Update by Ali 28-1-2015 at 9h43

diff --git a/CHAPITRE_01.tex b/CHAPITRE_01.tex
index f98ffcbec24603f0f1a0ad547d31caf520b22562..6e5f1e1c7982096d0a132ce6624a23b29d7e04a0 100644 (file)
--- a/CHAPITRE_01.tex
+++ b/CHAPITRE_01.tex
@@ -114,29 +114,31 @@ The fast development in WSNs has been led to extensive study on different charac
 
 \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, patients are wearing the sensors in order to oversee the state of their health and to allow medical staff to respond efficiently.The most general used vital signs are ECG, pulse oximetry, body temperature, heart rate, blood pressure~\cite{ref27}. These applications includes:  mass-casualty disaster monitoring, vital sign monitoring in hospitals,  and sudden fall or epilepsy seizure detection. On the other hand, remote health-care surveillance is related to the health services that do not require constant presence of health care. These applications includes: 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, patients are wearing the sensors in order to oversee the state of their health and to allow medical staff to respond efficiently.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 is related to the health services that do not require constant presence of health care. These applications includes: 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:}

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

 
 

-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, 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}. 

 
 

-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).

 
 

-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}.

 
 \item \textbf{Public safety and military systems Applications:}
 The WSNs can be incorporated into military command, control, communications, computing, intelligence,
 
 \item \textbf{Public safety and military systems Applications:}
 The WSNs can be incorporated into military command, control, communications, computing, intelligence,

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

 
 \item \textbf{Transportation systems Applications:}
 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}.

-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 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 employed 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 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 employed 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}.
+

 \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 energy supply and  consumption operation. The main WSN applications in smart grid includes: 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 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 energy supply and  consumption operation. The main WSN applications in smart grid includes: 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}


@@ -144,20 +146,23 @@ The most significant goal for many companies is the automation of controlling an
 
 \section{The Main Challenges in Wireless Sensor Networks}
 \label{ch1:sec:05}
 
 \section{The Main Challenges in Wireless Sensor Networks}
 \label{ch1:sec:05}

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

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

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

 
 \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 on the application requirements in executing its tasks as long time as possible. 
 

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

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

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

 
 

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

 
 \item \textbf{Topology Control:} The maintenance and repair of the network topology is a challenging task because there are a large number of inaccessible sensor nodes that are prone to failure. So, some schemes need to used to deal with the dynamic changing of topology and the failure of some sensor nodes due to energy depletion or malfunction.
 
 
 \item \textbf{Topology Control:} The maintenance and repair of the network topology is a challenging task because there are a large number of inaccessible sensor nodes that are prone to failure. So, some schemes need to used to deal with the dynamic changing of topology and the failure of some sensor nodes due to energy depletion or malfunction.
 


@@ -174,7 +179,7 @@ Since the nature of many WSN applications that need to be deployed in a remote o
 
 \section{Energy-Efficient Mechanisms in Wireless Sensor Networks}
 \label{ch1:sec:06}
 
 \section{Energy-Efficient Mechanisms in Wireless Sensor Networks}
 \label{ch1:sec:06}

-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.
+\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.

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


@@ -182,19 +187,19 @@ The energy limited nature of wireless sensor nodes need to use energy efficient
 \label{emwsn}
 \end{figure}
 
 \label{emwsn}
 \end{figure}
 

-\subsection{Energy-Efficient Routing}
-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}. There are 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.
+\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}. There are 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.

 
 

-\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 and take it into account during executing the routing protocol in WSNs. So, the routing protocols should concentrate on the remaining power of sensor nodes during taking the decision to select the next hop toward the destination and not depend on the shortest path solution. It prioritizes routes on the basis of an energy metric (sometimes with other routing metrics) so it is called energy-aware routing protocols~\cite{ref45,ref46}.

 
 

-\subsubsection{Multipath Routing} efficient strategy that can provides reliability, security and load balancing  in order to forward packets in a limited energy and constrained resources(computation, communication, and storage) networks like WSNs~\cite{ref50}. The single path routing is simple and scalable but it is not efficient for energy constrained networks such as WSNs . There are many multipath routing protocol are summarized in~\cite{ref50,ref51}.
+\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 and take it into account during executing the routing protocol in WSNs. So, the routing protocols should concentrate on the remaining power of sensor nodes during taking the decision to select the next hop toward the destination and not depend on the shortest path solution. It prioritizes routes on the basis of an energy metric (sometimes with other routing metrics) so it is called energy-aware routing protocols~\cite{ref45,ref46}.
+
+\subsubsection{Multipath Routing:} Efficient strategy that can provides reliability, security and load balancing  in order to forward packets in a limited energy and constrained resources(computation, communication, and storage) networks like WSNs~\cite{ref50}. The single path routing is simple and scalable but it is not efficient for energy constrained networks such as WSNs . There are many multipath routing protocol are summarized in~\cite{ref50,ref51}.

