home energy management; and remote detection of faulty components.
+The most significant goal for many companies is the automation of controlling and monitoring systems in many applications such as manufacturing, water treatment, electrical power distribution, and oil and gas refining. The WSNs is incorporated in Supervisory Control, Data Acquisition (SCADA) systems and smart grids~\cite{ref22}. SCADA systems are a computer software by which the industrial processes in factories are controlled and supervised. The wireless sensors are used with actuators to control the factory,  detection of liquid/gas leakages, and inventory management. These applications are needed for precise monitoring of temperature, shock, and noise factors in remote locations such as tanks, turbine engines, or pipelines. In Smart Grids, the goal is to supervise the power supply and  depletion operation. The main applications in smart grid include: sensing the relevant parameters affecting power output (pressure, humidity, wind orientation, radiation, etc.); control of turbines, motors and underground cables; home energy management; and remote detection of faulty components.

 
 \end{enumerate}
 %\section{Protocol Design Requirements}
 
 \end{enumerate}
 %\section{Protocol Design Requirements}


@@ -150,35 +152,37 @@ The most significant goal for many companies is the automation of controlling an
 \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)]
 \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{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{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 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 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{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 the 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 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.
+Since the nature of many WSN applications needs 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. Therefore, the main challenge is how to apply 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 are 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,  the 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 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{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 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 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{Topology Control:} The maintenance and repair of the network topology is a challenging task due to the large number of inaccessible sensor nodes that are prone to failure. Therefore, some schemes need to be used to deal with the dynamic changing of topology and the failure of some sensor nodes due to energy depletion or malfunction.

 
 

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

 
 

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

 
 \end{enumerate}
 
 
 
 \end{enumerate}
 
 

+

 \section{Energy-Efficient Mechanisms in Wireless Sensor Networks}
 \label{ch1:sec:06}
 \indent The energy limited nature of wireless sensor nodes need to use energy efficient mechanisms to prolong network lifetime. The energy efficient mechanisms can be classified into five categories~\cite{ref22}. Figure~\ref{emwsn} summarizes the energy-efficient mechanisms in WSNs.
 \section{Energy-Efficient Mechanisms in Wireless Sensor Networks}
 \label{ch1:sec:06}
 \indent The energy limited nature of wireless sensor nodes need to use energy efficient mechanisms to prolong network lifetime. The energy efficient mechanisms can be classified into five categories~\cite{ref22}. Figure~\ref{emwsn} summarizes the energy-efficient mechanisms in WSNs.


@@ -191,33 +195,37 @@ nodes in order to achieve their tasks efficiently.
 
 \subsection{Energy-Efficient Routing:}
 
 
 \subsection{Energy-Efficient Routing:}
 

-\indent The energy-efficient routing is a significant factor to the design of WSN protocols in order to satisfy the main constraints in the hardware, power, and other resources of wireless sensor nodes~\cite{ref42}. Many challenging factors need to be taken into consideration during designing a routing protocol for WSN, like: Limited energy capacity, node deployment, sensor location, dynamic network, hardware resource constraints, data aggregation and gathering, latency, scalability, and fault tolerance.
-
+\indent The energy-efficient routing is a significant factor in the design of WSN protocols in order to satisfy the main constraints in the hardware, power, and other resources of wireless sensor nodes~\cite{ref42}. Many challenging factors need to be taken into consideration in designing a routing protocol for WSN such as limited energy capacity, node deployment, sensor location, dynamic network, hardware resource constraints, data aggregation, latency, scalability, and fault tolerance.

 
 

-\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 summarized in~\cite{ref50,ref51}.
+\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 in 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:} Represents an 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 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}:
+\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 for managing the activities of the wireless sensor nodes in 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)]
 
 \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 Grouping the wireless sensor nodes into clusters is led to decrease the communication range within the cluster. Therefore, the energy needed for communication among the nodes inside the cluster is minimized.
+\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.
+\item The continuous changing of cluster head according to the 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}
 
 \end{enumerate}
 

-\indent In addition, the clustering supports network scalability in WSNs~\cite{ref43,ref44}. The clustering approach represents an efficient mechanism for scalability of WSN and providing energy-efficient data aggregation by minimizing the consumption of a limited energy by means of grouping the sensor nodes and organizing them hierarchically. Several important design considerations that should be taken into account during designing clustering algorithms, such as: limited energy, network lifetime, limited abilities, application dependency, secure communication, cluster formation, and cluster head selection, synchronization, data aggregation, repair mechanisms, and Quality of Service (QoS)~\cite{ref161}.
-
+\indent In addition, the clustering supports network scalability in WSNs~\cite{ref43,ref44}. The clustering approach represents an efficient mechanism for scalability of WSN and providing energy-efficient data aggregation by minimizing the consumption of a limited energy by means of grouping the sensor nodes and organizing them hierarchically. Several important design considerations that should be taken into account during designing clustering algorithms such as limited energy, network lifetime, limited abilities, application dependency, secure communication, cluster formation, cluster head selection, synchronization, data aggregation, repair mechanisms, and Quality of Service (QoS)~\cite{ref161}.

