\section{Introduction}
\label{ch1:sec:01}
%The wireless networking has received more attention and fast growth in the last decade.
-This last decade, wireless networking has became a major component of the global network infrastructure.
+In the last decade, wireless networking has became a major component of the global network infrastructure.
More precisely, the growing demand for the use of wireless applications and the continuous arrival of wireless devices such as portable computers, cellular phones, and personal digital assistants (PDAs) have led to develop different infrastructures of wireless networks. The wireless networks can be classified into two classes based on the network architecture~\cite{ref154,ref155}: Infrastructure-based networks that consist of a fixed network structure such as cellular networks and wireless local-area networks
(WLANs); and Infrastructureless networks that are constructed dynamically by the cooperation of the wireless nodes in the network, where each node is capable of sending packets and taking decisions based on the network status. Examples of such type of networks include mobile ad hoc networks and wireless sensor networks. Figure~\ref{WNT} shows the taxonomy of wireless networks.
\begin{figure}[h!]
\centering
-\includegraphics[scale=0.5]{Figures/ch1/twsn2.pdf}
+\includegraphics[scale=0.52]{Figures/ch1/twsn2.pdf}
\caption{ Components of a typical wireless sensor node.}
\label{twsn}
\end{figure}
\begin{enumerate} [(I)]
-\item \textbf{Sensing Unit:} It consists of two main parts: sensors and Analog to Digital Converters (ADCs). It is responsible for sensing physical phenomena. The analog signal produced by a sensor is converted into digital data by ADC and then sent to the computation unit for further processing.
+\item \textbf{Sensing Unit:} It consists of two main parts: sensors and Analog to Digital Converters (ADCs). It is responsible for sensing physical phenomena. The analog signal produced by a sensor is converted into digital data by ADC, and then sent to the computation unit for further processing.
\item \textbf{Computation Unit:} The main purpose of this unit is to manage and manipulate the instructions that are related to sensing, communication, and self-organization. This allows the sensor node to cooperate with other sensor nodes in order to perform the allocated sensing tasks. It is composed of a processor chip, an active short-term memory for storing the sensed data, an internal flash memory for storing program instructions, and an internal timer.
\end{enumerate}
-Furthermore, additional components can be incorporated into wireless sensor node according to the application requirements, such as a localization system, a power generator, and a mobilizer~\cite{ref17,ref19}. These components are showed by the dashed boxes in figure~\ref{twsn}.
+Furthermore, additional components can be incorporated into wireless sensor node according to the application requirements, such as a localization system, a power generator, and a mobilizer~\cite{ref17,ref19}. These components are showed by the dashed boxes in figure~\ref{twsn}.
\begin{enumerate} [(I)]
\item \textbf{Localization System:} It is important that a sensor node is equipped with a location finding system because it is necessary for many WSN applications. It is required for routing algorithms and sensing coverage algorithms, which need information about the location of the wireless sensor nodes. Location finding system is composed of a Global Positioning System (GPS) or a discovery algorithm which provides information about the location of wireless sensor node using distributed computation \cite{ref232,ref233}.
-\item \textbf{Mobilizer:} The mobility function is sometimes needed in some applications, like move the wireless sensor node from one location to another so as to perform a certain task in WSN. Therefore it is necessary to equip the node with the mobilizer system for such applications. A high energy provision is needed to support mobility in wireless sensor node, and it should be supported efficiently. The movements are controlled by the mobility function in cooperation with the sensing unit and the computation unit.
+\item \textbf{Mobilizer:} The mobility function is sometimes needed in some applications, like health monitoring~\cite{ref27} and animal tracking~\cite{ref22}, to move the wireless sensor node from one location to another so as to perform a certain task in WSN. Therefore, it is necessary to equip the node with the mobilizer system for such applications. A high energy provision is needed to support mobility in wireless sensor node, and it should be supported efficiently. The movements are controlled by the mobility function in cooperation with the sensing unit and the computation unit.
-\item \textbf{Power Generator:} Several applications in WSNs need to operate for a long time. So it is essential to equip the wireless sensor node with additional power source in order to prolong the network lifetime. The better energy source to generate power in outdoor applications is a solar cell. Other power harvesting mechanisms~\cite{ref20,ref21} like thermal, motion, vibration, micro water flow, biological, pressure gradients, and electromagnetic radiation energy harvesting can be used to yield increasing power output.