 
 

- 

 
 

+\subsection{Cluster Architectures:}

 
 

-\subsection{Cluster Architectures}
-In this strategy, the wireless sensor nodes are grouped into several groups that called clusters, each group of wireless sensor nodes are managed by a single sensor node, which is called cluster head. The cluster head takes the responsibility of manging the activities of the wireless sensor nodes with the cluster and it communicates and coordinates with other cluster heads or the base station in the WSN. This mechanism conserves the energy in WSNs by means of~\cite{ref43,ref22}:
+\indent In this strategy, the wireless sensor nodes are grouped into several groups that called clusters, each group of wireless sensor nodes are managed by a single sensor node, which is called cluster head. The cluster head takes the responsibility of manging the activities of the wireless sensor nodes with the cluster and it communicates and coordinates with other cluster heads or the base station in the WSN. This mechanism conserves the energy in WSNs by means of~\cite{ref43,ref22}:

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


@@ -204,12 +209,13 @@ In this strategy, the wireless sensor nodes are grouped into several groups that
 \item Some nodes can be turned-off within the same cluster whilst the cluster head manage the responsibilities.
 \end{enumerate}
 
 \item Some nodes can be turned-off within the same cluster whilst the cluster head manage the responsibilities.
 \end{enumerate}
 

-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. There are 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 CH 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. There are 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 CH selection, synchronization, data aggregation, repair mechanisms, and Quality of Service (QoS)~\cite{ref161}.

 
 
 
 
 
 

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

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


@@ -218,13 +224,13 @@ There are many scheduling schemes have been suggested so as to decrease the ener
 \end{figure}
 
 
 \end{figure}
 
 

-\subsubsection{Wake up Scheduling Schemes}
+\subsubsection{Wake up Scheduling Schemes:}

 
 

-This section demonstrates the scheduling schemes from point of view of schedule Composition process and the framework of the wake up schedule. In these scheduling schemes, the wake up interval refers to the period of time at which the radio unit is turned on so as to sends or receives the packets. Whilst the sleep interval refers to a period of time at which the radio unit is turned off so as to retain the energy of wireless sensor node. Some schemes divide the time into equal length durations of time that called slotted schemes; on the other hand, the other schemes works with the time in continuous way that called unslotted schemes. The sleep and wake up intervals are defined for the unslotted schemes whilst for the slotted schemes, these intervals are represented as multiples of slots. The wake up schedule represents a set of a wake up and sleep intervals, which are produced for one period. Those 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 those wake up schedule is to permit to exchange of 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:
+\indent This section demonstrates the scheduling schemes from point of view of schedule Composition process and the framework of the wake up schedule. In these scheduling schemes, the wake up interval refers to the period of time at which the radio unit is turned on so as to sends or receives the packets. Whilst the sleep interval refers to a period of time at which the radio unit is turned off so as to retain the energy of wireless sensor node. Some schemes divide the time into equal length durations of time that called slotted schemes; on the other hand, the other schemes works with the time in continuous way that called unslotted schemes. The sleep and wake up intervals are defined for the unslotted schemes whilst for the slotted schemes, these intervals are represented as multiples of slots. The wake up schedule represents a set of a wake up and sleep intervals, which are produced for one period. Those 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 those wake up schedule is to permit to exchange of 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:

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

-\item \textbf{Synchronous schemes:} require that the time among wireless sensor nodes are synchronized. Several synchronous approaches have been suggested that based on the time synchronization in their work. The majority of synchronous schemes work in periodic 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 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. 

 
 \begin{enumerate} [(A)]
 \item The periodic wakeup scheduling schemes can be operate 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 difficult. 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}: 
 
 \begin{enumerate} [(A)]
 \item The periodic wakeup scheduling schemes can be operate 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 difficult. 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}: 


@@ -244,7 +250,7 @@ This section demonstrates the scheduling schemes from point of view of schedule
  
 \end{enumerate}
 
  
 \end{enumerate}
 

-\item \textbf{Asynchronous schemes:} the time among the wireless sensor nodes do not needs synchronization. The wireless sensor node wake up to send packets with out taking into account whether the receiving sensor nodes are wake up and ready to receive. The major advantages received from these schemes in that they do not need time synchronization that lead to remove the energy consumption required by applying the periodic resynchronization of time among the sensor nodes~\cite{ref74}. Another benefit come from using the asynchronous schemes that they do not need high exploitation for wireless sensor resources (processing, memory, and radio) because there is no shared wake up schedules to be exchanged or saved in the memory.  So,  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 do not needs synchronization. The wireless sensor node wake up to send packets with out taking into account whether the receiving sensor nodes are wake up and ready to receive. The major advantages received from these schemes in that they do not need time synchronization that lead to remove the energy consumption required by applying the periodic resynchronization of time among the sensor nodes~\cite{ref74}. Another benefit come from using the asynchronous schemes that they do not need high exploitation for wireless sensor resources (processing, memory, and radio) because there is no shared wake up schedules to be exchanged or saved in the memory.  So,  exchanging the packets among the wireless sensor nodes, which are not aware of each other's wake up schedules have been considered a major challenge in asynchronous schemes. These schemes can be categorized into three groups~\cite{ref57}: 

  
 \begin{enumerate} [(A)]
 \item Transmitter-initiated: a special frame is sent by the transmitting sensor node to inform the receiving sensor node that it has a data frame to send.  If the receiving sensor node is hearing the the special frame during one of its wake up intervals, it waits for sending the data frame. The major advantages of these schemes represented by the low requirement of the memory and processing whilst the major disadvantages are low-duty cycle and the sleep latency is non-deterministic. 
  