 
 
 \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 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. 
+\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}: 
+\begin{itemize}
+\item 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. 
+\item Topology control in which a set of a wireless sensor nodes is chosen to be awake from a given sensor nodes in WSN.
+\end{itemize}
+
+ 

 \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} 


@@ -228,22 +236,23 @@ 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 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}:
+\indent This section demonstrates the scheduling schemes from the 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. On the other hand, 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. 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)]
 

-\item \textbf{Synchronous Schemes:} The time synchronization among wireless sensor nodes is required. Several synchronous approaches have been suggested that based on the time synchronization in their work. The majority of synchronous schemes work in periodic (cyclic) way by preparing the same wake up schedule for every period unless a change by wake up scheduling algorithm. On the other hand, the aperiodic schemes does not apply the periodic schedule. 
+\item \textbf{Synchronous Schemes:} The time synchronization among wireless sensor nodes is required. Several synchronous approaches have been suggested that based on the time synchronization in their work. The majority of synchronous schemes work in periodic (cyclic) way by preparing the same wake-up schedule for every period unless a change by wake-up scheduling algorithm. On the other hand, the aperiodic schemes do not apply the periodic schedule. 

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

-\item The periodic wakeup scheduling schemes work either in slotted and unslotted way, where the period is divided into equal-length slots in the slotted schemes. The major challenge in periodic wakeup scheduling is to select and activate the best time interval(s) in a period so as to the wireless sensor node performs the communication (sending and receiving). This is from point of view of wireless sensor node, whilst from the standpoint of the WSN, choosing the time intervals through the wireless sensor nodes to satisfy a certain performance factor in WSN seems to be hard task. This performance can be carried out from the cooperation among the sensor nodes in WSN to produce the wake up schedule. The periodic wakeup scheduling schemes are classified into five groups based on the degree of the cooperation~\cite{ref57}: 
+\item The periodic wakeup scheduling schemes work either in slotted and unslotted way, where the period is divided into equal-length slots in the slotted schemes. The major challenge in periodic wakeup scheduling is to select and activate the best time interval(s) for a period so as to the wireless sensor node performs the communication (sending and receiving). This is from point of view of wireless sensor node, whilst from the standpoint of the WSN, choosing the time intervals through the wireless sensor nodes to satisfy a certain performance factor in WSN seems to be hard task. This performance can be carried out with the cooperation among the sensor nodes in WSN to produce the wake-up schedule. The periodic wakeup scheduling schemes are classified into five groups based on the degree of the cooperation~\cite{ref57}:
+ 

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

 %Some examples  used this approach~\cite{ref65,ref66,ref67}; 
 %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.  
+\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 objective.  These schemes can be centralized or distributed. In centralized scheme, one sensor node responsible for constructing the wake-up schedule for a subset or all nodes in WSN. In a distributed scheme, every wireless sensor node in the network contributes in the production of their wake-up schedules.  

 %Instances that used this approach in~\cite{ref68,ref69}; 
 %Instances that used this approach in~\cite{ref68,ref69}; 

-\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. 
+\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). 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}
 
 %Some examples that used these schemes~\cite{ref70,ref71}.
 \end{enumerate}
 


@@ -252,16 +261,16 @@ nodes in order to achieve their tasks efficiently.
  
 \end{enumerate}
 
  
 \end{enumerate}
 

-\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}: 
+\item \textbf{Asynchronous Schemes:} The time among the wireless sensor nodes does not need synchronization. The wireless sensor node wakes up to send packets without taking into account whether the receiving sensor nodes are 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 removing 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 considered as a major challenge in asynchronous schemes. These schemes can be categorized into three groups~\cite{ref57}: 

  
 \begin{enumerate} [(A)]
  
 \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. 
+\item Transmitter-initiated: a special frame is sent by the transmitting sensor node to inform the receiving sensor node that it has a data frame to send.  If the receiving sensor node is hearing the special frame during one of its wake up intervals, it waits for sending the data frame. The major 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}.
 
 %Examples on these schemes in~\cite{ref75,ref76}.
 