+\item \textbf{Power Generator:} Several applications in WSNs need to operate for a long time. So, it is essential to equip the wireless sensor node with additional power source in order to prolong the network lifetime. The better energy source to generate power in outdoor applications is a solar cell. Other power harvesting mechanisms~\cite{ref20,ref21} like thermal, motion, vibration, micro water flow, biological, pressure gradients, and electromagnetic radiation energy harvesting can be used to yield increasing power output.
\end{enumerate}
\begin{figure}[h!]
\label{wsn}
\end{figure}
-%The TinyOS has been used as an operating system in wireless sensor node. It is developed by the university of California, Berkeley and designed to work on platforms with limited storage and processing power.
+The sensor node use a software layer called, Operating System (OS), is logically locates between the node's hardware and the application layer~\cite{ref18}. The OS enables the applications to interact with hardware resources, to schedule and prioritize tasks, memory management, power management, file management, networking, and to arbitrate between contending applications and services that attempt to reserve resources. The TinyOS has been used as an operating system in wireless sensor node. It is developed by the university of California, Berkeley and designed to work on platforms with limited storage and processing power.
\section{Types of Wireless Sensor Networks}
Nodes are deployed over caves, mines, or underground and communicate through soil~\cite{ref9,ref10}. The most important applications in underground WSNs are structural monitoring, agriculture monitoring, landscape management, underground environment monitoring of soil, water or mineral and military border monitoring. The essential challenges of underground WSNs are the high levels of attenuation and signal loss in communication. Therefore, it needs a certain type of devices able to provide a robust wireless underground communication. The risk on these devices comes from unsuitable underground conditions, replacing or recharging the battery seems to be impossible, and the WSN deployment is expensive.
\item \textbf{Underwater WSNs:}
-Such a WSN is composed of nodes deployed in the water such as the ocean~\cite{ref11,ref12}. Many challenges must be faced in this type of WSN such as the high cost of the underwater sensor devices; underwater wireless communication with limited bandwidth, high latency, signal fading, and long propagation delay problems; sparse deployment in which the wireless sensors should be able to self-organized to adapt to various condition of the ocean environment; the limited power of the node battery, and the difficulty to replace or recharge it. These challenges led to look for energy efficient underwater wireless communication mechanisms. The main underwater WSNs applications are seismic monitoring, disaster prevention monitoring, underwater robotics, pollution monitoring, equipment monitoring, and undersea surveillance and exploration.
+This type of WSNs is composed of wireless sensor nodes deployed in the water such as the ocean~\cite{ref11,ref12}. Many challenges must be faced in this type of WSN such as the high cost of the underwater sensor devices; underwater wireless communication with limited bandwidth, high latency, signal fading, and long propagation delay problems; sparse deployment in which the wireless sensors should be able to self-organized to adapt to various condition of the ocean environment; the limited power of the node battery, and the difficulty to replace or recharge it. These challenges led to look for energy efficient underwater wireless communication mechanisms. The main underwater WSNs applications are seismic monitoring, disaster prevention monitoring, underwater robotics, pollution monitoring, equipment monitoring, and undersea surveillance and exploration.
\item \textbf{Multimedia WSNs:}
They consist of inexpensive wireless sensor nodes supplied with CMOS (Complementary Metal-Oxide-Silicon) cameras or microphones devices. The nodes are deployed in a pre-guided way to ensure the coverage. Multimedia WSN is capable of retrieving and storing audio, video, and image contents from the physical environment~\cite{ref13,ref14,ref15}. Multimedia WSN contributed in improving some existing WSN applications such as tracking and monitoring. The main challenges in multimedia WSN include: the processing, filtering, and compressing of multimedia data; the requested bandwidth and high energy consumption; Quality-of-Service provisioning is very difficult because of the link capacity and delays; it should combine different wireless techniques; energy-efficient cross-layer design; it needs flexible architecture to support various applications; and the deployment is based on the multimedia devices coverage.
\begin{enumerate}[(I)]
-\item \textbf{Health-care Applications:} There is an increasing interest and extensive research in this domain. 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 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 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 an increasing interest and extensive research in this domain. 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 their health state 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 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}
\indent Several WSNs applications have been developed for precision agriculture, cattle monitoring, and environmental monitoring.
\indent Precision agriculture refers to the science of using innovative and modern technologies to improve the crop production. WSNs are the main technology for developing 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 create 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 Various WSN applications can be used in agricultural services and environmental monitoring like Irrigation, fertilization, pest control, animal and pastures monitoring, horticulture (e.g., greenhouse and viticulture), coastline erosion, air quality monitoring, safe drinking water, and contamination control~\cite{ref30,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}. For instance, 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{ref30,ref22}.