 \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. 


@@ -261,8 +267,9 @@ This section demonstrates the scheduling schemes from point of view of schedule
 
 
 
 
 
 

-\subsubsection{Topology Control Schemes}
- The topology control schemes are dealing with the redundancy in the WSNs. The WSN are always deploying with high density and in a random way, where a large number of wireless sensor nodes are usually throwing by the airplane over the area of interest. The purpose of deploying a dense WSN is to cope with the sensor failure during or after the WSN deployment and to maximize the network lifetime by means of exploiting the overlapping among the sensor nodes in the network by putting the redundant sensor nodes into sleep mode in order to benefit from it later. The major goal of topology control protocols is to dynamically adapt network topology based on requirements of application so as to minimize the number of active sensor nodes, achieve the tasks of the network, and prolong the network lifetime~\cite{ref56,ref22}. Many factors can be used to decide which sensor nodes should be turned on or off and when. The topology control schemes have been classified into two categories~\cite{ref56}: 
+\subsubsection{Topology Control Schemes:}
+
+\indent The topology control schemes are dealing with the redundancy in the WSNs. The WSN are always deploying with high density and in a random way, where a large number of wireless sensor nodes are usually throwing by the airplane over the area of interest. The purpose of deploying a dense WSN is to cope with the sensor failure during or after the WSN deployment and to maximize the network lifetime by means of exploiting the overlapping among the sensor nodes in the network by putting the redundant sensor nodes into sleep mode in order to benefit from it later. The major goal of topology control protocols is to dynamically adapt network topology based on requirements of application so as to minimize the number of active sensor nodes, achieve the tasks of the network, and prolong the network lifetime~\cite{ref56,ref22}. Many factors can be used to decide which sensor nodes should be turned on or off and when. The topology control schemes have been classified into two categories~\cite{ref56}: 

  
 \begin{enumerate} [(I)]
 \item \textbf{Location driven protocols} in which determining which wireless sensor node to turn on or off based on its location that should be known, such as Geographical Adaptive Fidelity (GAF) protocol~\cite{ref84}. These schemes are called network coverage that describing how the sensing field is monitored using minimum number of wireless sensor nodes in order to achieve application requirements and prolong the network lifetime~\cite{ref102}.
  
 \begin{enumerate} [(I)]
 \item \textbf{Location driven protocols} in which determining which wireless sensor node to turn on or off based on its location that should be known, such as Geographical Adaptive Fidelity (GAF) protocol~\cite{ref84}. These schemes are called network coverage that describing how the sensing field is monitored using minimum number of wireless sensor nodes in order to achieve application requirements and prolong the network lifetime~\cite{ref102}.


@@ -271,35 +278,41 @@ This section demonstrates the scheduling schemes from point of view of schedule
 \end{enumerate}
 
 
 \end{enumerate}
 
 

-\subsection{Data-Driven Schemes}
-Data driven approaches aim to decrease the amount of data sent to the sink whilst maintaining the accuracy of sensing within acceptable level. So, removing unwanted data during the transmission and restriction the sensing tasks during data acquisition can be participating in reduce the energy consumption in WSNs. 
+\subsection{Data-Driven Schemes:}
+
+\indent Data driven approaches aim to decrease the amount of data sent to the sink whilst maintaining the accuracy of sensing within acceptable level. So, removing unwanted data during the transmission and restriction the sensing tasks during data acquisition can be participating in reduce the energy consumption in WSNs. 

 %Several data-driven schemes have been proposed in~\cite{ref86,ref87,ref88,ref89,ref90}.
 Data driven schemes classified into two main approaches~\cite{ref59,ref22}:
  
 %\begin{enumerate} [(I)]
 \subsubsection{Data Reduction Schemes} that deal with reducing the amount of data need to be transmitted to sink. They can be divided into stochastic approaches, time series forecasting and algorithmic approaches. In stochastic approaches, the physical phenomena are transformed using stochastic characterization. the aggregating by these protocols require high processing so it is feasible to work on a powerful sensor nodes with a big battery. In time series forecasting, the old values of periodic sampling can be used to forecast a future value in the same series. In algorithmic approaches, sensed phenomena are demonstrated using heuristic or state transition model. 
 %Several data-driven schemes have been proposed in~\cite{ref86,ref87,ref88,ref89,ref90}.
 Data driven schemes classified into two main approaches~\cite{ref59,ref22}:
  
 %\begin{enumerate} [(I)]
 \subsubsection{Data Reduction Schemes} that deal with reducing the amount of data need to be transmitted to sink. They can be divided into stochastic approaches, time series forecasting and algorithmic approaches. In stochastic approaches, the physical phenomena are transformed using stochastic characterization. the aggregating by these protocols require high processing so it is feasible to work on a powerful sensor nodes with a big battery. In time series forecasting, the old values of periodic sampling can be used to forecast a future value in the same series. In algorithmic approaches, sensed phenomena are demonstrated using heuristic or state transition model. 