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

 %The proposed works in~\cite{ref81,ref82,ref83} give an instances for these schemes.
 \end{enumerate}
  
 %The proposed works in~\cite{ref81,ref82,ref83} give an instances for these schemes.
 \end{enumerate}
  


@@ -273,51 +282,50 @@ nodes in order to achieve their tasks efficiently.
 
 \subsubsection{Topology Control Schemes:}
 
 
 \subsubsection{Topology Control Schemes:}
 

-\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}: 
+\indent The topology control schemes deal with the redundancy in the WSNs. The WSN is 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)]
  
 \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, 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{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{GAF}. 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 connectivity of WSN 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:}
 
 \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. 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. 
+\indent Data-driven approaches aim to decrease the amount of data sent to the sink whilst maintaining the accuracy of sensing within an acceptable level. Therefore, removing unwanted data during the transmission and restriction the sensing tasks during data acquisition can be participating in reducing the energy consumption in WSNs. 

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

-\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{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 are 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 are 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 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. 
+\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. While, the hierarchical sampling are more efficient when there are different types of sensors 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} 
 
 %\end{enumerate} 
 

-

 \subsection{Battery Repletion:}
 
 \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}. 
 
 \subsection{Battery Repletion:}
 
 \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{Wireless Charging:}In wireless charging, the wireless power can be transmitted between the devices without requiring to the connection between the transmitter and the receiver. These techniques are participating in increasing the the availability of WSNs and prolonging the network lifetime. Wireless charging in WSNs can be performed by using two manners: magnetic resonant coupling and electromagnetic radiation~\cite{ref22}.
-
+\subsubsection{Wireless Charging:}In wireless charging, the wireless power can be transmitted between the devices without requiring to the connection between the transmitter and the receiver. These techniques are participating in increasing the availability of WSNs and prolonging the network lifetime. Wireless charging in WSNs can be performed by using two manners: magnetic resonant coupling and electromagnetic radiation~\cite{ref22}.

 
 \subsection{Radio Optimization:}
 
 
 \subsection{Radio Optimization:}
 

-\indent In wireless sensor node, the radio is the most energy-consuming unit for draining the battery power. Extensive researches have been focused on decreasing the power depletion due to wireless communication by means of optimizing the radio parameters such as: coding and modulation schemes; transmission Power and antenna
+\indent In wireless sensor node, the radio is the most energy-consuming unit for draining the battery power. Extensive researches have been focused on decreasing the power depletion due to wireless communication by means of optimizing the radio parameters such as coding and modulation schemes; transmission power and antenna

 direction; and cognitive radio and Cooperative communications schemes~\cite{ref22}.
 
 \subsection{Relay nodes and Sink Mobility:}
 direction; and cognitive radio and Cooperative communications schemes~\cite{ref22}.
 
 \subsection{Relay nodes and Sink Mobility:}

-
-\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.
+\indent The relay node 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:} 
 %\begin{enumerate} [(I)]
 \subsubsection{Relay node placement:} 

-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.
+In WSN, some wireless sensor nodes in a certain region may die 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 powerful capabilities. The major goal of relay nodes 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 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}.
+In the WSNs include a static sink, the wireless sensor nodes, which are near the sink drain their power more rapidly compared with other sensor nodes, and this leads to WSN disconnection and limited network lifetime~\cite{ref53}. Sending all the data in WSN to the sink maximizes the overload on the sensor nodes near 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 near to the base station, extending the lifetime of WSN~\cite{ref54,ref55}.
+
+ 

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


@@ -326,25 +334,25 @@ In WSNs includes a static sink, the wireless sensor nodes, which are near the si
 
 \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, 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 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 sometimes 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: 
+\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 previously proposed works in prolonging the network lifetime, various definitions 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}.
 %~\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 The time spent by WSN until the death of all wireless sensor nodes in WSN because they have depleted their energy~\cite{ref124}.
+\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 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 For $\alpha$-coverage: the total time by which at least $\alpha$ part of the sensing field is covered with 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 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 the successful data gathering trips~\cite{ref53}.

 \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 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 spent by WSN until losing 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{ref124}.