+
+%\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).
\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. In that case WSNs are incorporated in Supervisory Control and Data Acquisition (SCADA) systems and smart grids~\cite{ref22}.A SCADA system is a computer software by which industrial processes in factories are controlled and supervised. The wireless sensors are used with actuators to control the factory, to detect 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. In that case WSNs are incorporated in Supervisory Control and Data Acquisition (SCADA) systems and smart grids~\cite{ref22}.A SCADA system is a computer software by which industrial processes in factories are controlled and supervised. The wireless sensors are used with actuators to control the factory, to detect of liquid/gas leakages, and for 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}
\item \textbf{Routing:} It 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 nodes in WSNs.
\item \textbf{Autonomous and Distributed Management:}
-Since the nature of many WSN applications induce a deployment in a remote or hostile environment, it is important that the wireless sensor nodes work in an autonomous and distributed way to communicate and cooperate without any human intervention since 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.
+Since the nature of many WSN applications induce a deployment in a remote or hostile environment, it is important that the wireless sensor nodes work in an autonomous and distributed way to communicate and cooperate, without any human intervention since 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 the deployment of a dense WSN. A large number of sensor nodes maybe needed for different reasons such as the huge size of the sensed area, the reliability requirement, or network lifetime prolongation. It is necessary that the proposed protocols for WSNs are scalable for these large number of sensor nodes in order to achieve their tasks efficiently.
-
-\item \textbf{Reliability:} Many applications require high quality of coverage, connectivity, routing, data aggregation, etc. These applications need to deploy a large number of inexpensive sensor nodes so as to satisfy their requirements. This large number of the sensor nodes may be prone to 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{Reliability:} Many applications require high quality of services, connectivity, routing, data aggregation, etc. These applications need to deploy a large number of inexpensive sensor nodes so as to satisfy their requirements. This large number of the sensor nodes may be prone to 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 due to the large number of inaccessible sensor nodes that are prone to failure. Therefore, some schemes need to be used to deal with dynamic topology changes and sensor node failure due to energy depletion or malfunction.
\section{Energy-Efficient Mechanisms of a working WSN}
\label{ch1:sec:06}
-\indent The strong constraint on limiting wireless sensor nodes energy usage demand energy efficient mechanisms to prolong network lifetime. The energy efficient mechanisms can be classified into five categories~\cite{ref22}, as summarized in figure~\ref{emwsn}.
+\indent The strong constraint on limiting wireless sensor nodes energy usage requires energy efficient mechanisms to prolong network lifetime. The energy efficient mechanisms can be classified into five categories~\cite{ref22}, as summarized in figure~\ref{emwsn}.
\begin{figure}[h!]
\centering
\includegraphics[scale=0.4]{Figures/ch1/WSN-M.eps}
\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 that an active 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 seems to be hard task. This level of 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 a cooperation~\cite{ref57}:
\begin{enumerate} [(i)]
-\item Neighbor-coordinated is in which a wireless sensor node generates its own wake-up schedule taking into consideration the wake-up schedules of its neighbor sensor nodes.
+\item Neighbor-coordinated is a scheme in which a 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 in order to permit packets passing on the path without delay.
%Some examples used this approach~\cite{ref65,ref66,ref67};
\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 waked up and ready to receive. These schemes do not need time synchronization which consumes energy~\cite{ref74}. They do not need to exploit the limited resources (processing, memory, and radio) of the sensor nodes because there are 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, is have considered as a major challenge in asynchronous schemes. These schemes can been categorized into three groups~\cite{ref57}:
+The wireless sensor node wakes up to send packets without taking into account whether the receiving sensor nodes are waked up and ready to receive. These schemes do not need time synchronization which consumes energy~\cite{ref74}. They do not need to exploit the limited resources (processing, memory, and radio) of the sensor nodes because there are 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, is a major challenge in asynchronous schemes. These schemes can been 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 special frame during one of its wake up intervals, the receiving node waits for sending the data frame by sender to receive it. The major advantage of these schemes is the low memory and processing requirements whilst the major disadvantages are low-duty-cycle and the non-deterministic sleep latency.