+

 \subsubsection{Energy Efficient Data Acquisition Schemes} are concentrated on the energy consumption reduction in the sensing unit. These schemes are divided into adaptive sampling, hierarchical sampling and model based active sampling. In adaptive sampling, the amount of data that acquired from the transducer can be reduced by spatial or temporal correlation between data. These approaches are more efficient to be used in centralized fusion but it consumes a high energy due to requiring a high processing. In hierarchical sampling, are more efficient when there are different types of sensor are installed on the nodes. These approaches are more energy efficient and application specific. The model based approaches are similar to data prediction schemes. These approaches aim to decrease the data samples by using computed models and conserve the energy by means of data acquisition. 
 %\end{enumerate} 
 
 
 \subsubsection{Energy Efficient Data Acquisition Schemes} are concentrated on the energy consumption reduction in the sensing unit. These schemes are divided into adaptive sampling, hierarchical sampling and model based active sampling. In adaptive sampling, the amount of data that acquired from the transducer can be reduced by spatial or temporal correlation between data. These approaches are more efficient to be used in centralized fusion but it consumes a high energy due to requiring a high processing. In hierarchical sampling, are more efficient when there are different types of sensor are installed on the nodes. These approaches are more energy efficient and application specific. The model based approaches are similar to data prediction schemes. These approaches aim to decrease the data samples by using computed models and conserve the energy by means of data acquisition. 
 %\end{enumerate} 
 
 

-\subsection{Battery Repletion}
-In the last years, extensive researches have been focused on energy harvesting and wireless charging techniques. These solutions are representing alternate energy sources to recharge wireless sensor batteries without human intervention and instead of depending on the limited power supplied by a typical batteries~\cite{ref91,ref59}.
-\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 are representing alternate energy sources to recharge wireless sensor batteries without human intervention and instead of depending on the limited power supplied by a typical batteries~\cite{ref91,ref59}.
+
+\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 the availability of WSNs and prolonging the network lifetime. Wireless charging in WSNs can be performed by using two manners: magnetic resonant coupling and electromagnetic radiation~\cite{ref22}.

 
 
 
 

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

 direction; and cognitive radio and Cooperative communications schemes~\cite{ref22}.
 
 direction; and cognitive radio and Cooperative communications schemes~\cite{ref22}.
 

-\subsection{Relay nodes and Sink Mobility}
-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.
+\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.

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

-\subsubsection{Relay node placement} 
+\subsubsection{Relay node placement:} 

 In WSN, some wireless sensor nodes in a certain region may be died and this will leads to create a hole in the WSN. This problem can be solved by placing the wireless sensor nodes in sensing field using optimal distribution or by deploying a small number of relay wireless sensor nodes with a powerful capabilities whose major goal is the communication with other wireless sensor nodes or relay nodes~\cite{ref52}. This solution can enhance the power balancing and avoiding the overloaded wireless sensor nodes in a particular region in WSN.
 
 In WSN, some wireless sensor nodes in a certain region may be died and this will leads to create a hole in the WSN. This problem can be solved by placing the wireless sensor nodes in sensing field using optimal distribution or by deploying a small number of relay wireless sensor nodes with a powerful capabilities whose major goal is the communication with other wireless sensor nodes or relay nodes~\cite{ref52}. This solution can enhance the power balancing and avoiding the overloaded wireless sensor nodes in a particular region in WSN.
 

-\subsubsection{Sink Mobility} 
+\subsubsection{Sink Mobility:} 

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


@@ -309,20 +322,21 @@ In WSNs that included a static sink, the wireless sensor nodes, which are near t
 