 \item The time during which the application requirement is satisfied~\cite{ref172}.
 \end{enumerate}
 
 \item The time during which the application requirement is satisfied~\cite{ref172}.
 \end{enumerate}
 


@@ -357,24 +365,18 @@ The network lifetime has been defined in this dissertation as the time spent by
 \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, 
 
 \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 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
-the sensing phase to prolong the network lifetime.
-The most discussed coverage problems in literature can be classified into three types~\cite{ref96}:
+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 less 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 the sensing phase to prolong the network lifetime. The most discussed coverage problems in literature can be classified into three types~\cite{ref102}:

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

-\item \textbf{Area coverage}~\cite{ref97,ref153} where every point inside an area is to be monitored. The work in this dissertation deals with this type of coverage.
-\item \textbf{Target coverage}~\cite{ref98,ref153} where the main objective is to cover only a finite number of discrete points called targets.
+\item \textbf{Area coverage}~\cite{ref97,ref101} where every point inside an area is to be monitored. The work in this dissertation deals with this type of coverage.
+\item \textbf{Target coverage}~\cite{ref98,ref101} where the main objective is to cover only a finite number of discrete points called targets.

 \item \textbf{Barrier coverage}~\cite{ref99,ref100} to prevent intruders from entering into the region of interest. 
 \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 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}:
+\indent The sensing quality and capability can be assessed by a sensing coverage models due to discovering the mathematical relationship between the point and the sensor node in the sensing field. In the real world, there are sometimes obstacles in the environment that affect the sensing range \cite{ref104}. Therefore, several sensing coverage models have been suggested according to application requirements and physical working environment such as~\cite{ref103}: boolean sector coverage, boolean disk coverage, attenuated disk coverage, truncated attenuated disk, detection coverage, and estimation coverage Models. However, two main sensing coverage models have been used for simulating the performance of wireless sensors~\cite{ref104,ref105,ref106}:

 
 \begin{enumerate}[(A)] 
 \item \textbf{The Binary Disc Sensing Model:}
 
 \begin{enumerate}[(A)] 
 \item \textbf{The Binary Disc Sensing Model:}

-It is the simplest sensing coverage model in which every point in the sensing field can be sensed if it is within the sensing range of the wireless sensor node, otherwise, it is not able to detect any point that is outside the sensing range of the sensor node. The sensing range in this model can be viewed as a circular disk with a radius equal to $R_s$. Assume that a sensor node $s_i$ is deployed in the position $(x_i,y_i)$. For any point P at the position $(x,y)$, the equation \ref{eq1-ch1} shows the binary sensor model that expresses the coverage $C_{xy}$ of the point P by sensor node $s_i$.
+It is the simplest sensing coverage model in which every point in the sensing field can be sensed if it is within the sensing range of the wireless sensor node. Otherwise, it is not able to detect any point that is outside the sensing range of the sensor node. The sensing range in this model can be viewed as a circular disk with a radius equal to $R_s$. Assume that a sensor node $s_i$ is deployed in the position $(x_i,y_i)$. For any point P at the position $(x,y)$, the equation \ref{eq1-ch1} shows the binary sensor model that expresses the coverage $C_{xy}$ of the point P by sensor node $s_i$ as follow

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


@@ -388,7 +390,7 @@ 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 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 $s_i$ as follow
+In reality, the event detection by the sensor node is imprecise. Hence, the coverage $C_{xy}$ requires to be represented in probabilistic way. The probabilistic sensing model is more practical, which can be used as an extension of 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 the sensor node $s_i$ as follow

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


@@ -400,11 +402,11 @@ C_{xy}\left(s_i \right)  = \left \{
 \label{eq2-ch1} 
 \end{equation}
 
 \label{eq2-ch1} 
 \end{equation}
 

-where $R_u$ is a measure of the uncertainty in sensor detection, $\alpha = d(s_i,P) - (R_s - R_u)$, and $\lambda$ and $\beta$ are parameters that measure detection probability when a point P is at distance greater than $R_u$ but within a distance from the sensor node $s_i$. 
+Where $R_u$ is a measure of the uncertainty in sensor detection, $\alpha = d(s_i,P) - (R_s - R_u)$.  The $\lambda$ and the $\beta$ are parameters that measure detection probability when a point P is at distance greater than $R_u$ but within a distance from the sensor node $s_i$. 

 
 \end{enumerate}
 
 
 \end{enumerate}
 

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

 
 
 
 
 
 


@@ -414,25 +416,26 @@ The coverage protocols proposed in this dissertation use the binary disc sensing
 \indent Several design issues should be considered in order to produce solutions for the coverage problems in WSNs. These design issues can be classified into~\cite{ref103}:
 
 \begin{enumerate}[(i)]
 \indent Several design issues should be considered in order to produce solutions for the coverage problems in WSNs. These design issues can be classified into~\cite{ref103}:
 
 \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 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 $\textbf{Coverage Type}$ 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 of a barrier.