%\begin{enumerate} [(I)]
\subsubsection{Data Reduction Schemes}
-Data reduction schemes deal with reducing the amount of data to be transmitted to a sink. They can be divided into stochastic approaches, time series forecasting, and algorithmic approaches. In stochastic approaches, physical phenomena are transformed using stochastic characterization. The aggregation by these protocols requires high processing. Therefore, it is feasible only on 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 described using heuristic or state transition model.
+Data reduction schemes deal with reducing the amount of data to be transmitted to a sink. They can be divided into stochastic approaches, time series forecasting, and algorithmic approaches. In stochastic approaches, physical phenomena are transformed using stochastic characterization. The aggregation by these protocols requires high processing. Therefore, it is feasible only on 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 described using heuristic or state transition model.
\subsubsection{Energy Efficient Data Acquisition Schemes}
They concentrate 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 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 they consume more energy due to requiring a high processing. Hierarchical sampling is more efficient when there are different types of sensors 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 to conserve the energy by means of data acquisition.
\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. These energy sources are 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}.
+\begin{enumerate} [i)]
+\item{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. These energy sources are 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 power can be transmitted between the devices without requiring a connection between the transmitter and the receiver. These techniques participate in increasing the availability of WSNs and prolonging the network lifetime. Wireless charging in WSNs can be performed in two ways: magnetic resonant coupling and electromagnetic radiation~\cite{ref22}.
+\item{Wireless Charging:} In wireless charging, the power can be transmitted between the devices without requiring a connection between the transmitter and the receiver. These techniques participate in increasing the availability of WSNs and prolonging the network lifetime. Wireless charging in WSNs can be performed in two ways: magnetic resonant coupling and electromagnetic radiation~\cite{ref22}.
+
+\end{enumerate}
\subsection{Radio Optimization}
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 by using an 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 avoid overloaded wireless sensor nodes in a particular region of a WSN.
\subsubsection{Sink Mobility}
-In WSNs including 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 to the sink maximizes the overload on the sensor nodes near to the sink. In order to overcome this problem and prolong the network lifetime, a solution is to use a mobile sink moving within the area of interest so as to collect the sensory data from the static sensor nodes over a single hop communication. A 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}.
+In WSNs including 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 to the sink maximizes the overload on the sensor nodes near to the sink. In order to overcome this problem and prolong the network lifetime, we can use a mobile sink which moves within the area of interest to collect the sensory data from the static sensor nodes over a single hop communication. A 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}.
\item \textbf{Barrier coverage}~\cite{ref99,ref100} where the main goal is to detect targets as they cross a barrier, which is usually a long belt region such as one can be found in intrusion detection and border surveillance applications.
\end{enumerate}
-\indent The sensing quality and capability can be assessed by a sensing coverage model of trained through the identification of a 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}:
+\indent The sensing quality and capability can be assessed by a sensing coverage model obtained through the identification of a 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{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, the sensor node is not able to detect any point that is outside its sensing range. 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 at the position $(x_i,y_i)$. For any point P at the position $(x,y)$, 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
+\item \textbf{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, the sensor node is not able to detect any point that is outside its sensing range. 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 at the position $(x_i,y_i)$. For any point P at the position $(x,y)$, 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}
where $d(s_i,P) = \sqrt{(x_i - x)^2 + (y_i - y)^2}$ denotes the Euclidean distance between sensor node $s_i$ and P.
-\item \textbf{Probabilistic Sensing Model}
-In reality, an event detection by a sensor node is imprecise. Hence, the coverage $C_{xy}$ requires to be represented in a probabilistic way. The probabilistic sensing model is more practical and can be used as an extension of the binary disc sensing model. 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
+\item \textbf{Probabilistic Sensing Model:} In reality, an event detection by a sensor node is imprecise. Hence, the coverage $C_{xy}$ requires to be represented in a probabilistic way. The probabilistic sensing model is more practical and can be used as an extension of the binary disc sensing model. 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{enumerate}[(i)]
\item $\textbf{Coverage Type}$ Is the WSN dedicated to monitor a whole area, to observe a set of targets, or to look for a breach of 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~\cite{ref107}. The method of placement can be selected based on the type of sensors, application, and the environment. In the deterministic placement, the deployment can be achieved for a small number of sensor nodes and in friendly environment, whilst for a large number of sensor nodes or when the area of interest is inaccessible or hostile, a random placement 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 $\textbf{Deployment Method}$ 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~\cite{ref107}. The method of placement can be selected based on the type of sensors, application, and the environment. In the deterministic placement, the deployment can be achieved for a small number of sensor nodes and in friendly environment, whilst for a large number of sensor nodes or when the area of interest is inaccessible or hostile, a random placement is the choice. The sensor network can be either dense or sparse. On the one hand, 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 low number of sensor nodes.