 \section{Network Lifetime in Wireless Sensor Networks}  
 \label{ch1:sec:07}
 
 \section{Network Lifetime in Wireless Sensor Networks}  
 \label{ch1:sec:07}

-The limited resources in WSNs have been addressed, and one of the main challenges in WSNs is the limited power resource. For this reason, there are extensive researches have been proposed in order to prolong the network lifetime by means of designing and implementing energy-efficient protocols. The reason for these large number of proposed protocols to maximize the network lifetime is the difficulty and sometime impossibility to replace or recharge the batteries of wireless sensor nodes especially in the large WSN and hostile environment. The authors have been defined the network lifetime in different contexts and use it as a metric to evaluate the performance of their protocols. Based on the previous proposed works in prolonging the network lifetime;Various definitions exist for the lifetime of a sensor network~\cite{ref92,ref93} such as:~\textbf{(i)} is the time spent by WSN until the death of the first wireless sensor node ( or cluster head ) in the network due to its energy depletion.~\textbf{(ii)} is the time spent by WSN and has at least a specific set $\beta$ of alive sensor nodes in WSN.~\textbf{(iii)} is the time spent by WSN until the death of all wireless sensor nodes in WSN because they have been depleted of their energy.~\textbf{(iv)} for  k-coverage is the time spent by WSN in covering the area of interest by at least $k$ sensor nodes.~\textbf{(v)} for 100 $\%$ coverage is the time spent by WSN in covering each target or the whole area by at least one sensor node.~\textbf{(vi)} for $\alpha$-coverage: the total time by which at least $\alpha$ part of the sensing field is covered by at least one node; or is the time spent by WSN until the coverage ratio becomes less than a predetermined threshold $\alpha$.
-~\textbf{(vii)} the working time spent by the system before either the coverage ratio or delivery ratio become less than a predetermined threshold.~\textbf{(viii)} the number of the successful data gathering trips.~\textbf{(ix)} the number of sent packets.~\textbf{(x)} the percentage of wireless sensor nodes that have a route to the sink.~\textbf{(xi)} the prediction of the total period of time during which the probability of ensuring the connectivity and k-coverage concurrently is at least $\alpha$.~\textbf{(xii)} the time spent by WSN until loosing the connectivity or the coverage.~\textbf{(xiii)} the time spent by WSN until acceptable event detection ratio is not acceptable in the network.~\textbf{(xiv)} the time spent by WSN and the application requirement has been met.
+\indent The limited resources in WSNs have been addressed, and one of the main challenges in WSNs is the limited power resource. For this reason, there are extensive researches have been proposed in order to prolong the network lifetime by means of designing and implementing energy-efficient protocols. The reason for these large number of proposed protocols to maximize the network lifetime is the difficulty and sometime impossibility to replace or recharge the batteries of wireless sensor nodes especially in the large WSN and hostile environment. 

 
 

-According to the above definitions for network lifetime, There is no universal definition reflects the requirements of each application and the effects of the environment. In real WSN, the network lifetime reflects a set of a particular circumstances of the environment. Accordingly, the current definitions are applicable for the WSNs that meet a particular conditions. However, there are many more parameters, which are affecting on the network lifetime of WSN such as~\cite{ref92}:  heterogeneity, node mobility, topology changes, application characteristics, quality of service, and completeness.
+\indent The authors have been defined the network lifetime in different contexts and use it as a metric to evaluate the performance of their protocols. Based on the previous proposed works in prolonging the network lifetime;Various definitions exist for the lifetime of a sensor network~\cite{ref92,ref93} such as:~\textbf{(i)} is the time spent by WSN until the death of the first wireless sensor node ( or cluster head ) in the network due to its energy depletion.~\textbf{(ii)} is the time spent by WSN and has at least a specific set $\beta$ of alive sensor nodes in WSN.~\textbf{(iii)} is the time spent by WSN until the death of all wireless sensor nodes in WSN because they have been depleted of their energy.~\textbf{(iv)} for  k-coverage is the time spent by WSN in covering the area of interest by at least $k$ sensor nodes.~\textbf{(v)} for 100 $\%$ coverage is the time spent by WSN in covering each target or the whole area by at least one sensor node.~\textbf{(vi)} for $\alpha$-coverage: the total time by which at least $\alpha$ part of the sensing field is covered by at least one node; or is the time spent by WSN until the coverage ratio becomes less than a predetermined threshold $\alpha$.~\textbf{(vii)} the working time spent by the system before either the coverage ratio or delivery ratio become less than a predetermined threshold.~\textbf{(viii)} the number of the successful data gathering trips.~\textbf{(ix)} the number of sent packets.~\textbf{(x)} the percentage of wireless sensor nodes that have a route to the sink.~\textbf{(xi)} the prediction of the total period of time during which the probability of ensuring the connectivity and k-coverage concurrently is at least $\alpha$.~\textbf{(xii)} the time spent by WSN until loosing the connectivity or the coverage.~\textbf{(xiii)} the time spent by WSN until acceptable event detection ratio is not acceptable in the network.~\textbf{(xiv)} the time spent by WSN and the application requirement has been met.
+
+\indent According to the above definitions for network lifetime, There is no universal definition reflects the requirements of each application and the effects of the environment. In real WSN, the network lifetime reflects a set of a particular circumstances of the environment. Accordingly, the current definitions are applicable for the WSNs that meet a particular conditions. However, there are many more parameters, which are affecting on the network lifetime of WSN such as~\cite{ref92}:  heterogeneity, node mobility, topology changes, application characteristics, quality of service, and completeness.

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

-Energy efficiency is  a crucial issue in wireless sensor networks since sensory
+\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
 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
+coverage and connectivity~\cite{ref94,ref101}. Coverage reflects how well a

 sensor  field is  monitored. On  the one  hand we  want to  monitor the  area of
 interest in the most efficient way~\cite{ref95}.  On the other hand we want to
 use  as little energy  as possible.   Sensor nodes  are battery-powered  with no
 sensor  field is  monitored. On  the one  hand we  want to  monitor the  area of
 interest in the most efficient way~\cite{ref95}.  On the other hand we want to
 use  as little energy  as possible.   Sensor nodes  are battery-powered  with no


@@ -338,10 +352,10 @@ The most discussed coverage problems in literature can be classified into three
 \item \textbf{Barrier coverage}~\cite{ref99,ref100} to prevent intruders from entering into the region of interest. 
 \end{enumerate}
 