  
  

-\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 $\textbf{Deployment Method}$ 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 $\textbf{Coverage Ratio}$ 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 $\textbf{Coverage Degree}$ refers to how many sensor nodes are required to cover a target or an area. This K-coverage means 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 with at least one sensor.
+
+\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 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 $\textbf{Network Connectivity}$ 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 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.
+ 
+\item $\textbf{Activity based Scheduling}$ is to schedule the activation and deactivation of sensor nodes during the network lifetime. The basic objective is to decide which sensors are in what states (active or sleeping mode) and for how long, so that the application coverage requirement can be guaranteed and the network lifetime can be prolonged. Various  centralized, distributed, and localized approaches have been proposed for activity scheduling. In distributed algorithms, each node in the network autonomously makes decisions on whether to turn on or turn off itself only using local neighbor information. In centralized algorithms, a central controller (a node or base station) informs every sensor of the time intervals to be activated.

 
 \end{enumerate}
 
 \section{Energy Consumption Modeling:} 
 \label{ch1:sec:9}
 
 \end{enumerate}
 
 \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 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:
+\indent The WSNs have been received a lot of interests because the low energy consumption of the sensor nodes.  One of the most critical issues in WSNs is to reduce the energy consumption of the limited power battery of the 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)]
 


@@ -444,9 +447,11 @@ guaranteed and the network lifetime can be prolonged. Various approaches, includ
 
 \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, analog to digital conversion, preprocessing, and maybe storing.
+
+\item Sleeping: a low power to the sensor node to stay alive.
+

 
 

-\item Sleeping: provide a low power to sensor node to stay alive.

 
 \end{enumerate}
 
 
 \end{enumerate}
 


@@ -455,7 +460,7 @@ guaranteed and the network lifetime can be prolonged. Various approaches, includ
 
 %\subsection{Radio Energy Dissipation Model:}
 %\label{ch1:sec9:subsec1}
 
 %\subsection{Radio Energy Dissipation Model:}
 %\label{ch1:sec9:subsec1}

-\indent Since the communication unit is the most energy-consuming part inside the sensor node, 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.
+\indent Since the communication unit is the most energy-consuming part of 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} 


@@ -463,7 +468,7 @@ guaranteed and the network lifetime can be prolonged. Various approaches, includ
 \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 ($d^2$ 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:
+\indent In this model, the radio consumes an energy to execute the transmitter and the power amplifier. The 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 a k-bit packet with a distance d, the radio expends

 
 
 \begin{equation}
 
 
 \begin{equation}


@@ -475,17 +480,16 @@ E_{tx}\left(k,d \right)  = \left \{
 \label{eq3-ch1} 
 \end{equation}
 
 \label{eq3-ch1} 
 \end{equation}
 

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

 
 \begin{equation}
 
 \begin{equation}

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

 \label{eq4-ch1} 
 \end{equation}
 
 The typical parameters are set as: $E_{elec}$ = 50 nJ/bit, $\varepsilon_{fs}$ = 10 pJ/bit/$m^2$, $\varepsilon_{mp}$ = 0.0013 pJ/bit/$m^4$. In addition, the energy for data aggregation is set as $E_{DA}$ = 5 nJ/bit.
 
 \label{eq4-ch1} 
 \end{equation}
 
 The typical parameters are set as: $E_{elec}$ = 50 nJ/bit, $\varepsilon_{fs}$ = 10 pJ/bit/$m^2$, $\varepsilon_{mp}$ = 0.0013 pJ/bit/$m^4$. In addition, the energy for data aggregation is set as $E_{DA}$ = 5 nJ/bit.
 

-\indent The radio energy dissipation model 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. 
-
+\indent The radio energy dissipation model have been considered only the energy, which is consumed by the communication part of the sensor node. However, in order to achieve a more accurate model, it is necessary to take into account the energy 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}
 
 %\subsection{Our Energy Consumption Model:}
 %\label{ch1:sec9:subsec2}


@@ -494,4 +498,4 @@ The typical parameters are set as: $E_{elec}$ = 50 nJ/bit, $\varepsilon_{fs}$ =
 \section{Conclusion}
 \label{ch1:sec:10}
 
 \section{Conclusion}
 \label{ch1:sec:10}
 

-\indent In this chapter, an overview about the wireless sensor networks have presented that represent our focus in this dissertation.  The structure of 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 explained; on the other hand, the energy efficient solutions have 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. 
+\indent In this chapter, an overview of the wireless sensor networks have been presented that shows our focus in this dissertation.  The structure of 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 including health, home, environmental, military, and industrial applications have been presented.  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 explained; on the other hand, the energy efficient solutions have proposed in order to handle these challenges. 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 in designing a coverage protocol for WSNs. In addition, the energy consumption Modeling have been demonstrated.