\item $\textbf{Coverage Degree}$ refers to how many sensor nodes are required to cover a target or an area. A point in the sensing field is said to be K-coverage by at least K sensor nodes. Some applications need a high reliability to achieve their tasks. Therefore, the sensing field is deployed densely so as to perform a K-coverage for this field. The simple coverage problem consists of a coverage degree equal to one (i.e., K=1), where every point in the sensing field is covered by at least one sensor.
-\item $\textbf{Coverage Ratio}$ is the percentage of the area of sensing field that 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 $\textbf{Coverage Ratio}$ is the percentage of the 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 is said 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.
+\item $\textbf{Network Connectivity}$ ensures the existence of a path from any sensor node in WSN to the sink. A connected WSN ensures the sending of the 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}.
+Activity based Scheduling schedules the activation and deactivation of sensor nodes during the network lifetime.
-\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.
-This dissertation deals with activity based scheduling to ensure the best coverage in WSNs.
+\item $\textbf{Activity based Scheduling}$ schedules 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, using only local neighbor information. In centralized algorithms, a central controller (a node or base station) informs every sensor of the time intervals to be activated.
+This dissertation deals with activity based scheduling to ensure the best coverage.
\end{enumerate}
-\section{Energy Consumption Modeling}
+\section{Energy Consumption Model}
\label{ch1:sec:9}
%\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:
+%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 data acquisition. The main tasks of each of these states include:
\begin{enumerate}[(i)]
\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: a low power level allowing the sensor node to stay alive.
\label{RDM}
\end{figure}
-\indent In this model, the radio consumes energy to execute the transmitter and the power amplifier. The receiver circuitry consumes 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 ($d_0$), 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
+\indent In this model, the radio consumes energy to execute the transmitter and the power amplifier. The receiver circuitry consumes 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 ($d_0$), 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 across a distance d, the radio expends
\begin{equation}
\label{eq3-ch1}
\end{equation}
-As well as to receive a K-bit packet, the radio expends
+while to receive a K-bit packet, the radio expends
\begin{equation}
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.
+\noindent 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 considers only the energy 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 consumed by other parts inside the sensor node such as computation unit and sensing unit.
-In this dissertation, we developed another energy consumption model that proposed by~\cite{DESK} and based on \cite{ref112}.
+\indent The radio energy dissipation model considers only the energy 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 consumed by other parts inside the sensor node such as computation and sensing units.
+In this dissertation, we developed another energy consumption model that based on \cite{ref112}.
%\subsection{Our Energy Consumption Model:}
%\label{ch1:sec9:subsec2}
\section{Conclusion}
\label{ch1:sec:10}
-\indent In this chapter, an overview of the wireless sensor networks has been presented. Unlike traditional ad-hoc networks, WSNs are collaborative and very oriented toward a specific application domain. The structure of the typical wireless sensor network and the main components of the sensor nodes have been detailed. 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 shown, 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. Energy efficiency is the primary challenge to increase the network lifetime. Therefore, the energy efficient solutions have proposed in order to handle that challenge. Many energy efficient mechanisms have been illustrated, which are aimed to reduce the energy consumption of the different parts 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 in WSNs is explained. One of the main scientific research challenges in WSNs is how to build energy efficient coverage protocols. 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 illustrated.
\ No newline at end of file
+\indent In this chapter, an overview of the wireless sensor networks has been presented. Unlike traditional ad-hoc networks, WSNs are collaborative and very oriented toward a specific application domain. The structure of the typical wireless sensor network and the main components of the sensor nodes have been detailed. Several types of wireless sensor networks are described. Various fields of applications covering a wide spectrum including health, home, environmental, military, and industrial applications have been presented. As shown, 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. Energy efficiency is the primary challenge to increase the network lifetime. Therefore, energy efficient solutions have been proposed in order to handle that challenge. Many energy efficient mechanisms have been illustrated, which are aimed to reduce the energy consumption of the different parts of the wireless sensor nodes. The definition of the network lifetime has been presented in different contexts. The problem of the coverage in WSNs is also explained.
+%One of the main scientific research challenges in WSNs is how to build energy efficient coverage protocols.
+This chapter highlights the main design issues that need to be considered when designing an energy efficient coverage protocol for WSNs. In addition, energy consumption models have been discussed.
\ No newline at end of file