 \item \textbf{Barrier coverage}~\cite{ref99,ref100} to prevent intruders from entering into the region of interest. 
 \end{enumerate}
 

-The sensing quality and capability can be assessed by a sensing coverage models due to discovering the mathematical relationship between the point and the sensor node in the sensing field. In the real world, there are sometimes an obstacles in the environment that affect on the sensing range~\cite{ref104}, so there are several sensing coverage models have been suggested according to application requirements and physical working environment such as~\cite{ref103}: boolean sector coverage, boolean disk coverage, attenuated disk coverage, truncated attenuated disk, detection coverage, and estimation coverage Models. However, There are two common sensing coverage models have been used for simulating the performance of wireless sensors~\cite{ref104,ref105,ref106}:
+\indent The sensing quality and capability can be assessed by a sensing coverage models due to discovering the mathematical relationship between the point and the sensor node in the sensing field. In the real world, there are sometimes an obstacles in the environment that affect on the sensing range~\cite{ref104}, so there are several sensing coverage models have been suggested according to application requirements and physical working environment such as~\cite{ref103}: boolean sector coverage, boolean disk coverage, attenuated disk coverage, truncated attenuated disk, detection coverage, and estimation coverage Models. However, There are two common sensing coverage models have been used for simulating the performance of wireless sensors~\cite{ref104,ref105,ref106}:

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

-\item \textbf{The Binary Disc Sensing Model}
+\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 $s_i$.
 \begin{equation}
 C_{xy}\left(s_i \right)  = \left \{ 
 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 $s_i$.
 \begin{equation}
 C_{xy}\left(s_i \right)  = \left \{ 


@@ -375,9 +389,9 @@ where $R_u$ is a measure of the uncertainty in sensor detection, $\alpha = d(s_i
 The coverage protocols that proposed in this dissertation have been used the binary disc sensing model.
 
 
 The coverage protocols that proposed in this dissertation have been used the binary disc sensing model.
 
 

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

 \label{ch1:sec:11}
 \label{ch1:sec:11}

-There are several design issues that should be considered in order to produce a solutions for the coverage problems in WSNs. These design issues can be classified into~\cite{ref103}:
+\indent Several design issues that should be considered in order to produce a 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. 
 
 \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. 


@@ -397,9 +411,9 @@ central controller (a node or base station) informs every sensors of the time in
 
 \end{enumerate}
 
 
 \end{enumerate}
 

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

 \label{ch1:sec:9}
 \label{ch1:sec:9}

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

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


@@ -420,9 +434,9 @@ The WSNs have been received a lot of interest because their low energy consumpti
 In this section, two energy consumption models are explained. The first model called radio energy dissipation model and the second model represent our energy consumption model, which has been used by the proposed protocols in this dissertation.
 
 
 In this section, two energy consumption models are explained. The first model called radio energy dissipation model and the second model represent our energy consumption model, which has been used by the proposed protocols in this dissertation.
 
 

-\subsection{Radio Energy Dissipation Model}
+\subsection{Radio Energy Dissipation Model:}

 \label{ch1:sec9:subsec1}
 \label{ch1:sec9:subsec1}

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

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


@@ -430,8 +444,7 @@ Since the communication unit is the most energy-consuming part inside the sensor
 \label{RDM}
 \end{figure}
 
 \label{RDM}
 \end{figure}
 

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

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


@@ -449,18 +462,16 @@ As well as to receive an k-bit packet, the radio expends
 \label{eq4-ch1} 
 \end{equation}
 
 \label{eq4-ch1} 
 \end{equation}
 

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

 
 

-The radio energy dissipation model have been considered only the energy consumed by the communication part inside the sensor node; however, in order to achieve a more accurate model, it is necessary to take into account the energy consumed by the other parts inside the sensor node such as: computation unit and sensing unit. 
+\indent The radio energy dissipation model have been considered only the energy consumed by the communication part inside the sensor node; however, in order to achieve a more accurate model, it is necessary to take into account the energy consumed by the other parts inside the sensor node such as: computation unit and sensing unit. 

 
 
 
 

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

 \label{ch1:sec9:subsec2}
 \label{ch1:sec9:subsec2}

-In this dissertation, the coverage protocols have been used an energy consumption model proposed by~\cite{ref111} and based on \cite{ref112} with slight  modifications.  The energy consumption for  sending/receiving the packets is added, whereas the  part related to the sensing range is removed because we consider a fixed sensing range.
-For our energy consumption model, we refer to the sensor node Medusa~II which uses an Atmels  AVR ATmega103L microcontroller~\cite{ref112}. The typical architecture  of a  sensor  is composed  of  four  subsystems: the  MCU subsystem which is capable of computation, communication subsystem (radio) which is responsible  for transmitting/receiving messages, the  sensing subsystem that collects  data, and  the  power supply  which  powers the  complete sensor  node
-\cite{ref112}. Each  of the first three subsystems  can be turned on or  off depending on  the current status  of the sensor.   Energy consumption
-(expressed in  milliWatt per second) for  the different status of  the sensor is summarized in Table~\ref{table1}.
+\indent In this dissertation, the coverage protocols have been used an energy consumption model proposed by~\cite{ref111} and based on \cite{ref112} with slight  modifications.  The energy consumption for  sending/receiving the packets is added, whereas the  part related to the sensing range is removed because we consider a fixed sensing range.
+
+\indent For our energy consumption model, we refer to the sensor node Medusa~II which uses an Atmels  AVR ATmega103L microcontroller~\cite{ref112}. The typical architecture  of a  sensor  is composed  of  four  subsystems: the  MCU subsystem which is capable of computation, communication subsystem (radio) which is responsible  for transmitting/receiving messages, the  sensing subsystem that collects  data, and  the  power supply  which  powers the  complete sensor  node \cite{ref112}. Each  of the first three subsystems  can be turned on or  off depending on  the current status  of the sensor.   Energy consumption (expressed in  milliWatt per second) for  the different status of  the sensor is summarized in Table~\ref{table1}.

 
 \begin{table}[ht]
 \caption{The Energy Consumption Model}
 
 \begin{table}[ht]
 \caption{The Energy Consumption Model}


@@ -491,12 +502,10 @@ COMPUTATION & on & on & on & 26.83 \\
 % is used to refer this table in the text
 \end{table}
 
 % is used to refer this table in the text
 \end{table}
 

-For the sake of simplicity we ignore  the energy needed to turn on the radio, to start up the sensor node, to move from one status to another, etc.
-Thus, when a sensor becomes active (i.e., it has already chosen its status), it can turn  its radio  off to  save battery. The value of energy spent to send a 1-bit-content message is  obtained by using  the equation in  ~\cite{ref112} to calculate  the energy cost for transmitting  messages and  we propose  the same
-value for receiving the packets. The energy  needed to send or receive a 1-bit packet is equal to $0.2575~mW$.
+\indent For the sake of simplicity we ignore  the energy needed to turn on the radio, to start up the sensor node, to move from one status to another, etc. Thus, when a sensor becomes active (i.e., it has already chosen its status), it can turn  its radio  off to  save battery. The value of energy spent to send a 1-bit-content message is  obtained by using  the equation in  ~\cite{ref112} to calculate  the energy cost for transmitting  messages and  we propose  the same value for receiving the packets. The energy  needed to send or receive a 1-bit packet is equal to $0.2575~mW$.

 
 
 \section{Conclusion}
 \label{ch1:sec:10}
 
 
 
 \section{Conclusion}
 \label{ch1:sec:10}
 

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

diff --git a/Thesis.toc b/Thesis.toc
index e96bc82de38571aecf06efb61aca2fb8e7d21f02..bd9d8acf17e605ae717d093933ae4bbbb5eec165 100644 (file)
--- a/Thesis.toc
+++ b/Thesis.toc
@@ -17,29 +17,29 @@
 \contentsline {section}{\numberline {1.4}Wireless Sensor Network Applications}{6}{section.1.4}
 \contentsline {section}{\numberline {1.5}The Main Challenges in Wireless Sensor Networks}{9}{section.1.5}
 \contentsline {section}{\numberline {1.6}Energy-Efficient Mechanisms in Wireless Sensor Networks}{11}{section.1.6}
 \contentsline {section}{\numberline {1.4}Wireless Sensor Network Applications}{6}{section.1.4}
 \contentsline {section}{\numberline {1.5}The Main Challenges in Wireless Sensor Networks}{9}{section.1.5}
 \contentsline {section}{\numberline {1.6}Energy-Efficient Mechanisms in Wireless Sensor Networks}{11}{section.1.6}

-\contentsline {subsection}{\numberline {1.6.1}Energy-Efficient Routing}{11}{subsection.1.6.1}
-\contentsline {subsubsection}{\numberline {1.6.1.1}Routing Metric based on Residual Energy}{11}{subsubsection.1.6.1.1}
-\contentsline {subsubsection}{\numberline {1.6.1.2}Multipath Routing}{12}{subsubsection.1.6.1.2}
-\contentsline {subsection}{\numberline {1.6.2}Cluster Architectures}{12}{subsection.1.6.2}
-\contentsline {subsection}{\numberline {1.6.3}Scheduling Schemes}{12}{subsection.1.6.3}
-\contentsline {subsubsection}{\numberline {1.6.3.1}Wake up Scheduling Schemes}{13}{subsubsection.1.6.3.1}
-\contentsline {subsubsection}{\numberline {1.6.3.2}Topology Control Schemes}{15}{subsubsection.1.6.3.2}
-\contentsline {subsection}{\numberline {1.6.4}Data-Driven Schemes}{16}{subsection.1.6.4}
+\contentsline {subsection}{\numberline {1.6.1}Energy-Efficient Routing:}{11}{subsection.1.6.1}
+\contentsline {subsubsection}{\numberline {1.6.1.1}Routing Metric based on Residual Energy:}{11}{subsubsection.1.6.1.1}
+\contentsline {subsubsection}{\numberline {1.6.1.2}Multipath Routing:}{12}{subsubsection.1.6.1.2}
+\contentsline {subsection}{\numberline {1.6.2}Cluster Architectures:}{12}{subsection.1.6.2}
+\contentsline {subsection}{\numberline {1.6.3}Scheduling Schemes:}{12}{subsection.1.6.3}
+\contentsline {subsubsection}{\numberline {1.6.3.1}Wake up Scheduling Schemes:}{13}{subsubsection.1.6.3.1}
+\contentsline {subsubsection}{\numberline {1.6.3.2}Topology Control Schemes:}{15}{subsubsection.1.6.3.2}
+\contentsline {subsection}{\numberline {1.6.4}Data-Driven Schemes:}{16}{subsection.1.6.4}

 \contentsline {subsubsection}{\numberline {1.6.4.1}Data Reduction Schemes}{16}{subsubsection.1.6.4.1}
 \contentsline {subsubsection}{\numberline {1.6.4.2}Energy Efficient Data Acquisition Schemes}{16}{subsubsection.1.6.4.2}
 \contentsline {subsubsection}{\numberline {1.6.4.1}Data Reduction Schemes}{16}{subsubsection.1.6.4.1}
 \contentsline {subsubsection}{\numberline {1.6.4.2}Energy Efficient Data Acquisition Schemes}{16}{subsubsection.1.6.4.2}

-\contentsline {subsection}{\numberline {1.6.5}Battery Repletion}{17}{subsection.1.6.5}
-\contentsline {subsubsection}{\numberline {1.6.5.1}Energy Harvesting}{17}{subsubsection.1.6.5.1}
-\contentsline {subsubsection}{\numberline {1.6.5.2}Wireless Charging}{17}{subsubsection.1.6.5.2}
-\contentsline {subsection}{\numberline {1.6.6}Radio Optimization}{17}{subsection.1.6.6}
-\contentsline {subsection}{\numberline {1.6.7}Relay nodes and Sink Mobility}{17}{subsection.1.6.7}
-\contentsline {subsubsection}{\numberline {1.6.7.1}Relay node placement}{17}{subsubsection.1.6.7.1}
-\contentsline {subsubsection}{\numberline {1.6.7.2}Sink Mobility}{18}{subsubsection.1.6.7.2}
+\contentsline {subsection}{\numberline {1.6.5}Battery Repletion:}{17}{subsection.1.6.5}
+\contentsline {subsubsection}{\numberline {1.6.5.1}Energy Harvesting:}{17}{subsubsection.1.6.5.1}
+\contentsline {subsubsection}{\numberline {1.6.5.2}Wireless Charging:}{17}{subsubsection.1.6.5.2}
+\contentsline {subsection}{\numberline {1.6.6}Radio Optimization:}{17}{subsection.1.6.6}
+\contentsline {subsection}{\numberline {1.6.7}Relay nodes and Sink Mobility:}{17}{subsection.1.6.7}
+\contentsline {subsubsection}{\numberline {1.6.7.1}Relay node placement:}{17}{subsubsection.1.6.7.1}
+\contentsline {subsubsection}{\numberline {1.6.7.2}Sink Mobility:}{18}{subsubsection.1.6.7.2}

 \contentsline {section}{\numberline {1.7}Network Lifetime in Wireless Sensor Networks}{18}{section.1.7}
 \contentsline {section}{\numberline {1.8}Coverage in Wireless Sensor Networks }{19}{section.1.8}
 \contentsline {section}{\numberline {1.7}Network Lifetime in Wireless Sensor Networks}{18}{section.1.7}
 \contentsline {section}{\numberline {1.8}Coverage in Wireless Sensor Networks }{19}{section.1.8}

-\contentsline {section}{\numberline {1.9}Design Issues for Coverage Problems}{20}{section.1.9}
-\contentsline {section}{\numberline {1.10}Energy Consumption Models}{21}{section.1.10}
-\contentsline {subsection}{\numberline {1.10.1}Radio Energy Dissipation Model}{22}{subsection.1.10.1}
-\contentsline {subsection}{\numberline {1.10.2}Our Energy Consumption Model}{23}{subsection.1.10.2}
+\contentsline {section}{\numberline {1.9}Design Issues for Coverage Problems:}{20}{section.1.9}
+\contentsline {section}{\numberline {1.10}Energy Consumption Models:}{21}{section.1.10}
+\contentsline {subsection}{\numberline {1.10.1}Radio Energy Dissipation Model:}{22}{subsection.1.10.1}
+\contentsline {subsection}{\numberline {1.10.2}Our Energy Consumption Model:}{23}{subsection.1.10.2}

 \contentsline {section}{\numberline {1.11}Conclusion}{23}{section.1.11}
 \contentsline {chapter}{\numberline {2}Related Literatures}{25}{chapter.2}
 \contentsline {section}{\numberline {2.1}Introduction}{25}{section.2.1}
 \contentsline {section}{\numberline {1.11}Conclusion}{23}{section.1.11}
 \contentsline {chapter}{\numberline {2}Related Literatures}{25}{chapter.2}
 \contentsline {section}{\numberline {2.1}Introduction}{25}{section.2.1}